Sleep-wake classification via quantifying heart rate variability by convolutional neural network

by   John Malik, et al.

Fluctuations in heart rate are intimately tied to changes in the physiological state of the organism. We examine and exploit this relationship by classifying a human subject's wake/sleep status using his instantaneous heart rate (IHR) series. We use a convolutional neural network (CNN) to build features from the IHR series extracted from a whole-night electrocardiogram (ECG) and predict every 30 seconds whether the subject is awake or asleep. Our training database consists of 56 normal subjects, and we consider three different databases for validation; one is private, and two are public with different races and apnea severities. On our private database of 27 subjects, our accuracy, sensitivity, specificity, and AUC values for predicting the wake stage are 83.1 is similar on our two public databases. When we use the photoplethysmography instead of the ECG to obtain the IHR series, the performance is also comparable. A robustness check is carried out to confirm the obtained performance statistics. This result advocates for an effective and scalable method for recognizing changes in physiological state using non-invasive heart rate monitoring. The CNN model adaptively quantifies IHR fluctuation as well as its location in time and is suitable for differentiating between the wake and sleep stages.


page 1

page 2

page 3

page 4


Classifying sleep-wake stages through recurrent neural networks using pulse oximetry signals

The regulation of the autonomic nervous system changes with the sleep st...

Deep learning for automated sleep staging using instantaneous heart rate

Clinical sleep evaluations currently require multimodal data collection ...

Statistical model for describing heart rate variability in normal rhythm and atrial fibrillation

Heart rate variability (HRV) indices describe properties of interbeat in...

Understanding of Normal and Abnormal Hearts by Phase Space Analysis and Convolutional Neural Networks

Cardiac diseases are one of the leading mortality factors in modern, ind...

Phenotype-based and Self-learning Inter-individual Sleep Apnea Screening with a Level IV Monitoring System

Purpose: We propose a phenotype-based artificial intelligence system tha...

Sleep Quality Prediction from Wearables using Convolution Neural Networks and Ensemble Learning

Sleep is among the most important factors affecting one's daily performa...

BeliefPPG: Uncertainty-aware Heart Rate Estimation from PPG signals via Belief Propagation

We present a novel learning-based method that achieves state-of-the-art ...

Please sign up or login with your details

Forgot password? Click here to reset