A Survey of Challenges and Opportunities in Sensing and Analytics for Cardiovascular Disorders

08/12/2019 ∙ by Nathan C. Hurley, et al. ∙ Yale University Texas A&M University 0

Cardiovascular disorders account for nearly 1 in 3 deaths in the United States. Care for these disorders are often determined during visits to acute care facilities, such as hospitals. While the length of stay in these settings represents just a small proportion of patients' lives, they account for a disproportionately large amount of decision making. To overcome this bias towards data from acute care settings, there is a need for longitudinal monitoring in patients with cardiovascular disorders. Longitudinal monitoring can provide a more comprehensive picture of patient health, allowing for more informed decision making. This work surveys the current field of sensing technologies and machine learning analytics that exist in the field of remote monitoring for cardiovascular disorders. We highlight three primary needs in the design of new smart health technologies: 1) the need for sensing technology that can track longitudinal trends in signs and symptoms of the cardiovascular disorder despite potentially infrequent, noisy, or missing data measurements; 2) the need for new analytic techniques that model data captured in a longitudinal, continual fashion to aid in the development of new risk prediction techniques and in tracking disease progression; and 3) the need for machine learning techniques that are personalized and interpretable, allowing for advancements in shared clinical decision making. We highlight these needs based upon the current state-of-the-art in smart health technologies and analytics and discuss the ample opportunities that exist in addressing all three needs in the development of smart health technologies and analytics applied to the field of cardiovascular disorders and care.

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1. Introduction

Cardiovascular diseases are the worldwide leading cause of death (Benjamin et al., 2019). In 2016, cardiovascular diseases accounted for nearly 1 in 3 deaths in the United States. Real-time vital monitoring allows for care providers to track patient progress and to rapidly respond to any changes in patient condition. In the hospital, monitoring patients is part of routine clinical practice. Providers are able to monitor cardiac status and basic vitals from anywhere in the hospital at any time. Slight deteriorations in health can be observed and interventions put into place before patients suffer worsening harm. However, length of stay in these acute care settings is often quite short (Wright et al., 2003; Baker et al., 2004), representing only a small portion of a patient’s life. Monitoring physiologic parameters and symptoms outside of the hospital setting can enable better detection and response systems before a person becomes acutely ill and requires hospitalization or after hospitalization to prevent early readmission to the hospital; however, many of the devices today are targeted to healthy people. With the prevalence and ubiquitous nature of remote and wearable sensors, opportunities exist to broaden the applications of sensing and for adapting analytic techniques to enhance diagnosis, monitoring, and treatment among people with cardiovascular disease.

A challenge in monitoring patients with cardiovascular disorders is designing the technology and algorithms to support a variety of conditions and signs/symptoms, including heart failure (HF), coronary artery disease (CAD), and stroke, along with risk factors for these conditions such as hypertension (HTN). HF is typically a chronic condition where the heart is unable to drive blood forward through the body sufficiently or can only do so under damagingly high pressures. HF is a debilitating disease that causes significant global disease burden. In 2016, HF was the most rapidly growing cardiovascular condition in the world (Ziaeian and Fonarow, 2016). CAD occurs when blood flow through the coronary arteries, the small arteries that provide blood to the heart, becomes impeded. This occurs both gradually as plaque builds up within the coronary arteries and suddenly when a plaque ruptures and clots. The former causes chest pain and exercise intolerance, while the latter, commonly known as a heart attack, can cause severe pain, loss of consciousness, and death. Each year around 800,000 Americans suffer a heart attack, and rapid care following a heart attack is a chief predictor for minimizing long term morbidity and mortality (McNamara et al., 2006; Krumholz et al., 2011; Nallamothu et al., 2015). Stroke is any disease impacting the blood vessels to the brain. In particular, acute stroke is a condition that occurs when either a blood vessel in the brain ruptures, or when one of those blood vessels becomes blocked. Stroke manifests with the sudden onset of neurological deficits, some of which may be irreversible. Stroke is the fifth leading cause of death in the United States and is a leading cause of long-term disability (Benjamin et al., 2019). HTN is a disease in which the arterial blood pressure (BP) is chronically elevated. This elevation may remain asymptomatic for years but can ultimately lead to damage of multiple organ systems. HTN is a very common condition, with middle-aged Americans having approximately 90% lifetime risk of developing HTN (Vasan et al., 2002).

Patients with cardiovascular disorders present a number of challenges for remote monitoring and diagnosis because of complexities within the diseases. Many of these diseases involve seemingly trivial symptoms that may suddenly change from a minor inconvenience to a debilitating lack of function. A patient with a given disease may feel well for multiple years, and then suddenly decompensate and require emergent care. Ideally, remote monitoring should be able to track the slow, daily progression of a disease and alert the patient and healthcare providers to worsening disease before decompensation and patient suffering. However, preliminary studies in remote monitoring have failed at preventing adverse events, such as in preventing repeated hospital admissions in patients diagnosed with HF. For example, the Telemonitoring in Patients with Heart Failure trial (Tele-HF) used patient self-reports of daily changes in symptoms, weight, and a variety of other factors (e.g., medication changes, depression scores, etc.) to identify worsening symptoms in an effort to intervene prior to another acute event, but did not find a statistically significant difference between control and intervention arms (Chaudhry et al., 2010)

. However, an analysis of participant subgroups did find that patient self-reported data could improve prediction of readmission likelihood, showing potential for more advanced analytic techniques to better identify participant risk and to improve estimates in this space

(Krumholz et al., 2016). A further exploration in automating the capture of relevant biometric signals, including heart rate, blood pressure, and weight, was similarly unable to find a statistically significant difference in control and intervention arms (Ong et al., 2016), suggesting that further exploration of additional biomedical signals are needed.

Remote sensing technologies have increased in prevalence and have made personalized health data collection feasible. In human activity recognition (HAR), wearable sensors and inertial measurement units embedded within smartphones and smartwatches have enabled the tracking of detailed motions (Lara and Labrador, 2012; Avci et al., 2010). Coupled with nearable sensors that capture motion via video, these sensing systems allow for the tracking of motions of healthy participants (Chen et al., 2017a) to tracking of disease state with custom-built sensors, such as smartshoes (Eskofier et al., 2017). The data provided by these wearable and remote sensors has more recently enabled advanced machine learning techniques to identify more complex patterns of motions, better understanding personalized behavior (Hoque and Stankovic, 2012; Wang et al., 2019). Eventually, these techniques have emerged to personalize models of activity recognition to individual users, providing the most robust interpretation of activities of daily living per user (Rokni et al., 2018), enabling feedback and the measurement of clinical outcomes (Dobkin and Martinez, 2018). This progression from the development of new sensing modalities to the analytic techniques that detect patterns within the data and finally to personalization in tracking and disease progression modeling is a pathway that should be leveraged for more advanced clinical disorders.

The development of new sensors to measure signs and symptoms of cardiovascular disorders would ideally enable a similar progression for tracking of cardiovascular outcomes. These new sensors would be able to identify conditions that may not be apparent to patients or providers, such as different sounds from the heart, slowly decreasing patterns of activity, or combination of vitals that may appear normal in isolation but may be indicative of risk given a combination of values and certain patient contexts. By identifying dangerous signs before symptoms manifest, earlier interventions can lead to improved health outcomes. A variety of technologies and machine learning techniques to this purpose exist in condition-specific settings (Mortazavi et al., 2014b; Fox et al., 2017) to varied success (Mortazavi et al., 2016; Fox et al., 2018; Hijazi et al., 2016). Understanding the pathologies of the disorders is important in understanding the clinical needs and opportunities that exist in developing new wearable and remote sensors for diagnosis and treatment of a variety of cardiovascular conditions and using advanced analytic techniques that are enabled from the collection of new, comprehensive patient data.

In this survey, we discuss different sensing modalities that have been or that could be applied to tracking cardiac health in remote settings; in addition, we consider the opportunities that advanced analytic strategies present with the acquisition of remote sensing data for continuous risk modeling. As cardiac pathologies manifest, they can also be indirectly observed through physical changes in the body, potentially measured by sensors on or around the body. These changes can be utilized to track patient health, to plan interventions to maximize patient wellness, and to decrease the overall impacts of the disease. One of the oldest technologies used for assessing cardiac health is the stethoscope. In the digital era, the electronic stethoscope is a varied group of technologies that incorporate a microphone in order to automate acoustic diagnose and facilitate remote monitoring (Leng et al., 2015). Other technologies, such as photoplethysmography and sphygmomanometry, allow for remote measurement of the characteristics of the blood flow including heart rate and blood pressure (Zhang, 2015). Doppler radar can detect vital signs such as respiratory rate and heart rate (Li et al., 2009). Electrical techniques such as electrocardiography (ECG) or other conduction studies such as Bioimpedance can give insights into the internal physiology of the heart (Wasserlauf et al., 2019).

Sensing systems provide for opportunities to proactively detect and alert patients and physicians to worsening health states. However, to allow for timely and effective interventions as well as to rapidly evaluate the impact of those interventions, development of advanced signal processing and machine learning techniques need to keep pace with the development of raw sensor modalities. This paper presents a survey of state-of-the-art sensing technologies and analytics with respect to monitoring cardiovascular disorders, in order to highlight successes and provide areas for additional growth. Two key ways in which analytics associated with sensing systems can provide support are to monitor for the start of new disease and to track the progression of preexisting disease. Tracking the progression of existing disease is the easier task: once an underlying disease state is known, appropriate monitoring can be put into place and utilized to follow the progression of the disease. Monitoring for the start of new disease is more difficult, as the focus is more general. In either case, sensing and clinical characteristics can be combined for decision support with the aid of machine learning approaches. In this paper, we survey the current state of the art in patient monitoring and analytics for patient risk and care, highlighting needs and opportunities for advancements in the field of smart health with respect to monitoring signs, symptoms, and treatments in patients diagnosed with cardiovascular disorders. The workflow described in this paper towards developing new tools for remote clinical decision support is shown in Figure 1.

Figure 1. Overview of a workflow to developing personalized, remote clinical decision support tools for patients with cardiac disorders. Needs are shown in three categories: needs in sensor development and data handling, needs in continuous data collection and analysis, and needs in developing comprehensive and personalized analytical models. Addressing these three categories will allow for improved personalized remote clinical decision support for patients with cardiac disorders.

Overview of paper structure.

2. Case Studies and Needs

This work considers three primary cardiovascular disorders for the review of gaps and opportunities, though by no means encompasses the entirety of technologies available for monitoring and treating these conditions nor the entirety of conditions to which these technologies could be applied. Instead, these conditions serve as meaningful examples in which technical solutions would be clinically impactful, following the same trajectory of development discussed in the prior section with regards to HAR. Those conditions are heart failure (HF), coronary artery disease (CAD), and stroke, with an additional emphasis on hypertension (HTN) as an important and common risk factor for these conditions. We briefly review these conditions and use them as case studies to discuss the current needs in smart health technologies.

2.1. Clinical Conditions

Heart failure (HF) occurs when one or both halves of the heart are unable to drive blood flow forward at the rate required by the body or can only do so under high pressures. This discussion of pathology will focus primarily on left-sided HF rather than right-sided HF, but the two are often closely associated and technologies for monitoring the two will have a large amount of overlap. The two will also often coexist. HF can result from ineffective heart contractions, from high pressure limiting the effect of heart contractions, or from difficulty in filling the heart. The first two causes lead to HF with reduced ejection fraction (HFrEF), and the last leads to HF with preserved ejection fraction (HFpEF). Ineffective heart contractions can result from muscle damage caused by CAD, by chronic volume overload as seen in mitral regurgitation (MR) or aortic regurgitation (AR), or by a family of cardiac muscle disorders known as cardiomyopathies. High pressure can lead to HF either from aortic stenosis (AS) or from uncontrolled hypertension. In either case, the pressure that the heart has to work against is so high that the pumping becomes ineffective. Difficulty in filling the heart can be caused by ventricular hypertrophy, cardiomyopathy, fibrosis, disease around the outside of the heart (the pericardium), or by CAD.

Coronary artery disease (CAD) is a family of diseases where blood flow through the small arteries of the heart, the coronary arteries, is restricted. This restriction can be caused by deposits of fatty plaques within the arteries, or by clotting caused by the rupture of one of these plaques. Depending on the extent of the blood flow restriction and the current oxygen demands of the heart, CAD may cause different symptoms. CAD is represented by a spectrum of conditions that are defined by specific clinical and physiological signs.

Stroke occurs when blood supply in and around the brain is acutely disrupted, and results in acute neurologic defects. Ischemic stroke is a type of stroke where a blockage in cerebral arteries rapidly blocks off blood flow, leading to cell death. Hemorrhagic stroke is a type of stroke where a blood vessel in the brain ruptures, rapidly raising pressure inside the skull and causing cell death. Transient ischemic attacks (TIAs) are similar in cause and presentation to strokes but resolve spontaneously. They are often an indicator of underlying disease and put the patient at increased risk for future TIA or stroke. The neurological pathology goes beyond the scope of this work, but there are several notable cardiovascular impairments that may cause a stroke.

Hypertension is a condition where a patient’s blood pressure is persistently elevated and is often a condition that serves as a modifiable precursor to each of the three cardiovascular disorders discussed (Olsen et al., 2016). Hypertension is divided by cause into two categories: primary (or essential) hypertension is hypertension without a particular medical cause, while secondary hypertension is hypertension caused by some other medical condition. Primary hypertension accounts for roughly 90% of all hypertension, while secondary hypertension accounts for the remaining 10%. Causes of secondary hypertension include renal disease and endocrine diseases that disrupt the body’s natural control of blood pressure (Onusko, 2003). Essential hypertension is a diagnosis of exclusion, and requires ruling out the possibility of any secondary causes. Risk factors for essential hypertension include both hereditary and environmental factors (Reboussin et al., 2018). There is a strong association between hypertension, obesity, and insulin resistance. Hypertension is associated with poor diet, excessive alcohol intake, and age. Additional discussion of normal cardiac physiology can be found in the Supplementary Appendix (Section A).

2.2. Needs for Monitoring Cardiovascular Disorders

Figure 2 illustrates the three primary needs this survey will discuss: 1) the need for sensing technology that can track longitudinal trends in signs and symptoms of the cardiovascular disorder despite potentially infrequent, noisy, or missing data measurements; 2) the need for new analytic techniques that model data captured in a longitudinal, continual fashion to aid in the development of new risk prediction techniques and in tracking disease progression; and 3) the need for machine learning techniques that are personalized and interpretable, allowing for advancements in shared clinical decision making. A number of varied signs and symptoms exist for HF, CAD, and stroke. The remainder of this section briefly introduces some common signs and symptoms. Here, a symptom is a change caused by disease that is noticed by and likely an irritant to the patient, while a sign is a change that the patient may not notice or that may not be concerning to the patient.

