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MSED: a multi-modal sleep event detection model for clinical sleep analysis
Study objective: Clinical sleep analysis require manual analysis of slee...
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SleepNet: Automated Sleep Disorder Detection via Dense Convolutional Neural Network
In this work, a dense recurrent convolutional neural network (DRCNN) was...
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Automated Polysomnography Analysis for Detection of Non-Apneic and Non-Hypopneic Arousals using Feature Engineering and a Bidirectional LSTM Network
Objective: The aim of this study is to develop an automated classificati...
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Sleep Apnea and Respiratory Anomaly Detection from a Wearable Band and Oxygen Saturation
Objective: Sleep related respiratory abnormalities are typically detecte...
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Automatic scoring of apnea and hypopnea events using blood oxygen saturation signals
The obstructive sleep apnea-hypopnea (OSAH) syndrome is a very common an...
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An Update of a Progressively Expanded Database for Automated Lung Sound Analysis
A continuous real-time respiratory sound automated analysis system is ne...
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Contactless Cardiac Arrest Detection Using Smart Devices
Out-of-hospital cardiac arrest (OHCA) is a leading cause of death worldw...
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Automated Respiratory Event Detection Using Deep Neural Networks
The gold standard to assess respiration during sleep is polysomnography; a technique that is burdensome, expensive (both in analysis time and measurement costs), and difficult to repeat. Automation of respiratory analysis can improve test efficiency and enable accessible implementation opportunities worldwide. Using 9,656 polysomnography recordings from the Massachusetts General Hospital (MGH), we trained a neural network (WaveNet) based on a single respiratory effort belt to detect obstructive apnea, central apnea, hypopnea and respiratory-effort related arousals. Performance evaluation included event-based and recording-based metrics - using an apnea-hypopnea index analysis. The model was further evaluated on a public dataset, the Sleep-Heart-Health-Study-1, containing 8,455 polysomnographic recordings. For binary apnea event detection in the MGH dataset, the neural network obtained an accuracy of 95 for the receiver operating characteristics curve and precision-recall curve of 0.93 and 0.74, respectively. For the multiclass task, we obtained varying performances: 81 whereas this metric was 46 related arousals and 16 misclassifications as another type of respiratory event. Our fully automated method can detect respiratory events and assess the apnea-hypopnea index with sufficient accuracy for clinical utilization. Differentiation of event types is more difficult and may reflect in part the complexity of human respiratory output and some degree of arbitrariness in the clinical thresholds and criteria used during manual annotation.
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