Figure 2. Progress from sensors to analytics (y axis) and how they relate to each of the three conditions (x axis).

Progress from sensors to analytics (y axis) and how they relate to each of the three conditions (x axis).

In HF, the symptoms result both from insufficient blood flow and from excess fluid buildup. The three main symptoms that are associated with diagnosis of HF and quantification of its severity are dyspnea (shortness of breath) on exertion; sudden, choking dyspnea at night; and difficulty breathing while lying down. In left-sided HF pulmonary vein pressure increases, causing buildup of fluid in the lungs (pulmonary edema) that worsens while lying down. In right-sided HF systemic venous congestion results in fluid buildup in the periphery (peripheral edema) that worsens while upright, resulting in noticeable swelling in the wrists and ankles. HF is difficult to precisely define as it is a clinical syndrome resulting from many different heart conditions, and many variants exist. Therefore, attempts to understand HF and to monitor its progression must focus on identifying the symptoms and identifying cardiac dysfunctions. Symptoms that can be measured include peripheral edema (swelling of ankles, rapid weight gain), decreased activity, and changes in respiratory patterns when lying down versus remaining upright. Changes in blood flow to the kidneys result in decreased urine production during the day, and increased urine production at night. Patients with HF will therefore often get up frequently in the night. These patients will also likely change posture in the night, with patients with advanced HF needing to sleep upright. One of the most used classification schemes for HF is the New York Heart Association (NYHA) Functional Classification (Levin et al., 1994)

. In this classification scheme, classes are separated based on the physical activity that the patient is able to achieve and the discomfort that results from physical activity. Class I is when no symptoms are present, and in Class IV the patient is unable to perform any physical activity without discomfort and symptoms of heart failure are never alleviated. As can be seen, a variety of sensing modalities could be employed to track signs and symptoms of HF, from measurements of peripheral blood flow, respiration rate, exercise capacity, and posture while sleeping. This illustrates the need for new sensors that can measure each of these various symptoms. However, not every sensor may be worn at all times, due to excessive burden on the user. Therefore, there is a need for new sensing modalities that can track different patterns and trends in captured data, as well as transfer learning techniques that can be adapted to estimate values of sensors that may be malfunctioning or not worn.

If the right set of sensors are selected and are designed to be worn longitudinally, new patterns and trends in signs and symptoms might be detected. In CAD, for example, restrictions in blood flow of the coronary artery may result in a condition called stable angina (SA). The rate at which the restrictions in blood flow occur, however, might change as the disease progresses. At some point, the restriction responsible for SA may rapidly increase, producing a situation where the patient is in emergent need of medical care. The most common way for this progression to occur is for a fatty plaque to rupture, leading to the formation of a clot that blocks blood flow. The first disease after this point is unstable angina (UA). As the restriction increases to a partial occlusion, the patient will experience chest pain that worsens without activity or that is not relieved with rest. Both stable and unstable angina present similarly in a patient. Typically, the patient will have episodes of chest pain that last from 3-10 minutes, but potentially lasting up to 30 minutes. This pain may radiate to the jaw, neck, shoulder, or arm. The patient will likely feel short of breath and may also experience nausea. If the patient takes a medication called nitroglycerine, the pain should resolve within 1-3 minutes. In UA, damage is still reversible, but intervention is emergently necessary to ensure that the disease does not progress. If UA progresses, it will progress to a condition commonly known as a heart attack, or in medical terminology as a myocardial infarction (MI). There are two types of MI: non-ST-elevation MI (NSTEMI), and ST-elevation MI (STEMI). In NSTEMI, some muscle in the heart has begun to die, and therefore at least some of the damage caused is irreversible. In a STEMI, there is a complete blockage of blood flow at some point and a large amount of muscle in the heart has begun to die. NSTEMI and STEMI are distinguished by characteristic findings on ECG; in a STEMI, the ST segment will be elevated above the baseline in some leads, while this elevation is absent in NSTEMI. The leads showing this change reflect the area of the heart impacted by the MI. This demonstrates the second need, longitudinal monitoring of continuous signals that can identify disease progression, and machine learning techniques that can account for the personal progression and varied rates of this progression.

In order to prevent conditions such as stroke, interventions are necessary in known risk factor conditions, such as hypertension. Hypertension can lead to stroke in multiple ways. Very high blood pressure raises the risk of hemorrhagic stroke, as blood vessels in the brain may not be able to support higher pressures. Additionally, chronic hypertension is the main risk factor associated with ischemic stroke. The diagnosis of hypertension requires repeated blood pressure measurements (sustained hypertension), as measured by ambulatory blood pressure measurements. Various reasons for blood pressure elevation must be identified, including white coat hypertension (when the blood pressure is elevated during a visit to a doctor but normal when measured in home settings), masked hypertension (when blood pressure is regularly elevated but detected as normal during a visit to a doctor), and evaluation in changes of blood pressure when sleeping versus when awake (nocturnal nondipping hypertension). Hypertension typically does not manifest with any symptoms, as the body is very good at masking the feeling of this pressure. Although high blood pressure has been colloquially associated with stress, headaches, or dizziness, these symptoms are typically not caused by chronic hypertension. The primary sign (and part of the diagnostic criteria) of hypertension is an elevated blood pressure. For diagnosis, at least two measurements on two different occasions of blood pressure above 120/80 mmHg are required. More recently, guidelines have suggested measuring blood pressure with an ambulatory blood pressure monitor over a 24-hour period, measuring blood pressure every 15 minutes during the day and every 30 minutes during sleep at night, and using the average values to have a better understanding of a patient’s blood pressure (Goff et al., 2014). This sustained elevation may result in stiffer arteries, reducing arterial compliance. Additionally, over time, this chronic elevation may result in left ventricular hypertrophy seen on ECG or in changes in the retina. Most patients with hypertension are largely asymptomatic, with the chief clinical sign being that of elevated blood pressure. When symptoms of hypertension do manifest, they are largely caused by organ damage that results from chronically elevated blood pressures. Chronically elevated blood pressure can lead to heart damage, as the heart must work harder than normal to produce these elevated pressures. This can lead to HF as the heart gains mass and loses efficiency, or to CAD as the increased mass of the heart requires increased myocardial oxygen supply. Chronically elevated blood pressure can also lead to damage of the arteries. This can lead to atherosclerosis, where plaque buildups can compromise coronary arteries, leading to CAD or cerebral arteries, leading to stroke. Weakening of arterial walls can lead to kidney disease or to retinal disease. Advanced hypertension can cause changes to the eye that can be observed visually by a physician. The definition of high blood pressure has undergone changes in recent years, with the SPRINT trial indicating that aggressive treatment of blood pressure to ¡120/¡80 mmHg is associated with decreased mortality (Group, 2015). The potential measurement of blood pressure from new sensing modalities can enable analytic techniques to identify cases of hypertension and evaluate the effectiveness of medication on reducing blood pressure, such as in the SPRINT trial. This illustrates the third need, where machine learning techniques, trained on continual data captured from new sensing modalities (the prior two needs), must provide actionable, interpretable estimations of signs, symptoms, and disease progress, in order to help guide treatment decision making and evaluate treatment effectiveness both prior to a diagnosis of a cardiovascular disorder and in the treatment and evaluation of recovery from an adverse cardiovascular event.

In the following sections, we explore the state of the art in technology associated with each of the needs. This survey reviews the technology available, the gaps that remain in addressing the needs, and highlights opportunities for researchers within the smart health field to design solutions with impact to clinical decision-making problems.

3. New Sensors, Trends in Longitudinal Capture, Missing Data, and Sensor Selection

The management of HF, CAD, and stroke can benefit from new sensing techniques by capturing acute data as well as detecting changes in sensed data over time. Each has unique signs and symptoms that manifest through a variety of changes in the body. For HF, improper blood flow can result in fluid retention (edema) in the lungs or the periphery, as well as develop signs of heart remodeling. Heart remodeling can be evidenced by third and fourth heart sounds (S3 and S4), as well as by a laterally or inferiorly displaced point of maximal impulse (PMI) of the heart on physical exam; the place where the heartbeat can be felt most strongly will migrate down and to the left of the thorax. One way in which improper blood flow can be detected is that the extremities will be cooler than normal.

In CAD, stable and unstable angina will often result in physical pain felt by the patient in an episode that may last up to 30 minutes in the chest that may also radiate to the jaw neck and arm. The patient’s heart rate and blood pressure will initially be elevated, although these can potentially decrease in NSTEMI and STEMI as the heart fails to operate optimally. The patient will breathe more quickly and will put more effort into breathing. Additionally, abnormal sounds may be heard with a stethoscope. It is possible for rales, an abnormal lung sound, to be heard at the posterior base of each lung. During chest pain, an ECG will show ST-segment depression, but this will change and progress to ST-segment elevation in STEMIs.

For stroke, this work focuses on the signs and symptoms that might lead to a stroke. Atrial fibrillation (AFib) is a relatively common arrhythmia that increases risk of stroke. AFib results when the atria of the heart beat ineffectively and randomly, causing turbulence within the atria. This turbulent flow allows for clots to form within the atria. If these clots are dislodged, they may travel through the arteries and become lodged in the brain, causing an ischemic stroke. AFib is classically defined as an “irregularly irregular” beat- the beat is not a typical rhythm (irregular) and additionally has no pattern determining when beats occur (irregularly). This is most often seen as absent P waves on ECG with variably occurring QRS complexes over a noisy baseline. However, this pattern could be detected by many techniques that measure pulse. Chief risk factors that predispose patients to AFib are age, other heart disease, diabetes, and chronic lung disease. HTN can also lead to stroke in multiple ways. Very high blood pressure raises the risk of hemorrhagic stroke, as blood vessels in the brain may not be able to support higher pressures. Chronic hypertension is the main risk factor associated with ischemic stroke.

These cardiac conditions present a range of sensing opportunities:

  • Acoustic measurement: capture of heart sounds to identify specific classes as well as respiratory effort are important in understanding acute conditions and changes in heart function over time. This also includes respiratory distress when lying down, causing patients diagnosed with HF to need to sleep in a more upright position.

  • Electrical Measurement: Remote ECG measurements can identify periods of atrial fibrillation and other arrhythmias or help identify progression of CAD during an acute event.

  • Blood flow: Understanding cardiac output, as well as measurement of blood pressure, is an important risk factor that needs periodic measurement.

  • Fluid Retention/Weight Change: HF often results in lung and peripheral edema that results in swelling and can be measured by cooler temperatures in the periphery and changes in weight.

  • Physical activity and pain: In all cases, patient self-reported pain, fatigue, and physical activity may be surrogates for worsening conditions. Activity recognition can include posture detection to link with respiratory measurements.

3.1. Existing Technologies and Applications

3.1.1. Acoustic Sensing/Vitals

Vital sign monitoring has been explored through a variety of technologies. Each sensor type has been designed to address some of the sensing needs described in the previous section in an effort to replace or replicate tools available in acute care settings for remote environments. The stethoscope is one of the oldest such tools in medicine and is an implementation of acoustic sensing. By hearing and interpreting sounds from the patient, the physician can develop insights into the health of the patient and the functionality of the organs. Recently, digital stethoscopes have been utilized to better capture sounds. Digital stethoscopes provide benefit in allowing soft sounds to be more easily heard, but also allow for recording of sounds for later manual or computational analysis. As physicians have grown more reliant on advanced imaging techniques such as ultrasound, physical exam skill, including skill at auscultation, has decreased (Conn and O’Keefe, 2009).

Developing a digital stethoscope involves multiple components requiring heart sound capture, segmentation of the audio signal, and understanding of the cardiac cycle, best paired with an external signal such as ECG or pulse to determine the reference interval (Leng et al., 2015)

. A limitation here is that the time from electrical activity to sound production is not constant in all samples. Direct segmentation techniques involve utilizing Shannon energy to calculate an envelope and to find its peaks, and then use those peaks to reconstruct the cardiac cycle. Techniques applied to analyzing this data include machine learning techniques to classify the sounds, including support vector machines (SVM), artificial neural networks (ANN), hidden markov models (HMM), and gaussian mixture models (GMM), for identifying sounds and identifying next likely sound given the state in the heart beat cycle currently detected. These techniques have accuracies near 90% for classifying signals as either normal or as having aortic or mitral valvular lesions.

Work has also been done to develop low-cost devices that can act as a bridge between a traditional stethoscope and a cell phone (Sinharay et al., 2016). Constructing a cavity with good resonance is necessary in collecting good quality sound transmissions from the stethoscope. In particular, Sinharay et al. have evaluated using different kind of sensors to capture sounds to be transmitted from and to smartphones for analysis.

In addition to detecting abnormal sounds in the cardiac cycle, there has been successful work in eliciting heart pathology from abnormalities within normal heart sounds. In particular, the normal cycle is composed of two sounds- one from the aortic valve closing and the other from the pulmonic valve closing. Both happen at nearly the same time, typically creating a single sound. However, some heart pathologies can impact the time between these. In a study of pediatric patients, high pressure in the pulmonary vasculature was found to be predicted by certain aortic and pulmonic valve relative intensities (Elgendi et al., 2014). Although this work has not been applied to adult patients, it could theoretically help to elicit information about the pressures at different points within the heart.

In several cases, radar has been utilized instead of direct, on-body measurement for detecting vital signs. Radar is able to detect periodic changes caused by both breathing and the heart, allowing heart rate and respiratory rate to be detected. Vinci et al. described a remote sensor that uses a six-point radar to monitor respiration and heartbeat (Vinci et al., 2013). It uses a continuous 24 GHz wave and a radiated power of less than 3 microwatts. It captures these values noninvasively in patients at rest. This is notable as it is a sensing modality that does not require attaching sensors to the human body. This is particularly valuable in infants, in adults in severe conditions that cannot have additional attachments placed on the body, and as a modality that improves patient quality of life by limiting on-body sensors. The sensor designed in this paper does not have the limitations of other radar systems that require a wide frequency band to achieve more accurate results. Because of the six-point receiver architecture, this sensor can accurately measure angle and displacement by only measuring phase difference in backscatter patterns. Models regarding the permittivity of the skin allow them to estimate that their signal has 1.52 mm penetration as well as estimates of blanket and clothing impact. As a result, they can estimate where the edge of the torso is to aid in monitoring breathing. This provides an opportunity to noninvasively measure respiration and heart rate. However, it requires known, fixed postures of the individuals. Additionally, it will only work for one patient at a time. While this modality provides activity, displacement, and vitals monitoring in controlled, clinical environments or within specific remote environments (such as in the bedroom while asleep), it does not provide flexibility while moving. There are needs to extend such sensing systems to a variety of environments.

Work by Li et al. explore the use of radar technology for vital sign monitoring (Li et al., 2009). Their system uses a hardware-controlled clutter cancellation system. This allows their radar technology to identify the difference between the person being monitored and background clutter that are likely present in rooms the person would be in. Authors propose taking ka-band radar systems that are meant for motion sensing and modify them for vitals sensing. Authors discuss existing work, design considerations for advancements, then opportunity to extend this to infant monitoring. The advancements in radar usage have come through the detection of the right frequency band to use. Different frequencies were shown to be able to go through different rubble with and without metal mesh. Authors then discuss the chip-level decisions that need to be made to create CMOS Doppler-based motion detectors. This allows vital sign detection through obstacles which can be important for noninvasive monitoring and for detection of vitals in emergency disaster scenarios. The application, however, is not clear for advanced signal processing of multiple vitals.

3.1.2. Electrical Measurements

Remote ECG monitoring has been explored by a number of researchers, primarily to solve the challenges that arise in noisy measurement. One issue that arises in continuous ECG monitoring, as with wearable ECG implementations, is that signals are often hidden by the noise of activity. Li et al. presented an approach for quantifying this noise (Li et al., 2014). While earlier approaches focused on labeling ECGs as either clean or noisy, the approach presented by Li introduced five classifications, each with different amounts of information available to be extracted from the ECG. They defined the noisiest strips as those where artifact obscures signals to the point that there can be no confidence in any interpretation of the ECG. Strips with severe noise were those where some interpretation could be made, but interpretations could be confused as to where the QRS complexes fell or to whether ventricular flutter rhythms were present. In strips with moderate noise the QRS complex and presence or absence of ventricular flutter rhythms could be assessed, but finer signals such as P or T waves could not be extracted. Minor noise was the label given to strips with some amount of noise, but where P waves and T waves could be extracted. This level of noise allows for the analysis of atrial arrhythmias such as atrial flutter. Finally, clean ECGs were those where no noise was present. The authors produced training data by adding three types of noise to the original clean dataset: baseline wandering, electrode motion, and muscle artifact. They trained an SVM to classify strips based on the amount of noise present and validated this classification scheme on real noisy data. This validation showed good agreement between manually annotated labels and model output labels, with the greatest confusion present where samples had been manually annotated as having minor noise, but the model labeled the samples as having moderate noise. The authors note that a chief limitation of this work was that the model was not trained for or with an arrhythmia database, which substantially lowers its effectiveness on samples with arrhythmias. Additionally, they note that methods based on continuous features rather than discretely extracted features would be likely to show greater performance.

Once identified, several approaches have been implemented in order to account for and to correct motion artifacts. Sriram et al. addressed this problem by utilizing a triaxial accelerometer (Sriram et al., 2009)

. ECG signals are usable as a means of continuous biometric security. However, this continuous security is lost when the ECG signal is distorted with motion artifact. This approach shows that supplementing the raw ECG signal with features extracted from acceleration allows for accurate classification of ECG subject identity. They segmented signals to windows containing roughly four heartbeats, averaged those four beats together, and then corrected for baseline abnormalities with linear interpolation of q-minima and a high pass filter in association with the accelerometer features. These features then served to correctly identify users using either a k-nearest neighbors or a Bayesian network classifier.

Another issue that arises with automatic ECG monitoring is that many abnormalities might be troubling in one patient while normal in another. Chen et al. (Chen et al., 2017b) described an approach to train ECG monitoring systems to discover patient-specific abnormalities. This work utilized an accelerometer to reduce the number of false alarms in monitoring systems. Over time, this system learns the normal for a given patient and uses a knowledge of this normal in order to reduce false alarms.

3.1.3. Blood Flow

The American College of Cardiology and the American Heart Association (ACC/AHA) recently released guidelines that suggest ambulatory blood pressure measurements, those taken at home in 15 minute intervals including during sleep, should be captured to better understand a patient’s blood pressure and potential cardiovascular risks (Reboussin et al., 2018). The sphygmomanometric and oscillometric techniques are well-established as the predominant means by which blood pressure is typically measured (O’brien et al., 2001). Both methods involve the inflation of a pressurized cuff, typically around the patient’s upper arm and maintained at the level of the heart. The pressure in the cuff is increased to above realistic values of the systolic blood pressure, and then slowly decreased. In the auditory sphygomomanometric method, sounds called Korotkoff sounds can be heard just distal to the cuff as it deflates. The pressure at which these sounds are first heard is the systolic pressure, and the pressure at which these sounds are no longer heard is the diastolic pressure. In the oscillometric technique, minute variations in pressure as the heart beats against the pressurized cuff are measured and the systolic and diastolic blood pressures are extracted from these variations (Babbs, 2012). Most at-home blood pressure monitoring devices utilize the oscillometric technique, which is well-validated to have performance similar in quality to the sphygmomanometric technique (Hodgkinson et al., 2011). Recently, cuff-less blood pressure monitoring techniques have been explored in order to record blood pressure.

The most common cuff-less approach thus far is to use photoplethysmography (PPG) and ECG to capture pulse arrival time, pulse transit time (and pulse wave velocity), as surrogates for blood pressure, then use analytic techniques to estimate the systolic and diastolic blood pressure values (Ma and Zhang, 2006; Thomas et al., 2014). If the posture of an individual is known, these techniques are able to measure an estimate of the blood pressure, without disturbing the individual with frequent cuff inflations. However, the ECG and PPG combination can result in error in blood pressure estimation because it does not appropriately account for artifacts that exist between the ECG measurement of a pulse and the PPG capture of the pulse arrival time (Bennis et al., 2019). To account for this, researchers have turned towards dual PPG capture (Dai et al., 2016; Trujillo et al., 2017) over a small portion of the artery to account for pulse transit time, or bio-impedence measurements, which are better able to locate the artery and avoid capturing blood profusion time into capillaries (Huynh et al., 2018; Ibrahim et al., 2017). Ballistocardiogram approaches look to capture pulse arrival time through the small changes in pressure sensed by the waves in each pulse, providing a method for capturing cuff-less blood pressure whenever participants are still (Inan et al., 2016; Kim et al., 2016, 2018). These approaches all look to address cuff-less blood pressure when the participant is in a fixed, known position, and provide the opportunity for more frequent ambulatory blood pressure measurement.

3.1.4. Fluid Retention

While prior studies, such as Tele-HF and Beat-HF, attempted to use weight scales as a surrogate for fluid retention in HF, the measurement of 3 pounds of weight change was not an alert that was able to reduce HF readmissions (Chaudhry et al., 2010; Krumholz et al., 2016; Ong et al., 2016). A number of attempts to measure peripheral edema and fluid retention have focused on the development of smart socks that look to measure fluid buildup in the ankles (Fallahzadeh et al., 2016, 2015). A stretch sensor measures the expanding duration of the patient’s ankle both as edema increases throughout the day and as edema increases over time. The context-awareness allows the device to discard ankle measurements when motion, muscle contractions, or an incorrect posture would interfere with the measurement. This sock was able to reliably determine the participant’s posture, and measurements of fluid retention were well correlated, but additional study is needed to determine if this measurement is accurate enough, and whether it can generate alerts early enough to intervene in HF patients. Yao et al. came to similar conclusions of needing further study of their sensor to classify edema (Yao et al., 2017), as this remains an open area of research.

3.1.5. Physical Activity and Posture

Activity, posture, and pain are important measurements in understanding symptom and treatment effectiveness in patients diagnosed with cardiovascular disorders. Measurement of respiratory distress in HF patients requires a measurement of posture, measurement of blood pressure through proxy measures such as pulse transit time require a measurement of posture, as did the smart sock for fluid retention (Section 3.1.4). While each sensor can capture posture, smartphones excel at this (Yürür et al., 2015), often coupled with other applications tracking activities of daily living (Ouchi and Doi, 2013; Pires et al., 2016). Recently, smartwatches have shown to accurately detect postures and exercises (Mortazavi et al., 2014a; Sen et al., 2015), which is important for patient monitoring, since smartphones are often in the proximity of the user, but often not physically on the user, unlike smartwatches (Van Laerhoven et al., 2015). These can also provide important context to the measurements captured by the other modalities discussed in this section (Mortazavi et al., 2014b).

3.2. Gaps

As richness of data increases across the variety of sensors, the potential for noise and missingness increases as well. It is difficult to understand the context in which measurements are captured. Accuracy of posture detection and presence of other noisy attributes impact the potential success of different sensing modalities. It is also unlikely a patient will wear all sensors all the time, as this will provide excessive burden. While a measurement performed on occasion is likely to be a high-quality measurement, continuous and automated measurements introduce a greater deal of variability in the quality of measurements. For instance, a once-a-day measurement is likely to be a measurement where the patient will intentionally position themselves appropriately and remain motionless during the measurement. A patient monitoring their blood pressure will likely sit upright with their legs uncrossed, or a technician performing an ECG will ensure that the printed ECG is taken at a point where the patient is motionless, and no artifacts are present. Conversely, more frequent or continuous monitoring must account for noise introduced by motion artifacts as well as from noise introduced from other suboptimal measuring conditions. As such, a number of challenges remain in capturing the necessary signals:

  • Acoustic measurement: challenges in identifying the specific patient limit the potential for non-wearable sensors. Wearable sensors must account for noise across a variety of motions, environments, and potential sensor misplacement.

  • Electrical Measurement: Continuous ECG requires multiple leads to be worn at the same time. Devices such as the Apple watch provide potential for requesting ECG periodically when other sensing modalities dictate when it is necessary (Wasserlauf et al., 2019), but the correlation between these modalities and necessary ECG readings has not been well studied outside of AFib.

  • Blood flow: Cuff-less blood pressure monitoring must extend to continuous, beat-to-beat measurements without constantly restraining users to fixed, known postures.

  • Fluid Retention/Weight Change: Edema measurements have not been clinically validated to show the degree of fluid retention which must generate alerts that can clinically improve outcomes.

  • Physical activity and pain: Remote measurement of acute and chronic pain remains an open challenge.

3.3. Opportunities

An additional source of noise can be introduced by the redundancy of signals that can exist. Different physical phenomena can be measured by different modalities, many of which will produce slightly different readings. Heart rate can be derived from multiple sources: auditorily by stethoscope, electrically by ECG, visually by PPG, and visually by radar. It stands to reason that these redundant values could be exchanged for each other, but that exchange may not completely be a one-to-one relationship. Transfer learning is an ongoing field of study that seeks to apply existing models to data that was not used in training or was only used minimally in training (Xie et al., 2016; Rokni and Ghasemzadeh, 2016, 2018). Transfer learning could be applied to this problem as a way to apply a single model to patients with disparate data collection modalities.

Missingness in data also increases as richness increases. While binary parameters used in many risk models (e.g. history of HF, current diabetic status, etc.) are easy to collect and even possible to impute, continuous monitoring opens the possibility of more complicated missingness. A battery may fail on a sensor leading to a variable period of missingness. Wearable sensors may introduce missingness secondary to poor compliance or poor utilization. The missingness introduced by gaps in continuous monitoring is more difficult to impute and presents a challenge in building comprehensive models

(Lipton et al., 2016; Liu et al., 2019). A number of opportunities emerge for immediate and impactful research on sensing signs and symptoms of cardiovascular disorders, illustrated in Figure 3, and listed below:

Figure 3. Overview of basic sensor categories proceeding to physiologic value measured and to overarching disease state.

Overview of basic sensor categories proceeding to physiologic value measured and to overarching disease state.

  • Integration of multiple sensing modalities into a single platform, reducing the number sensors needed to be worn. High impact areas appear to be the wrist (smartwatch) and chest (heart and lung sounds).

  • Integration of machine learning techniques to help identify when longitudinal data capture is necessary, similarly to ECG requests to verify periods of arrhythmias associated with AFib detection with the Apple Watch (Wasserlauf et al., 2019).

4. Continuous Data Collection and Analytic Models

Beyond the acute sensing and detection of symptoms related to HF, CAD, and stroke, analytic opportunities arise in the processing of this data longitudinally and continuously. As discussed, the progression of CAD from stable and unstable angina to NSTEMI and STEMI represent longitudinal changes that may have periods of rapid change interspersed. Similarly, untreated hypertension can lead to stroke if untreated. Changes in heart remolding in HF may be represented by changes in heart sounds as captured by acoustic sensing. Patients living with HF may experience long term changes in the amount of physical exertion required to perform activities of daily living. These changes may be gradual and unnoticeable to the patient, but may represent worsening condition or recovery.

These cardiac conditions present a range of analytic techniques necessary to capture longitudinal changes in continuously-sensed data:

  • Continuous capture of acoustic sensing: Understanding how sounds change over time may allow for the identification of new signals that represent earlier identifiers of worsening conditions or treatment effectiveness.

  • Continuous capture of electrical signals: While the detection of arrhythmias may be present in surrogate measures such as heart rate, detection of changes in ST segments of an ECG may allow for early alerts and acute care.

  • Continuous capture of vitals signals: Understanding the changes in the variety of vitals signals captured and how they may relate to each other can provide an understanding of improving or worsening risk factors relevant to HF, CAD, and stroke.

  • Continuous capture of physical activity: Physical activity and sleep are important functional measures of recovery, and accurate, longitudinal understanding of functional change can be correlated with improved mortality and prevention of adverse events.

4.1. Existing Technologies and Applications

4.1.1. Continuous Capture of Acoustic Sensing

Electronic auscultation is useful for deriving characteristics of other parts of the cardiovascular system than sounds generated specifically by the heart valves. A carotid bruit is a sound created by turbulent blood in a carotid artery, often caused by narrowing that in turn is produced by atherosclerotic plaques. Knapp et al. looked at the effectiveness of carotid bruit detection by electronic auscultation (Knapp et al., 2014). Out of 1,371 patients in this study, 84 were found to have carotid bruits by electronic auscultation. These patients were matched with controls who did not have bruits, and both patients from each pair were assessed with duplex ultrasound to determine extent of carotid stenosis. Bruit detection with electronic auscultation and manual annotation was found to have a sensitivity of 88% for stenosis 50%, and a specificity of 58% with duplex ultrasound providing the ground truth.

Work by Palaniappan et al. surveyed machine learning techniques to further analyze lung sounds (Palaniappan et al., 2013)

. They evaluated 59 papers that used signal processing and machine learning techniques on a variety of lung sound problems including normal breath sounds, abnormal breath sounds, and a series of sounds called adventitious lung sounds. This survey highlights an important need by evaluating short term sounds, long term sounds, and identifying normal and abnormal sounds across the different time periods. Most works in this survey focused on specific frequencies (between 150 and 2000 Hz, though they found that most work typically worked at 150 Hz), and evaluated machine learning techniques such as k-nearest neighbor, ANNs, HMMs, GMMs, genetic algorithms, SVMs, and fuzzy logic to classifying a variety of lung sounds. They found that by using piezoelectric microphones, contact microphones, and electric microphones, and one commercially available lung sound instrument, they could design electronic stethoscopes that filtered out heart sounds to capture necessary lung sounds. Similarly, one could use the same techniques to filter out the lung sound to capture the heart sounds. Using standard time-domain and frequency-domain signal processing features, algorithms were able to classify lung sounds with between 83-93% accuracy. Deep neural network techniques, such as convolutional neural networks (CNN), recurrent neural networks (RNN), and long short-term memory networks (LSTM) should be able to extract more time and frequency domain features rather than hand crafted features explored by authors to achieve higher accuracy.

4.1.2. Continuous Capture of Electrical Signals

In clinical settings, most ECGs are performed as 12-lead ECGs. In these ECGs, there are 10 electrodes attached to the patient and 12 different measurements taken from these electrodes. Each provide a one-dimensional view of the magnitude of the vectors of all electrical impulses in the heart relative to a given axis. Different axes allow for information to be obtained about the functionality about different parts of the heart. Depending on the goals of remote monitoring, remote ECGs will typically only include a subset of these typical views. As a result, methods that can accurately detect essential signals from minimal lead ECGs are necessary.

Work by Jambukia et al. surveyed machine learning techniques to analyze and classify ECG signals (Jambukia et al., 2015). They evaluated 31 papers that used signal processing and machine learning techniques in order to extract clinically significant features from raw ECG signals. Most of the papers evaluated used the MIT-BIH arrhythmia dataset (Moody and Mark, 2001)

for both training and testing purposes. Two aspects of ECG classification considered were ECG beat classification for individual, isolated beats, and ECG signal classification for interpretation of a longer signal. Some approaches evaluated involve signal feature extraction followed by threshold-based algorithms such as the Pan-Tompkins algorithm. Other approaches utilized various neural network architectures, with the authors finding that of the architectures studied, multilayer perceptron neural networks provided the best performance. Recurrent neural networks, such as the long short-term memory (LSTM) architecture, were not evaluated in this survey. Deep learning techniques have also been utilized for ECG evaluation. Yildirim showed that a bidirectional LSTM architecture can reliably classify five different rhythms from the MIT-BHI arrhythmia database

(Yildirim, 2018). This bidirectional LSTM model achieved accuracies greater than other techniques. Additional deep learning techniques that combine CNN and LSTM have been used to detect AFib without explicit feature extraction (such as R peak extraction) (Andersen et al., 2019). Further deep learning techniques have looked at a variety of processing individual beat anomalies and sequence anomalies (Baloglu et al., 2019), though time series presented to CNN models often needs fixed windows of time to be pre-determined for evaluation. Additionally, some work uses a single lead (Mathews et al., 2018) for detecting arrhythmia, though it is likely at least two leads are currently necessary for other ECG feature extraction.

There is evidence to suggest that patients at risk of cardiac pathology benefit from more continuous remote ECG monitoring. The mHealth Screening to Prevent Strokes (mSToPS) randomized clinical trial is an ongoing trial of 2659 patients investigating the benefit of continuous monitoring for AFib (Steinhubl et al., 2018). As reported by Steinhubl et al., the initial phase of the trial discovered that for individuals at risk of AFib, home ECG monitoring was superior to routine care for discovering new incidence of AFib. In the actively monitored group, there was a 3.9% diagnosis of new-onset AFib, vs 0.9% in the control. This resulted in earlier initiation of anticoagulative therapy (a preventative measure for stroke) in these patients. However, this has also resulted in a higher healthcare utilization among these actively monitored patients. This trial is still ongoing- the ultimate clinical impact is still unknown. Clinical outcomes are due to be published in a 3-year follow-up.

4.1.3. Continuous Capture of Vitals Sensing

Ultasonography is a technique that uses ultrasonic sound waves to produce images of tissues beneath the skin. Ultrasonography is valuable for visualizing structures that are unreachable noninvasively. In hospital settings, point-of-care ultrasound has increasingly grown in utilization as mobile ultrasound systems become cheaper and comparable in quality to larger ultrasound systems (Moore and Copel, 2011). Point-of-care ultrasonography is useful as a tool that physicians can bring to the bedside for aid in diagnosis, much like a stethoscope, but deep learning techniques are necessary to evaluate the ultrasound images and classify changes in conditions.

Ultrasonography can also be used to evaluate the fluid status of the lungs. As described in Assaad et al., lung ultrasound is a valuable tool for quickly assessing the health of a patient’s lungs (Assaad et al., 2018). Certain visual findings, such as “B-lines” are highly associated with edema and various pulmonary pathologies. These visual findings also change very rapidly, reflecting the present disease state more accurately in some cases than measures such as blood oxygen saturation. Lung ultrasonography is also useful in differentiating between cardiogenic and noncardiogenic pulmonary edema; cardiogenic pulmonary edema typically shows more uniform findings and plural effusion (fluid buildup in the tissue surrounding the lungs). Lung ultrasonography is an underutilized technique in medicine and lacks standardization in training and implementation.

Work by Bhuyan et al. explores an exciting possibility of wearable ultrasound for the monitoring of internal function noninvasively (Bhuyan et al., 2011)

. In order to create a small form factor that could be used to measure organ function with wearable, remote ultrasound, they created a small, flexible probe through a flexible PCB integrated circuit. They also used a system that has only one transmit and one receive channel to avoid excess signal degredation. This system has a bandwidth of 10 MHz, power consumption of 6.72 mW per channel, and uses 16 such channels to measure a 5.6 mm x 1.6 mm area. They used classical image processing with ultrasound for their validation. Their system, however, used an attached cable to measure. There is an opportunity to create a remote, continuous version of such a system if a flexible PCB-based wearable ultrasound with necessary battery and wireless transmission capabilities were added, but, computer vision techniques are needed to enhance the analytic component of the wearable ultrasound.

Echocardiography is the practice of using ultrasound in order to visualize the structures of the heart. Echocardiography can take place either as an invasive transesophageal echocardiography (TEE) or as the noninvasive transthoracic echocardiography (TTE). Many different aspects of the heart can be described and quantified via echocardiography (Lang et al., 2015), including size, function, and mass of various structures of the heart. Measurement of these parameters aids in the diagnosis of HF. For instance, left ventricular mass or poor emptying are markers of HF. Valvular disfunction, such as stenoses or regurgitations can be directly observed. These measurements also aid in assessing cardiac function in CAD, particularly following MI; injured portions of the cardiac wall will often move less than they normally would.

Various groups have found preliminary success in applying deep learning computer vision techniques to the analysis of ultrasonographic images. The first step in automatic analysis of ultrasonographic images is to recognize the view in question. Østvik et al. describe the use of a convolutional neural network (CNN) to classify TTE images according to the view being presented (Østvik et al., 2019). This method showed classification high accuracy in distinguishing among seven different TTE views. Additionally, the authors described a technique for extracting 2D slices from 3D images and achieved a mean error of 4°.

Techniques for measuring edema include cuffs that track ankle circumference and measurement of electrical impedance. Weight monitoring is sometimes used as a proxy for tracking edema, as edema co-presents with fluid retention. There has been success in implantable impedance monitors to measure pulmonary edema. Yu et al. found that intrathoracic impedance serves as a predictor for imminent hospitalization due to fluid overload (Yu et al., 2005). In a population of 33 patients with HF, a device consisting of a pacemaker and defibrillator was implanted. The device measured the impedance between those two leads. This study found that there was a significant decrease in impedance prior to hospitalization with fluid overload. This decrease began on average two weeks prior to hospitalization and continued through the date of hospitalization.

Impedance monitoring has also been implemented in noninvasive and ambulatory monitoring systems. Weyer et al. describe a system that incorporates both ECG and noninvasive impedance cardiography (Weyer et al., 2015). This device includes Bluetooth connectivity and a battery that lasts for up to 21 hours. This system could be implemented for long-term monitoring in patients with HF to monitor pulmonary congestion and to potentially allow remote interventions before hospitalization is necessary.

The internal and external jugular veins provide drainage from the head into the heart. The right jugular veins are positioned almost directly above the right atrium, and therefore the pressure within them is very closely tied to the pressure of the right atrium. The external jugular vein’s filling level indicates the pressure within the right atrium and will be distended in cases of right heart failure. Pulsations can be observed with great difficulty in the internal jugular vein. These pulsations provide evidence as to the relative timing and forces involved in right atrial contraction, atrial relaxation, right ventricular contraction, venous filling after the closing of the tricuspid valve, and emptying of the atrium after opening of the tricuspid valve.

As venous pressures are so much lower than arterial pressures, measurement of the jugular venous pulse is much more difficult than the measurement of arterial pulses. However, Amelard et al. were recently able to utilize a technique called PPG Imaging (PPGI) as a viable technique to correctly measure the jugular venous pressure (Amelard et al., 2017). This technique uses a system located approximately 1.5 meters away from a supine patient. A light shines on the patient and the reflected light is analyzed to identify pulsations. The arterial pulsation from the carotid artery is easier to detect, and the jugular pulsation can be identified as a corresponding inverted pulsation at a location near but lateral to the arterial pulsation. In this study, the ground truth arterial waveform was verified with a PPG measuring device. Pertinent clinical features were consistently able to be extracted from the venous waveform, including the c, v, x, and y waves (corresponding to the contraction of the right ventricle, systolic filling of the right atrium, relaxation of the right atrium, and beginning of the filling of the right ventricle). In about half of subjects, the a wave was also observed (corresponding to the contraction of the right atrium). The ability to regularly monitor and quantify these waveforms could allow for new techniques in monitoring right heart function. Ballistocardigrams may be another way to measure this venous pressure.

Signals that capture continuous blood pressure, described in Section 3, may also be extended to capture a variety of heart rate, heart rate variability, blood pressure, respiratory rate, and changes in these values. Obstructive sleep apnea, a condition in which airwaves are restricted causing the body to wake up from sleep to begin breathing again, increases heart rate, respiratory rate, and blood pressure, keeping patients from falling asleep. This has a direct relationship with blood pressure and nocturnal nondipping hypertension, and treatments for apnea have shown to be correlated to improvements in blood pressure (Sánchez-de-la-Torre et al., 2015). This means approaches for measuring cuff-less blood pressure cannot be restricted to periodic, ambulatory measurements, but must transition to continuous beat-to-beat measurement and interpretation.

Finally, telemonitoring trials for HF readmission have tracked longitudinal measurements of symptoms, vitals, and patient qualitative reports (Chaudhry et al., 2010; Krumholz et al., 2016; Ong et al., 2016). In the Tele-HF study, a number of vitals signals and patient reported outcomes generated alerts for interventions if the values were below a specified threshold, or represented a significance drop from the prior day’s values. However, the study was unable to find a statistically significant reduction in readmissions in the intervention arm. Ong et al. in the Beat-HF study tried to use some machine learning techniques to further identify risks of adverse events, and while the study was unable to reduce readmissions, the techniques did show some promise in stratifying patients (Pourhomayoun et al., 2014), as did further statistical techniques applied to the Tele-HF data (Krumholz et al., 2016; Mortazavi et al., 2016). With the addition of more signals captured, and techniques that can better account for varied time-domain aspects of analytics, it is possible that better just-in-time alerts can be generated for preventing future recurrent HF events.

4.1.4. Continuous Capture of Physical Activity

For cardiovascular disorders, the detection of activities and postures is important in understanding the other biomarkers captured, providing context for their readings. For example, at nighttime, knowing the posture of the user provides context for dyspnea measurements and heart sound recordings for HF patients. In addition to providing context for the other vitals measurements related to cardiovascular disorders, the change in physical activity performance can show increasing effects of HF symptoms, pain as a result of CAD, and acts as a surrogate for the general well-being of these patients.

Many research-oriented activity recognition platforms focus on the detection of activities of daily living (Mortazavi et al., 2014b, 2013; Bennett et al., 2014; Nathan et al., 2018) and understanding daily exercise intensities. These sensors are capable of tracking sports movements in the healthy and measure sedentary time in the elderly, and come in many forms of smartwatches, smartphones, smartwatch-sized sensors (Mortazavi et al., 2014a, 2013), embedded within shoes, and most recently within eTextiles (Zhang et al., 2006; Abtahi et al., 2018).

HF participants have had improved outcomes in mortality and readmission when involved in cardiac rehabilitation programs that encourage continuous physical exercise (Heran et al., 2011). This physical exercise routine has shown that measurements in improved peak exercise capacity correlate with improved cardiovascular outcomes. Home-based cardiac rehabilitation systems centered on physical activity detection in order to quantify a home-based exercise routine (Maddison et al., 2014). However, such systems do not yet quantify improvement directly from physical activity measurements. This is necessary since adherence in cardiac rehabilitation programs is often quite low (Karmali et al., 2014).

4.2. Gaps

Deep learning techniques have made the exploration of time series data more fruitful with the development of automatic features that represent longitudinal risk or outcomes. Techniques such as attention-based LSTMs have shown promise in exploring continuous time-series data to predict clinical mortality, decompensation, and length of stay, which outperform hand-crafted feature extraction, and other deep-learning techniques that do not find focus on specific periods of time, in intensive care unit data (Song et al., 2018). However, these techniques have not been applied to this remote data yet because the integration of these sensing techniques have not yet occurred.

A number of the analytic techniques tied to the use of these new sensing paradigms have focused on the diagnosis of specific anomalies or classification of specific types of sounds or signals captured. What is still needed is the following:

  • Integrated sensors that can capture signals frequently, or continuously, over entire study periods.

  • Machine learning techniques that can explore multiple windows of time over multiple combinations of available signals in order to quantify trajectories in signals, identifying longitudinal patterns and changes in signal that may be indicative or worsening conditions or treatment effectiveness and recovery, extending beyond anomaly detection and signal classification.

4.3. Opportunities

One way in which advanced work in analytics could be incorporated is in better personalized monitoring for risk of CAD. As previously discussed, CAD is a condition often characterized by gradual worsening of chest pain that culminates in a heart attack. Ideally, monitoring systems would be able to follow gradual changes prior to the rupture of a plaque that causes a heart attack. In the early stages of CAD, activity monitoring could be used to assess wellness. By tracking a certain threshold of activity that the patient does not (or cannot) exceed, a monitoring system can estimate the severity of angina. As that threshold begins to decrease, the patient’s angina is likely increasing and greater intervention may be warranted. The monitoring system for a patient at risk of CAD should also include an ECG system to watch for changes associated with a heart attack. If any electrical changes concerning for a heart attack begin to appear in the patient’s ECG, then emergency services would be required. Earlier interventions are associated with better outcomes, and a monitoring system like this coupled with improved analytics could potentially allow for earlier treatment, leading to less overall damage and better patient outcomes.

Improved analytics could also be implemented to better treat valvular diseases. Unlike the other pathologies discussed here, there are few risk models for predicting future valvular heart disease. However, advanced analytics could be implemented to allow for earlier detection of valvular disease. As discussed above, these abnormalities change the way in which blood flows through the chambers of the heart, producing turbulence that can be detected as sound. The most straightforward evaluation for valvular disease in remote wearable settings would involve electronic stethoscopes continuously monitoring the patient’s heart sounds. By learning the normal sound profile of a patient, new changes and murmurs could be quickly identified. After identifying a particular valvular disease and its associated murmur, long-term monitoring with electronic stethoscopes could be used to characterize the severity of the valvular insult; most murmurs initially increase in intensity, but in later disease stages decrease in intensity. Rather than risking false negative screening in physical examinations, a longitudinal monitoring system could detect the changes along this trajectory to allow for more informed decision making. As a more advanced option in monitoring valvular disease, miniaturized ultrasound probes could be incorporated into a wearable system. These could be used for imaging and analyzing the valvular parameters such as cross section and flow. Additional work into computer vision interpretation of ultrasound images would be necessary in order to automatically process these signals. Vital monitoring can also directly feed into an understanding of valvular disease. In particular, blood pressure can reflect aortic valve lesions. Finally, systems to monitor valvular disease could monitor symptomatic disease progression. As many types of valvular disease may ultimately lead to HF, the opportunities presented above for HF apply here as well. Foremost among them would be activity recognition, where late stage valvular disease can manifest with a loss of stamina in day-to-day activities.

Opportunities:

  • Improved Machine Learning Processing of Existing Sensor Modalities: Development of machine learning techniques that can extract meaningful data from non numerical sources, expanding on the computer vision work done in automatically processing and interpreting ultrasound images.

  • Time-Series Machine Learning Models: Development of machine learning techniques that can process longitudinal data and account for multiple channels of data, sampled at different frequencies, and with different segment lengths of importance, are required to develop new risk prediction techniques and alerts based upon continuously captured data.

5. Clinical Interpretability, Analytic Models, and Treatment Paradigms

Clinical risk prediction models and those that predict adverse events have helped guide medical treatments and improve patient care. These techniques, with machine learning modeling, have the potential to improve clinical care in both the acute care settings (Rymer and Rao, 2019) and remote care settings. This includes understanding the diagnosis and progression of diseases and the personalized patterns and signals that can be captured by advances made in categories listed in Sections 3 and 4. Some preliminary work has been conducted in clinical trials on HF patients, understanding distinct patient phenotypes within the disease. In one such HF trial, clinically-distinct clusters of patients were found to have different time-to-event predictions and outcome rates (Ahmad et al., 2014). Another relevant clinical trial in HF patients is the Treatment of Preserved Cardiac Function Heart Failure with an Aldosterone Antagonist (TOPCAT) trial (de Denus et al., 2017; Pitt et al., 2014). The purpose of this trial was to determine if a treatment designed specifically for HF patients with preserved ejection fraction could improve outcomes, a patient population where such treatments have not been found to universally treat these patients. This trial was also unsuccessful in showing that HF patients treated with Spironolactone had better outcomes (Pitt et al., 2014). However, due to some issues with data gathered in certain regions, investigators began taking a closer look at subsets of patients, to determine if specific patients were actually helped by the treatment. The investigators found regional variations lead to different treatment effectiveness in cohorts of participants (Pfeffer et al., 2015; de Denus et al., 2017)

. This indicates that HF patients diagnosed with preserved ejection fraction may benefit from cluster analysis, looking at personalized differences in outcome rates where different treatments may be helpful for different subsets of patients. These provide for the basis of the following needs in HF, CAD, and stroke:

  • Personalized models: as illustrated by the TOPCAT findings, these diseases are quite complex and understanding the person-to-person variation allows for specific risk prediction based upon data collected, along with matching techniques that allow for comparison to patients most similar to the individual modeled.

  • Dynamic adaptation: models must account for the varied data types potentially collected, the varied rate at which they are collected, how well to link them to data gathered in acute care settings, and be able to update as a disease progression worsens or treatment regimen proves effective, including providing confidence metrics that suggest the collection of additional data, if necessary.

  • Interpretable machine learning: as the data size progresses, medical models must be able to explain the driving risk factors in a manner interpretable to clinicians in order to guide treatment decision making.

5.1. Existing Technologies and Applications

5.1.1. Risk Prediction Models

Much of stroke risk prediction is tied to the risk associated with AFib. In particular, it may be appropriate for patients with AFib to undergo anticoagulation therapy in order to reduce their risk of stroke. Anticoagulation therapy is any therapy that works to reduce the rate at which blood clots form. This type of therapy can be beneficial by preventing thromboembolic stroke. Conversely, this type of therapy can be detrimental by promoting life-threatening bleeds, such as in hemorrhagic stroke. Therefore, implementation of any anticoagulation therapy must be implemented with great care. In addition to models that predict only stroke risk, the ACC/AHA Pooled Cohort Equations treat stroke and CAD together.

CHA2DS2-VASc is a model that predicts 12-month thromboembolic event rate (including stroke, pulmonary embolism, and peripheral thromboembolism) in patients with AFib who are not undergoing anticoagulation therapy (Lip et al., 2010). Creation of this model drew upon the efforts of and improved upon multiple older models in order to apply more broadly and accurately to diverse patient populations. One chief exclusion in this model is that only patients with non-valvular AFib are considered. The parameters considered in this model are presence of HF, hypertension, age, diabetic status, history of stroke or other thromboembolic event, history of any vascular disease, and gender. This model aids clinicians in prescribing anticoagulants, which increase the risk of bleeds but decrease the risk of thromboembolic events (including stroke).

The HAS-BLED model was created to predict the risk of bleeding in anticoagulated patients with AFib (Pisters et al., 2010). The parameters included in this model are hypertension, history of liver or kidney dysfunction, history of stroke, history of bleeding, difficulty calibrating oral anticoagulation therapy, use of alcohol, and use of certain drugs that may increase bleeding risk. Recommendations by groups such as the European Society of Cardiology (Members et al., 2012) are that CHA2DS2-VASc and HAS-BLED be used in conjunction for informed decision making, and that HAS-BLED alone should not be a reason to withhold anticoagulant therapy.

Other models have been produced to predict general risk of stroke. The MyRisk_Stroke Calculator is a model to predict 10-year risk of stroke (Nobel et al., 2014)

. This estimator was built on a prospective dataset where collection began in 1992 and validated with a second dataset with collection beginning in 1998. Follow-up was through the year 2007. In this cohort, the parameters found with an association to stroke risk were age, gender, education status, high blood pressure, smoking status, alcohol consumption, activity levels, anger, depression, and anxiety. Additionally, comorbidities such as renal disease, diabetic status, HF status, CAD, peripheral arterial disease were included as features in this model. The model was created as a Cox proportional-hazards model and predicts 10-year risk of any type of stroke.

Another stroke risk model is the QStroke score (Hippisley-Cox et al., 2013). QStroke was developed to be used for all patients without history of stroke but intended specifically to be used as a supplement or replacement for CHA2D2-VASc in predicting risk associated with AFib. The QStroke model features the following as parameters: age, gender, ethnicity, Townsend deprivation index (an index related to socioeconomic status), smoking status, body mass index, systolic blood pressure, blood lipid levels, and family history of CAD. Hypertension, diabetic status, AFib status, HF status, CAD, presence of rheumatoid arthritis, renal disease, and valvular disease were also included as pertinent comorbidities. The QStroke model was created as a Cox proportional-hazards model and predicts 10-year risk of any type of stroke.

Many attempts have been made to assess the risks of developing CAD. Among the most current of these are from the ACC/AHA Task Force on Practice Guideline (Goff et al., 2014). That work introduced a set of models termed the Pooled Cohort Equation to predict a primary CAD event within 10 years. The predicted risk in this model is based on age, gender, race, blood pressure (systolic and diastolic), diabetic status, smoking status, various cholesterol lab values, and on certain current medications (hypertension control, statins, or aspirin). This risk prediction tool was built to predict any type of “hard” atherosclerotic-based disease, and therefore in addition to predicting future CAD is also predicts future stroke. However, it does not distinguish between risks for these two different outcomes and treats them both as a positive outcome.

5.1.2. Remote and Dynamic Models

Remote and telemonitoring studies that use telephones and call-centers as the primary source of data have been used to track HF patients, in the hope of reducing heart failure admissions. These systems are intended to track patient symptoms, including impact of medication, weight gain (as a surrogate for edema), and depression, to identify early signs of decompensation aimed at providing interventions that prevent hospital readmissions in HF patients. In Tele-HF, Krumholz et al. found that a self-report telemonitoring system was not able to reduce readmissions in heart failure patients based upon daily reports of symptoms, medication usage, weight, and depression (Krumholz et al., 2016). Ong et al., in the Beat-HF trial, looked to automate some of the data collection surrounding blood pressure and weight, with machine learning risk models to drive interventions, but found similarly that HF readmissions were not reduced (Ong et al., 2016). Anker et al. surveyed meta-analyses and prospective clinical trials that evaluated the efficacy of telemonitoring in patients with HF (Anker et al., 2011). They found disagreement between the efficacy of telemonitoring for HF in different types of trials, but stress that the outcomes of telemedicine depend on personalization to the particular patient.

Models that predict risks within varied windows of time have, thus far, been restricted to medical settings. Henry et al. used a rolling model to predict the risk of sepsis in a hospital setting, selecting important features and identifying dynamic risks of sepsis within a single hospital admission (Henry et al., 2015). Such dynamic models could be adapted to remote and longitudinal settings, but have not done so yet.

5.1.3. Interpretable Machine Learning

A recent push in the machine learning field has been to explain predictions provided by deep learning methods that are generally considered black box techniques. Ribiero et al. developed a technique by which local logistic regression models are able to identify the reasons a particular prediction is made based upon the variables that generated the prediction of that element and similar model elements

(Ribeiro et al., 2016). This work demonstrates model interpretability, which comes naturally in CNN deep learning models that can visualize data in intermediate models but becomes much more complicated in time-series based models such as LSTMs. Additional machine learning techniques look to automatically cluster patients and explain the phenotype discovery (Henderson et al., 2018), while also learning to predict multiple outcomes at the same time across different patient types (Suresh et al., 2018), but work on explaining the findings remains in preliminary stages (Gee et al., 2019).

Interpretability also indicates the confidence in estimations, and understanding what data helps and hurts the predictive accuracy of techniques. In work aimed at improving real-time context and activity detection, Ardywibowo et al. evaluated selected sensors to improve HAR with constraints on the types of sensors and the power those sensors consume (Ardywibowo et al., 2019). Work by Solis et al. use the idea of uncertainty quantification in order to direct users to gather more data in real-time, diet logging settings (Solis et al., 2019). Uncertainty quantification is an emerging field of interpretable machine learning that has the ability to guide confidence in predictions collected as well as suggest additional data that patients and clinicians should consider collecting.

5.2. Gaps

Existing models for predicting risk in cardiovascular conditions rely on sparse data that are measured on rare occasions. Many parameters are trivial to measure (age, gender), and many parameters are Boolean values relating to history. In comparison to the data produced continuous monitoring systems, these data are sparse and likely overly-simplistic. There are two chief ways in which the limitations posed by this sparsity of data can be overcome with richer data: existing models can be updated to include richer data sources, and richer data sources can be analyzed for anomaly detection and rare event detection.

The following gaps remain in developing personalized analytic models based upon the remote sensing data gathered:

  • Integration of sensing data with acute care data and outcomes for robust risk prediction models.

  • Development of dynamic models that are flexible to the types of data collected, the windows of data collected, and the changing in patient condition throughout observation.

  • Interpretable machine learning to explain the predictions of these complex models, and help guide clinical decision making, including identifying similar patients and explaining potentially new phenotypes that might be discovered.

5.3. Opportunities

Existing models to quantify disease state and future disease risk could be improved through the implementation of richer data sources into the mode. The NYHA Functional Classification of HF relies in part on physical activity levels. The levels are subjective, with definitions in part of “no limitation of physical activity” (class I), “slight limitation of physical activity” (class II), “marked limitation of physical activity” (class III), and “unable to carry on any physical activity” (class IV). These classes are inherently subjective, and therefore susceptible to variability between patients with the same underlying disease state. Augmenting this classification with patterns detected from signals such as HAR and effort involved in activity (such as via heartrate monitoring) would allow for objective measurements from beyond the limited scope of direct patient-physician contact. The increase of objective measurements would likely lead to updates to existing models and better information to aid in making clinical decisions.

Existing models could also be improved by the detection of rare or uncommon events. For instance, when a patient presents with AFib, the duration of the AFib is typically unknown. As discussed above, the CHA2DS2-VASc score can aid physicians in predicting stroke and the appropriateness of implementing oral anticoagulation therapy. However, the parameters which contribute to the CHA2DS2-VASc score are simplistically sparse. Age and gender are (for cardiac risk purposes) nonmodifiable risk factors. Each of the other parameters are positive if the patient has ever had the given event once in their life: HF, hypertension, stroke/TIA/thromboembolism, vascular disease, or diabetes. It stands to reason that this model may be improved from richer data, such as the pattern or frequency with which the patient experiences episodes of AFib. Addition of this richer data to the model could potentially result in a model which is better able to discriminate between those at risk of stroke and those at lower risk of stroke, allowing for more appropriate and judicious use of oral anticoagulants.

The emergence of new sensing and internet of things (IoT) technologies creates a need for new models to incorporate new data for better prediction and understanding of disease states. The drastic increase in technology such as smartphones and smartwatches allows for new rich data sources, and also creates a need for the utilization of these data sources. Recently, smartwatches have been adapted to detect conditions such as AFib (Bumgarner et al., 2018). Further work should look at implementing these new modalities into longitudinal risk models. For instance, HAR recognition could be implemented as a parameter in monitoring activity tolerance in patients with HF. This could supplement the existing subjective measures of heart failure with newer objective measures.

Ultimately, data from new rich data sources is only valuable so far as it contributes to improving the quality of patient healthcare. In order for this contribution to take place, models must generate actionable feedback that can be used for informed clinical decision making. Rather than presenting modeling through a black box approach where data is supplied to the model and an answer is returned, it is desirable that the reasoning behind the risk score is understandable. If a model is interpretable, then the factors leading to a given score can be understood and interventions made to address the risk and to improve patient outcomes. Additionally, the greater the interpretability of a model, the more information that the physician and patient are able to have about the overall disease state. As this information is understood by the physician and the patient, it can be used to better inform and guide care. As a result, the following opportunities exist for immediate and impactful machine learning research:

  • Machine Learning models with cross-sectional and time-series data: Integration of sensing data with acute care data and outcomes for robust risk prediction models.

  • Development of dynamic models that are flexible to the types of data collected, the windows of data collected, and the changing in patient condition throughout observation.

  • Interpretable machine learning to explain the predictions of these complex models, and help guide clinical decision making, including identifying similar patients and explaining potentially new phenotypes that might be discovered.

  • Transfer Learning: transfer learning techniques will be able to take developed models and adapt to a variety of signals captured, a variety of patients modeled, or a combination therein, improving the flexibility of any analytic techniques developed to advance the prior three opportunities.

6. Discussion and Conclusion

We surveyed the field of sensing technologies and machine learning analytics that exist in the field of remote monitoring for cardiovascular disorders. Through the evaluation of these sensing modalities and machine learning techniques, we highlighted the potential for addressing three critical areas of need for care in patients diagnosed with heart failure, coronary artery disease, and stroke: 1) the need for sensing technology that can track longitudinal trends in signs and symptoms of the cardiovascular disorder despite potentially infrequent, noisy, or missing data measurements; 2) the need for new analytic techniques that model data captured in a longitudinal, continual fashion to aid in the development of new risk prediction techniques and in tracking disease progression; and 3) the need for machine learning techniques that are personalized and interpretable, allowing for advancements in shared clinical decision making. We highlight these needs based upon the current state-of-the-art in smart health technologies and analytics and discuss the ample opportunities that exist in addressing all three needs in the development of smart health technologies and analytics applied to the field of cardiovascular disorders and care. Whereas the progression of smart health technologies in these needs has demonstrated success in fields such as HAR and physical disorder monitoring, the opportunities for addressing cardiovascular care are many.

These cardiovascular disorders are often very complex conditions characterized by multiple changes in a patient, many of which are slow and difficult to notice. However, systems could be built to take into account and monitor many different changes in order to track disease progression and to allow clinical decisions to be made before rapid decompensations. As disease progresses, regular monitoring of heart sounds could be used in order to track heart remodeling. Instead of noticing these sounds in an acute care visit, computer-aided auscultation though wearable electronic stethoscopes could allow for earlier detection. Quantitative edema tracking would allow for monitoring functional changes within the heart. As pulmonary edema increases, clinicians are able to tell that left heart function is decreasing. Changes in ECG signals may indicate progression of CAD disorders, that may result in additional patient pain, prior to leading to heart attack. As a patient’s condition worsens, they may gradually lose the stamina to walk certain distances or to perform a certain amount of activity, demonstrating changes in physical activity capacity, respiratory rate, or sleep quality. These changes may be so gradual that patients may not notice them. Instead, new analytics for progression could instead build activity recognition into the modeling to understand slow changes in baseline function. In this, departures from a patient’s baseline level of activity would be significant and could be useful information for guiding clinical care. Similarly, alterations in blood flow may lead to changes in urination habits. As blood flow to the kidneys might be restricted during the day and increased at night due to postural changes, the kidneys will produce more urine at night. Tracking frequency of nocturnal urination could provide more clues as to overall health. Note that this will be sensitive, but not specific for heart failure. Additional measurement of hemodynamic characteristics, such as hypertension, may show treatment effectiveness and better guide the improvement of factors that would lead to conditions such as stroke. In total, a comprehensive system to track the progression of cardiovascular disorders should incorporate a body of integrated sensors, capture this data over longitudinal periods of time, and as a result, enable new advancements in machine learning techniques that can make best use of this data to help guide patient and clinician alike in improving patient care.

This survey highlighted the needs in developing smart health applications to treat HF, CAD, and stroke, and the risk factors associated with them. It reviewed the existing technologies, highlighting the current gaps in solutions presented for those needs. Finally, it presented a series of opportunities, including advanced analytic techniques to be developed once new sensing solutions are available that can guide impactful changes in the way patients with cardiovascular disorders are cared for.

Acknowledgements.
This work was supported in part by the National Institutes of Health under grant 1R01EB028106-01. Any opinions, findings, conclusions, or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the funding organizations.

References

  • M. Abtahi, J. V. Gyllinsky, B. Paesang, S. Barlow, M. Constant, N. Gomes, O. Tully, S. E. D’Andrea, and K. Mankodiya (2018) MagicSox: an e-textile iot system to quantify gait abnormalities. Smart Health 5, pp. 4–14. External Links: ISSN 2352-6483 Cited by: §4.1.4.
  • T. Ahmad, M. J. Pencina, P. J. Schulte, E. O’Brien, D. J. Whellan, I. L. Piña, D. W. Kitzman, K. L. Lee, C. M. O’Connor, and G. M. Felker (2014) Clinical implications of chronic heart failure phenotypes defined by cluster analysis. Journal of the American College of Cardiology 64 (17), pp. 1765–1774. External Links: ISSN 0735-1097, Document, Link Cited by: §5.
  • R. Amelard, R. L. Hughson, D. K. Greaves, K. J. Pfisterer, J. Leung, D. A. Clausi, and A. Wong (2017) Non-contact hemodynamic imaging reveals the jugular venous pulse waveform. Scientific reports 7, pp. 40150. External Links: ISSN 2045-2322, Document, Link Cited by: §4.1.3.
  • R. S. Andersen, A. Peimankar, and S. Puthusserypady (2019) A deep learning approach for real-time detection of atrial fibrillation. Expert Systems with Applications 115, pp. 465–473. External Links: ISSN 0957-4174 Cited by: §4.1.2.
  • S. D. Anker, F. Koehler, and W. T. Abraham (2011) Telemedicine and remote management of patients with heart failure. The Lancet 378 (9792), pp. 731–739. External Links: ISSN 0140-6736, Document, Link Cited by: §5.1.2.
  • R. Ardywibowo, G. Zhao, Z. Wang, B. Mortazavi, S. Huang, and X. Qian (2019) Adaptive activity monitoring with uncertainty quantification in switching gaussian process models. Conference Proceedings In

    The 22nd International Conference on Artificial Intelligence and Statistics

    ,
    pp. 266–275. Cited by: §5.1.3.
  • S. Assaad, W. B. Kratzert, B. Shelley, M. B. Friedman, and A. Perrino Jr (2018) Assessment of pulmonary edema: principles and practice. Journal of cardiothoracic and vascular anesthesia 32 (2), pp. 901–914. External Links: ISSN 1053-0770, Document, Link Cited by: §4.1.3.
  • A. Avci, S. Bosch, M. Marin-Perianu, R. Marin-Perianu, and P. Havinga (2010) Activity recognition using inertial sensing for healthcare, wellbeing and sports applications: a survey. Conference Proceedings In 23th International conference on architecture of computing systems 2010, pp. 1–10. External Links: ISBN 380073222X Cited by: §1.
  • C. F. Babbs (2012) Oscillometric measurement of systolic and diastolic blood pressures validated in a physiologic mathematical model. Biomedical engineering online 11 (1), pp. 56. External Links: ISSN 1475-925X, Document, Link Cited by: §3.1.3.
  • D. W. Baker, D. Einstadter, S. S. Husak, and R. D. Cebul (2004) Trends in postdischarge mortality and readmissions: has length of stay declined too far?. Archives of internal medicine 164 (5), pp. 538–544. External Links: ISSN 0003-9926 Cited by: §1.
  • U. B. Baloglu, M. Talo, O. Yildirim, R. San Tan, and U. R. Acharya (2019) Classification of myocardial infarction with multi-lead ecg signals and deep cnn. Pattern Recognition Letters 122, pp. 23–30. External Links: ISSN 0167-8655 Cited by: §4.1.2.
  • E. J. Benjamin, P. Muntner, and M. S. Bittencourt (2019) Heart disease and stroke statistics-2019 update: a report from the american heart association. Circulation 139 (10), pp. e56–e528. External Links: ISSN 1524-4539 (Electronic) 0009-7322 (Linking), Document, Link Cited by: §1, §1.
  • T. R. Bennett, C. Savaglio, D. Lu, H. Massey, X. Wang, J. Wu, and R. Jafari (2014) Motionsynthesis toolset (most): a toolset for human motion data synthesis and validation. Conference Proceedings In Proceedings of the 4th ACM MobiHoc workshop on Pervasive wireless healthcare, pp. 25–30. External Links: ISBN 1450329837 Cited by: §4.1.4.
  • F. C. Bennis, C. van Pul, J. J. van den Bogaart, P. Andriessen, B. W. Kramer, and T. Delhaas (2019) Artifacts in pulse transit time measurements using standard patient monitoring equipment. PloS one 14 (6), pp. e0218784. External Links: ISSN 1932-6203 Cited by: §3.1.3.
  • A. Bhuyan, J. W. Choe, B. C. Lee, P. Cristman, Ö. Oralkan, and B. T. Khuri-Yakub (2011) Miniaturized, wearable, ultrasound probe for on-demand ultrasound screening. Conference Proceedings In 2011 IEEE International Ultrasonics Symposium, pp. 1060–1063. External Links: ISBN 1457712520 Cited by: §4.1.3.
  • J. M. Bumgarner, C. T. Lambert, A. A. Hussein, D. J. Cantillon, B. Baranowski, K. Wolski, B. D. Lindsay, O. M. Wazni, and K. G. Tarakji (2018) Smartwatch algorithm for automated detection of atrial fibrillation. Journal of the American College of Cardiology 71 (21), pp. 2381–2388. External Links: ISSN 0735-1097, Document, Link Cited by: §5.3.
  • S. I. Chaudhry, J. A. Mattera, J. P. Curtis, J. A. Spertus, J. Herrin, Z. Lin, C. O. Phillips, B. V. Hodshon, L. S. Cooper, and H. M. Krumholz (2010) Telemonitoring in patients with heart failure. New England Journal of Medicine 363 (24), pp. 2301–2309. External Links: ISSN 0028-4793, Document, Link Cited by: §1, §3.1.4, §4.1.3.
  • C. Chen, R. Jafari, and N. Kehtarnavaz (2017a) A survey of depth and inertial sensor fusion for human action recognition. Multimedia Tools and Applications 76 (3), pp. 4405–4425. External Links: ISSN 1380-7501 Cited by: §1.
  • J. Chen, H. Peng, and A. Razi (2017b) Remote ecg monitoring kit to predict patient-specific heart abnormalities. Journal of Systemics, Cybernetics and Informatics 15 (4), pp. 82–89. Cited by: §3.1.2.
  • R. D. Conn and J. H. O’Keefe (2009) Cardiac physical diagnosis in the digital age: an important but increasingly neglected skill (from stethoscopes to microchips). The American journal of cardiology 104 (4), pp. 590–595. External Links: ISSN 0002-9149, Document, Link Cited by: §3.1.1.
  • W. Dai, Y. Zhang, J. Liu, X. Ding, and N. Zhao (2016) Dual-modality arterial pulse monitoring system for continuous blood pressure measurement. Conference Proceedings In 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 5773–5776. External Links: ISBN 1457702207 Cited by: §3.1.3.
  • S. de Denus, E. O’Meara, A. S. Desai, B. Claggett, E. F. Lewis, G. Leclair, M. Jutras, J. Lavoie, S. D. Solomon, and B. Pitt (2017) Spironolactone metabolites in topcat—new insights into regional variation. New England Journal of Medicine 376 (17), pp. 1690–1692. External Links: ISSN 0028-4793 Cited by: §5.
  • B. H. Dobkin and C. Martinez (2018) Wearable sensors to monitor, enable feedback, and measure outcomes of activity and practice. Current neurology and neuroscience reports 18 (12), pp. 87. External Links: ISSN 1528-4042, Document, Link Cited by: §1.
  • M. Elgendi, P. Bobhate, S. Jain, J. Rutledge, J. Y. Coe, R. Zemp, D. Schuurmans, and I. Adatia (2014) Time-domain analysis of heart sound intensity in children with and without pulmonary artery hypertension: a pilot study using a digital stethoscope. Pulmonary circulation 4 (4), pp. 685–695. External Links: ISSN 2045-8932 Cited by: §3.1.1.
  • B. Eskofier, S. Lee, M. Baron, A. Simon, C. Martindale, H. Gaßner, and J. Klucken (2017) An overview of smart shoes in the internet of health things: gait and mobility assessment in health promotion and disease monitoring. Applied Sciences 7 (10), pp. 986. Cited by: §1.
  • R. Fallahzadeh, M. Pedram, and H. Ghasemzadeh (2016) Smartsock: a wearable platform for context-aware assessment of ankle edema. Conference Proceedings In 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 6302–6306. External Links: ISBN 1457702207 Cited by: §3.1.4.
  • R. Fallahzadeh, M. Pedram, R. Saeedi, B. Sadeghi, M. Ong, and H. Ghasemzadeh (2015) Smart-cuff: a wearable bio-sensing platform with activity-sensitive information quality assessment for monitoring ankle edema. Conference Proceedings In 2015 IEEE International Conference on Pervasive Computing and Communication Workshops (PerCom Workshops), pp. 57–62. External Links: ISBN 1479984256 Cited by: §3.1.4.
  • I. Fox, L. Ang, M. Jaiswal, R. Pop-Busui, and J. Wiens (2017) Contextual motifs: increasing the utility of motifs using contextual data. Conference Proceedings In Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 155–164. External Links: ISBN 1450348874 Cited by: §1.
  • I. Fox, L. Ang, M. Jaiswal, R. Pop-Busui, and J. Wiens (2018) Deep multi-output forecasting: learning to accurately predict blood glucose trajectories. Conference Proceedings In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 1387–1395. External Links: ISBN 1450355528 Cited by: §1.
  • A. H. Gee, D. Garcia-Olano, J. Ghosh, and D. Paydarfar (2019) Explaining deep classification of time-series data with learned prototypes. arXiv preprint arXiv:1904.08935. Cited by: §5.1.3.
  • D. C. Goff, D. M. Lloyd-Jones, G. Bennett, S. Coady, R. B. D’Agostino, R. Gibbons, P. Greenland, D. T. Lackland, D. Levy, and C. J. O’Donnell (2014) 2013 acc/aha guideline on the assessment of cardiovascular risk: a report of the american college of cardiology/american heart association task force on practice guidelines. Journal of the American College of Cardiology 63 (25 Part B), pp. 2935–2959. External Links: ISSN 0735-1097 Cited by: §2.2, §5.1.1.
  • S. R. Group (2015) A randomized trial of intensive versus standard blood-pressure control. New England Journal of Medicine 373 (22), pp. 2103–2116. Cited by: §2.2.
  • J. Henderson, H. He, B. A. Malin, J. C. Denny, A. N. Kho, J. Ghosh, and J. C. Ho (2018)

    Phenotyping through semi-supervised tensor factorization (psst)

    .
    Conference Proceedings In AMIA Annual Symposium Proceedings, Vol. 2018, pp. 564. Cited by: §5.1.3.
  • K. E. Henry, D. N. Hager, P. J. Pronovost, and S. Saria (2015) A targeted real-time early warning score (trewscore) for septic shock. Science translational medicine 7 (299), pp. 299ra122–299ra122. External Links: ISSN 1946-6234, Document, Link Cited by: §5.1.2.
  • B. S. Heran, J. M. Chen, S. Ebrahim, T. Moxham, N. Oldridge, K. Rees, D. R. Thompson, and R. S. Taylor (2011) Exercise-based cardiac rehabilitation for coronary heart disease. Cochrane database of systematic reviews (7), pp. CD001800. External Links: ISSN 1465-1858, Document, Link Cited by: §4.1.4.
  • S. Hijazi, A. Page, B. Kantarci, and T. Soyata (2016) Machine learning in cardiac health monitoring and decision support. Computer 49 (11), pp. 38–48. External Links: ISSN 0018-9162 Cited by: §1.
  • J. Hippisley-Cox, C. Coupland, and P. Brindle (2013) Derivation and validation of qstroke score for predicting risk of ischaemic stroke in primary care and comparison with other risk scores: a prospective open cohort study. Bmj 346, pp. f2573. External Links: ISSN 1756-1833, Document, Link Cited by: §5.1.1.
  • J. Hodgkinson, J. Mant, U. Martin, B. Guo, F. Hobbs, J. Deeks, C. Heneghan, N. Roberts, and R. McManus (2011) Relative effectiveness of clinic and home blood pressure monitoring compared with ambulatory blood pressure monitoring in diagnosis of hypertension: systematic review. Bmj 342, pp. d3621. External Links: ISSN 0959-8138, Document, Link Cited by: §3.1.3.
  • E. Hoque and J. Stankovic (2012)

    AALO: activity recognition in smart homes using active learning in the presence of overlapped activities

    .
    Conference Proceedings In 2012 6th International Conference on Pervasive Computing Technologies for Healthcare (PervasiveHealth) and Workshops, pp. 139–146. External Links: ISBN 1936968436 Cited by: §1.
  • T. Huynh, R. Jafari, and W. Chung (2018) An accurate bioimpedance measurement system for blood pressure monitoring. Sensors 18 (7), pp. 2095. Cited by: §3.1.3.
  • B. Ibrahim, A. Akbari, and R. Jafari (2017) A novel method for pulse transit time estimation using wrist bio-impedance sensing based on a regression model. Conference Proceedings In 2017 IEEE Biomedical Circuits and Systems Conference (BioCAS), pp. 1–4. External Links: ISBN 1509058036 Cited by: §3.1.3.
  • O. T. Inan, A. Q. Javaid, S. Dowling, H. Ashouri, M. Etemadi, J. A. Heller, S. Roy, and L. Klein (2016) Using ballistocardiography to monitor left ventricular function in heart failure patients. Journal of Cardiac Failure 22 (8), pp. S45. External Links: ISSN 1071-9164 Cited by: §3.1.3.
  • S. H. Jambukia, V. K. Dabhi, and H. B. Prajapati (2015) Classification of ecg signals using machine learning techniques: a survey. Conference Proceedings In 2015 International Conference on Advances in Computer Engineering and Applications, pp. 714–721. External Links: ISBN 146736911X Cited by: §4.1.2.
  • K. N. Karmali, P. Davies, F. Taylor, A. Beswick, N. Martin, and S. Ebrahim (2014) Promoting patient uptake and adherence in cardiac rehabilitation. Cochrane Database of Systematic Reviews (6), pp. CD007131. External Links: ISSN 1465-1858, Document, Link Cited by: §4.1.4.
  • C. Kim, A. M. Carek, O. T. Inan, R. Mukkamala, and J. Hahn (2018) Ballistocardiogram-based approach to cuffless blood pressure monitoring: proof of concept and potential challenges. IEEE Transactions on Biomedical Engineering 65 (11), pp. 2384–2391. External Links: ISSN 0018-9294, Document, Link Cited by: §3.1.3.
  • C. Kim, S. L. Ober, M. S. McMurtry, B. A. Finegan, O. T. Inan, R. Mukkamala, and J. Hahn (2016) Ballistocardiogram: mechanism and potential for unobtrusive cardiovascular health monitoring. Scientific reports 6, pp. 31297. External Links: ISSN 2045-2322, Document, Link Cited by: §3.1.3.
  • A. Knapp, V. Cetrullo, B. A. Sillars, N. Lenzo, W. A. Davis, and T. M. Davis (2014) Carotid artery ultrasonographic assessment in patients from the fremantle diabetes study phase ii with carotid bruits detected by electronic auscultation. Diabetes technology & therapeutics 16 (9), pp. 604–610. External Links: ISSN 1520-9156, Document, Link Cited by: §4.1.1.
  • H. M. Krumholz, S. I. Chaudhry, J. A. Spertus, J. A. Mattera, B. Hodshon, and J. Herrin (2016) Do non-clinical factors improve prediction of readmission risk?: results from the tele-hf study. JACC: Heart Failure 4 (1), pp. 12–20. External Links: ISSN 2213-1779 Cited by: §1, §3.1.4, §4.1.3, §5.1.2.
  • H. M. Krumholz, J. Herrin, L. E. Miller, E. E. Drye, S. M. Ling, L. F. Han, M. T. Rapp, E. H. Bradley, B. K. Nallamothu, and W. Nsa (2011) Improvements in door-to-balloon time in the united states, 2005 to 2010. Circulation 124 (9), pp. 1038–1045. External Links: ISSN 0009-7322, Document, Link Cited by: §1.
  • R. M. Lang, L. P. Badano, V. Mor-Avi, J. Afilalo, A. Armstrong, L. Ernande, F. A. Flachskampf, E. Foster, S. A. Goldstein, and T. Kuznetsova (2015) Recommendations for cardiac chamber quantification by echocardiography in adults: an update from the american society of echocardiography and the european association of cardiovascular imaging. European Heart Journal-Cardiovascular Imaging 16 (3), pp. 233–271. External Links: ISSN 2047-2412 Cited by: §4.1.3.
  • O. D. Lara and M. A. Labrador (2012) A survey on human activity recognition using wearable sensors. IEEE communications surveys & tutorials 15 (3), pp. 1192–1209. External Links: ISSN 1553-877X Cited by: §1.
  • S. Leng, R. San Tan, K. T. C. Chai, C. Wang, D. Ghista, and L. Zhong (2015) The electronic stethoscope. Biomedical engineering online 14 (1), pp. 66. External Links: ISSN 1475-925X, Document, Link Cited by: §1, §3.1.1.
  • R. Levin, M. Dolgin, C. Fox, and R. Gorlin (1994) The criteria committee of the new york heart association: nomenclature and criteria for diagnosis of diseases of the heart and great vessels. LWW Handbooks 9, pp. 344. Cited by: §2.2.
  • C. Li, J. Cummings, J. Lam, E. Graves, and W. Wu (2009) Radar remote monitoring of vital signs. IEEE Microwave Magazine 10 (1), pp. 47–56. External Links: ISSN 1527-3342 Cited by: §1, §3.1.1.
  • Q. Li, C. Rajagopalan, and G. D. Clifford (2014) A machine learning approach to multi-level ecg signal quality classification. Computer methods and programs in biomedicine 117 (3), pp. 435–447. External Links: ISSN 0169-2607, Document, Link Cited by: §3.1.2.
  • G. Y. Lip, R. Nieuwlaat, R. Pisters, D. A. Lane, and H. J. Crijns (2010) Refining clinical risk stratification for predicting stroke and thromboembolism in atrial fibrillation using a novel risk factor-based approach: the euro heart survey on atrial fibrillation. Chest 137 (2), pp. 263–272. External Links: ISSN 0012-3692 Cited by: §5.1.1.
  • Z. C. Lipton, D. C. Kale, and R. Wetzel (2016) Modeling missing data in clinical time series with rnns. arXiv preprint arXiv:1606.04130. Cited by: §3.3.
  • Y. Liu, Z. Li, Z. Liu, and K. Wu (2019) Real-time arm skeleton tracking and gesture inference tolerant to missing wearable sensors. Conference Proceedings In Proceedings of the 17th Annual International Conference on Mobile Systems, Applications, and Services, pp. 287–299. External Links: ISBN 1450366619 Cited by: §3.3.
  • T. Ma and Y. Zhang (2006) A correlation study on the variabilities in pulse transit time, blood pressure, and heart rate recorded simultaneously from healthy subjects. Conference Proceedings In 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference, pp. 996–999. External Links: ISBN 0780387414 Cited by: §3.1.3.
  • R. Maddison, J. C. Rawstorn, A. Rolleston, R. Whittaker, R. Stewart, J. Benatar, I. Warren, Y. Jiang, and N. Gant (2014) The remote exercise monitoring trial for exercise-based cardiac rehabilitation (remote-cr): a randomised controlled trial protocol. BMC Public Health 14 (1), pp. 1236. External Links: ISSN 1471-2458, Document, Link Cited by: §4.1.4.
  • S. M. Mathews, C. Kambhamettu, and K. E. Barner (2018) A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, pp. 53–62. External Links: ISSN 0010-4825, Document, Link Cited by: §4.1.2.
  • R. L. McNamara, Y. Wang, J. Herrin, J. P. Curtis, E. H. Bradley, D. J. Magid, E. D. Peterson, M. Blaney, P. D. Frederick, and H. M. Krumholz (2006) Effect of door-to-balloon time on mortality in patients with st-segment elevation myocardial infarction. Journal of the American College of Cardiology 47 (11), pp. 2180–2186. External Links: ISSN 0735-1097, Document, Link Cited by: §1.
  • A. F. Members, A. J. Camm, G. Y. Lip, R. De Caterina, I. Savelieva, D. Atar, S. H. Hohnloser, G. Hindricks, P. Kirchhof, and E. C. f. P. Guidelines (2012) 2012 focused update of the esc guidelines for the management of atrial fibrillation: an update of the 2010 esc guidelines for the management of atrial fibrillation developed with the special contribution of the european heart rhythm association. European heart journal 33 (21), pp. 2719–2747. External Links: ISSN 1522-9645 Cited by: §5.1.1.
  • G. B. Moody and R. G. Mark (2001) The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20 (3), pp. 45–50. External Links: ISSN 0739-5175, Link Cited by: §4.1.2.
  • C. L. Moore and J. A. Copel (2011) Point-of-care ultrasonography. New England Journal of Medicine 364 (8), pp. 749–757. External Links: ISSN 0028-4793, Document, Link Cited by: §4.1.3.
  • B. J. Mortazavi, N. S. Downing, E. M. Bucholz, K. Dharmarajan, A. Manhapra, S. Li, S. N. Negahban, and H. M. Krumholz (2016) Analysis of machine learning techniques for heart failure readmissions. Circulation: Cardiovascular Quality and Outcomes 9 (6), pp. 629–640. External Links: ISSN 1941-7713, Document, Link Cited by: §1, §4.1.3.
  • B. J. Mortazavi, M. Pourhomayoun, G. Alsheikh, N. Alshurafa, S. I. Lee, and M. Sarrafzadeh (2014a) Determining the single best axis for exercise repetition recognition and counting on smartwatches. Conference Proceedings In 2014 11th International Conference on Wearable and Implantable Body Sensor Networks, pp. 33–38. External Links: ISBN 1479949590 Cited by: §3.1.5, §4.1.4.
  • B. Mortazavi, S. Nyamathi, S. I. Lee, T. Wilkerson, H. Ghasemzadeh, and M. Sarrafzadeh (2013) Near-realistic mobile exergames with wireless wearable sensors. IEEE Journal of Biomedical and Health Informatics 18 (2), pp. 449–456. External Links: ISSN 2168-2194 Cited by: §4.1.4.
  • B. Mortazavi, M. Pourhomayoun, H. Ghasemzadeh, R. Jafari, C. K. Roberts, and M. Sarrafzadeh (2014b) Context-aware data processing to enhance quality of measurements in wireless health systems: an application to met calculation of exergaming actions. IEEE Internet of Things Journal 2 (1), pp. 84–93. External Links: ISSN 2327-4662 Cited by: §1, §3.1.5, §4.1.4.
  • B. K. Nallamothu, S. T. Normand, Y. Wang, T. P. Hofer, J. E. Brush Jr, J. C. Messenger, E. H. Bradley, J. S. Rumsfeld, and H. M. Krumholz (2015) Relation between door-to-balloon times and mortality after primary percutaneous coronary intervention over time: a retrospective study. The Lancet 385 (9973), pp. 1114–1122. External Links: ISSN 0140-6736 Cited by: §1.
  • V. Nathan, S. Paul, T. Prioleau, L. Niu, B. J. Mortazavi, S. A. Cambone, A. Veeraraghavan, A. Sabharwal, and R. Jafari (2018) A survey on smart homes for aging in place: toward solutions to the specific needs of the elderly. IEEE Signal Processing Magazine 35 (5), pp. 111–119. External Links: ISSN 1053-5888 Cited by: §4.1.4.
  • L. Nobel, N. E. Mayo, J. Hanley, L. Nadeau, and S. S. Daskalopoulou (2014) MyRisk_Stroke calculator: a personalized stroke risk assessment tool for the general population. Journal of Clinical Neurology 10 (1), pp. 1–9. External Links: ISSN 1738-6586, Document, Link Cited by: §5.1.1.
  • E. O’brien, B. Waeber, G. Parati, J. Staessen, and M. G. Myers (2001) Blood pressure measuring devices: recommendations of the european society of hypertension. Bmj 322 (7285), pp. 531–536. External Links: ISSN 0959-8138, Document, Link Cited by: §3.1.3.
  • M. H. Olsen, S. Y. Angell, S. Asma, P. Boutouyrie, D. Burger, J. A. Chirinos, A. Damasceno, C. Delles, A. Gimenez-Roqueplo, and D. Hering (2016) A call to action and a lifecourse strategy to address the global burden of raised blood pressure on current and future generations: the lancet commission on hypertension. The Lancet 388 (10060), pp. 2665–2712. External Links: ISSN 0140-6736, Document, Link Cited by: §2.1.
  • M. K. Ong, P. S. Romano, S. Edgington, H. U. Aronow, A. D. Auerbach, J. T. Black, T. De Marco, J. J. Escarce, L. S. Evangelista, and B. Hanna (2016) Effectiveness of remote patient monitoring after discharge of hospitalized patients with heart failure: the better effectiveness after transition–heart failure (beat-hf) randomized clinical trial. JAMA internal medicine 176 (3), pp. 310–318. External Links: ISSN 2168-6106 Cited by: §1, §3.1.4, §4.1.3, §5.1.2.
  • E. Onusko (2003) Diagnosing secondary hypertension. American family physician 67 (1), pp. 67–74. External Links: ISSN 0002-838X, Link Cited by: §2.1.
  • A. Østvik, E. Smistad, S. A. Aase, B. O. Haugen, and L. Lovstakken (2019) Real-time standard view classification in transthoracic echocardiography using convolutional neural networks. Ultrasound in medicine & biology 45 (2), pp. 374–384. External Links: ISSN 0301-5629 Cited by: §4.1.3.
  • K. Ouchi and M. Doi (2013) Smartphone-based monitoring system for activities of daily living for elderly people and their relatives etc. Conference Proceedings In Proceedings of the 2013 ACM conference on Pervasive and ubiquitous computing adjunct publication, pp. 103–106. External Links: ISBN 1450322158 Cited by: §3.1.5.
  • R. Palaniappan, K. Sundaraj, and N. U. Ahamed (2013) Machine learning in lung sound analysis: a systematic review. Biocybernetics and Biomedical Engineering 33 (3), pp. 129–135. External Links: ISSN 0208-5216 Cited by: §4.1.1.
  • M. A. Pfeffer, B. Claggett, S. F. Assmann, R. Boineau, I. S. Anand, N. Clausell, A. S. Desai, R. Diaz, J. L. Fleg, and I. Gordeev (2015) Regional variation in patients and outcomes in the treatment of preserved cardiac function heart failure with an aldosterone antagonist (topcat) trial. Circulation 131 (1), pp. 34–42. External Links: ISSN 0009-7322, Document, Link Cited by: §5.
  • I. Pires, N. Garcia, N. Pombo, and F. Flórez-Revuelta (2016) From data acquisition to data fusion: a comprehensive review and a roadmap for the identification of activities of daily living using mobile devices. Sensors 16 (2), pp. 184. Cited by: §3.1.5.
  • R. Pisters, D. A. Lane, R. Nieuwlaat, C. B. De Vos, H. J. Crijns, and G. Y. Lip (2010) A novel user-friendly score (has-bled) to assess 1-year risk of major bleeding in patients with atrial fibrillation: the euro heart survey. Chest 138 (5), pp. 1093–1100. External Links: ISSN 0012-3692, Document, Link Cited by: §5.1.1.
  • B. Pitt, M. A. Pfeffer, S. F. Assmann, R. Boineau, I. S. Anand, B. Claggett, N. Clausell, A. S. Desai, R. Diaz, J. L. Fleg, et al. (2014) Spironolactone for heart failure with preserved ejection fraction. New England Journal of Medicine 370 (15), pp. 1383–1392. Cited by: §5.
  • M. Pourhomayoun, N. Alshurafa, B. Mortazavi, H. Ghasemzadeh, K. Sideris, B. Sadeghi, M. Ong, L. Evangelista, P. Romano, and A. Auerbach (2014) Multiple model analytics for adverse event prediction in remote health monitoring systems. Conference Proceedings In 2014 IEEE Healthcare Innovation Conference (HIC), pp. 106–110. External Links: ISBN 1467363642 Cited by: §4.1.3.
  • D. M. Reboussin, N. B. Allen, M. E. Griswold, E. Guallar, Y. Hong, D. T. Lackland, E. P. R. Miller, T. Polonsky, A. M. Thompson-Paul, and S. Vupputuri (2018) Systematic review for the 2017 acc/aha/aapa/abc/acpm/ags/apha/ash/aspc/nma/pcna guideline for the prevention, detection, evaluation, and management of high blood pressure in adults: a report of the american college of cardiology/american heart association task force on clinical practice guidelines. Journal of the American College of Cardiology 71 (19), pp. 2176–2198. External Links: ISSN 0735-1097 Cited by: §2.1, §3.1.3.
  • M. T. Ribeiro, S. Singh, and C. Guestrin (2016) Why should i trust you?: explaining the predictions of any classifier. Conference Proceedings In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. External Links: ISBN 1450342329 Cited by: §5.1.3.
  • S. A. Rokni and H. Ghasemzadeh (2016) Plug-n-learn: automatic learning of computational algorithms in human-centered internet-of-things applications. Conference Proceedings In Proceedings of the 53rd Annual Design Automation Conference, pp. 139. External Links: ISBN 1450342361 Cited by: §3.3.
  • S. A. Rokni and H. Ghasemzadeh (2018) Autonomous training of activity recognition algorithms in mobile sensors: a transfer learning approach in context-invariant views. IEEE Transactions on Mobile Computing 17 (8), pp. 1764–1777. External Links: ISSN 1536-1233 Cited by: §3.3.
  • S. A. Rokni, M. Nourollahi, and H. Ghasemzadeh (2018) Personalized human activity recognition using convolutional neural networks. Conference Proceedings In Thirty-Second AAAI Conference on Artificial Intelligence, Cited by: §1.
  • J. A. Rymer and S. V. Rao (2019) Enhancement of risk prediction with machine learning: rise of the machines. JAMA network open 2 (7), pp. e196823–e196823. External Links: ISSN 2574-3805 (Electronic) 2574-3805 (Linking), Document, Link Cited by: §5.
  • M. Sánchez-de-la-Torre, A. Khalyfa, A. Sánchez-de-la-Torre, M. Martinez-Alonso, M. Á. Martinez-García, A. Barceló, P. Lloberes, F. Campos-Rodriguez, F. Capote, and M. J. Diaz-de-Atauri (2015) Precision medicine in patients with resistant hypertension and obstructive sleep apnea: blood pressure response to continuous positive airway pressure treatment. Journal of the American College of Cardiology 66 (9), pp. 1023–1032. External Links: ISSN 0735-1097, Document, Link Cited by: §4.1.3.
  • S. Sen, V. Subbaraju, A. Misra, R. K. Balan, and Y. Lee (2015) The case for smartwatch-based diet monitoring. Conference Proceedings In 2015 IEEE international conference on pervasive computing and communication workshops (PerCom workshops), pp. 585–590. External Links: ISBN 1479984256 Cited by: §3.1.5.
  • A. Sinharay, D. Ghosh, P. Deshpande, S. Alam, R. Banerjee, and A. Pal (2016) Smartphone based digital stethoscope for connected health–a direct acoustic coupling technique. Conference Proceedings In 2016 IEEE First International Conference on Connected Health: Applications, Systems and Engineering Technologies (CHASE), pp. 193–198. External Links: ISBN 1509009434 Cited by: §3.1.1.
  • R. Solis, A. Pakbin, A. Akbari, B. J. Mortazavi, and R. Jafari (2019) A human-centered wearable sensing platform with intelligent automated data annotation capabilities. Conference Proceedings In Proceedings of the International Conference on Internet of Things Design and Implementation, pp. 255–260. External Links: ISBN 1450362834 Cited by: §5.1.3.
  • H. Song, D. Rajan, J. J. Thiagarajan, and A. Spanias (2018)

    Attend and diagnose: clinical time series analysis using attention models

    .
    Conference Proceedings In Thirty-Second AAAI Conference on Artificial Intelligence, Cited by: §4.2.
  • J. C. Sriram, M. Shin, T. Choudhury, and D. Kotz (2009) Activity-aware ecg-based patient authentication for remote health monitoring. Conference Proceedings In Proceedings of the 2009 international conference on Multimodal interfaces, pp. 297–304. External Links: ISBN 1605587729 Cited by: §3.1.2.
  • S. R. Steinhubl, J. Waalen, A. M. Edwards, L. M. Ariniello, R. R. Mehta, G. S. Ebner, C. Carter, K. Baca-Motes, E. Felicione, and T. Sarich (2018) Effect of a home-based wearable continuous ecg monitoring patch on detection of undiagnosed atrial fibrillation: the mstops randomized clinical trial. Jama 320 (2), pp. 146–155. External Links: ISSN 0098-7484, Document, Link Cited by: §4.1.2.
  • H. Suresh, J. J. Gong, and J. V. Guttag (2018) Learning tasks for multitask learning: heterogenous patient populations in the icu. Conference Proceedings In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 802–810. External Links: ISBN 1450355528 Cited by: §5.1.3.
  • S. S. Thomas, V. Nathan, C. Zong, E. Akinbola, A. L. P. Aroul, L. Philipose, K. Soundarapandian, X. Shi, and R. Jafari (2014) BioWatch-a wrist watch based signal acquisition system for physiological signals including blood pressure. Conference Proceedings In 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 2286–2289. External Links: ISBN 1424479290 Cited by: §3.1.3.
  • Z. Trujillo, V. Nathan, G. L. Coté, and R. Jafari (2017) Design and parametric analysis of a wearable dual-photoplethysmograph based system for pulse wave velocity detection. Conference Proceedings In 2017 IEEE International Symposium on Circuits and Systems (ISCAS), pp. 1–4. External Links: ISBN 1467368539 Cited by: §3.1.3.
  • K. Van Laerhoven, M. Borazio, and J. H. Burdinski (2015) Wear is your mobile? investigating phone carrying and use habits with a wearable device. Frontiers in ICT 2, pp. 10. External Links: ISSN 2297-198X Cited by: §3.1.5.
  • R. S. Vasan, A. Beiser, S. Seshadri, M. G. Larson, W. B. Kannel, R. B. D’Agostino, and D. Levy (2002) Residual lifetime risk for developing hypertension in middle-aged women and men: the framingham heart study. Jama 287 (8), pp. 1003–1010. External Links: ISSN 0098-7484, Document, Link Cited by: §1.
  • G. Vinci, S. Lindner, F. Barbon, S. Mann, M. Hofmann, A. Duda, R. Weigel, and A. Koelpin (2013) Six-port radar sensor for remote respiration rate and heartbeat vital-sign monitoring. IEEE Transactions on Microwave Theory and Techniques 61 (5), pp. 2093–2100. External Links: ISSN 0018-9480 Cited by: §3.1.1.
  • J. Wang, Y. Chen, S. Hao, X. Peng, and L. Hu (2019) Deep learning for sensor-based activity recognition: a survey. Pattern Recognition Letters 119, pp. 3–11. External Links: ISSN 0167-8655 Cited by: §1.
  • J. Wasserlauf, C. You, R. Patel, A. Valys, D. Albert, and R. Passman (2019) Smartwatch performance for the detection and quantification of atrial fibrillation. Circulation: Arrhythmia and Electrophysiology 12 (6), pp. e006834. External Links: ISSN 1941-3084, Document, Link Cited by: §1, 2nd item, 2nd item.
  • S. Weyer, T. Menden, L. Leicht, S. Leonhardt, and T. Wartzek (2015) Development of a wearable multi-frequency impedance cardiography device. Journal of medical engineering & technology 39 (2), pp. 131–137. External Links: ISSN 0309-1902, Document, Link Cited by: §4.1.3.
  • S. Wright, D. Verouhis, G. Gamble, K. Swedberg, N. Sharpe, and R. Doughty (2003) Factors influencing the length of hospital stay of patients with heart failure. European Journal of Heart Failure 5 (2), pp. 201–209. External Links: ISSN 1388-9842, Document, Link Cited by: §1.
  • M. Xie, N. Jean, M. Burke, D. Lobell, and S. Ermon (2016)

    Transfer learning from deep features for remote sensing and poverty mapping

    .
    Conference Proceedings In Thirtieth AAAI Conference on Artificial Intelligence, Cited by: §3.3.
  • J. Yao, E. M. Weaver, B. D. Langley, S. M. George, and S. R. Hardin (2017) Monitoring peripheral edema of heart failure patients at home: device, algorithm, and clinic study. Conference Proceedings In 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 4074–4077. External Links: ISBN 1509028099 Cited by: §3.1.4.
  • Ö. Yildirim (2018) A novel wavelet sequence based on deep bidirectional lstm network model for ecg signal classification. Computers in biology and medicine 96, pp. 189–202. External Links: ISSN 0010-4825, Document, Link Cited by: §4.1.2.
  • C. Yu, L. Wang, E. Chau, R. H. Chan, S. Kong, M. Tang, J. Christensen, R. W. Stadler, and C. Lau (2005) Intrathoracic impedance monitoring in patients with heart failure: correlation with fluid status and feasibility of early warning preceding hospitalization. Circulation 112 (6), pp. 841–848. External Links: ISSN 0009-7322, Document, Link Cited by: §4.1.3.
  • Ö. Yürür, C. H. Liu, and W. Moreno (2015) Light-weight online unsupervised posture detection by smartphone accelerometer. IEEE Internet of Things Journal 2 (4), pp. 329–339. External Links: ISSN 2327-4662 Cited by: §3.1.5.
  • Y. Zhang, C. C. Poon, C. Chan, M. W. Tsang, and K. Wu (2006) A health-shirt using e-textile materials for the continuous and cuffless monitoring of arterial blood pressure. Conference Proceedings In 2006 3rd IEEE/EMBS International Summer School on Medical Devices and Biosensors, pp. 86–89. External Links: ISBN 078039786X Cited by: §4.1.4.
  • Z. Zhang (2015) Photoplethysmography-based heart rate monitoring in physical activities via joint sparse spectrum reconstruction. IEEE transactions on biomedical engineering 62 (8), pp. 1902–1910. External Links: ISSN 0018-9294, Document, Link Cited by: §1.
  • B. Ziaeian and G. C. Fonarow (2016) Epidemiology and aetiology of heart failure. Nature Reviews Cardiology 13 (6), pp. 368. External Links: ISSN 1759-5010, Document, Link Cited by: §1.

Appendix A Normal Cardiac Physiology

In order to better understand the opportunities discussed in this work, we provide a brief medical review of normal function of the heart.

The heart consists of four chambers- two atria and two ventricles, one of each composing the right heart and one of each composing the left heart. The two atria (singular atrium) drive blood into the two ventricles, and the ventricles drive blood forward. The right heart takes deoxygenated blood from the body and pumps it through the lungs. The left heart takes blood from the lungs and pumps it through the body. The left heart does more work than the right heart, and the pressures that it produces are higher than the pressures produced by the right heart. There are four valves in the heart. The right heart valves are the tricuspid and the pulmonic. The tricuspid valve is between the right atrium and ventricle, the pulmonic valve is between the right ventricle and the lungs. The valves of the left heart are the mitral and the aortic. The mitral valve is between the left atrium and ventricle, while the aortic valve is between the left ventricle and the rest of the body.

The heart cycles through two phases as it beats- systole and diastole. In systole, the heart contracts and drives blood forward. In diastole, the heart relaxes and refills with blood. One common metric of cardiac function is blood pressure, represented by two numbers- a systolic and a diastolic pressure. The systolic pressure is the pressure in the arteries as blood is actively pumped out of the heart. The diastolic pressure is the pressure in the arteries as the heart relaxes. The heart typically spends about one third of the time in systole, and two thirds of the time in diastole. The normal heart produces two sounds, called S1 and S2. S1 is caused by the closing of the mitral and tricuspid valves at the beginning of systole. Their closure prevents the backwards flow of blood from the ventricles into the atria. S2 is caused by the closing of the aortic and pulmonic valves. Their closure prevents the backwards flow of blood from the body or lungs backwards into the heart.