AIMC Topic: Polysomnography
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SleepNet: automated sleep analysis via dense convolutional neural network using physiological time series.
Physiological measurement
Sep 3, 2019
OBJECTIVE: In this work, a dense recurrent convolutional neural network (DRCNN) was constructed to detect sleep disorders including arousal, apnea and hypopnea using polysomnography (PSG) measurement channels provided in the 2018 PhysioNet Challenge ...
A Hierarchical Neural Network for Sleep Stage Classification Based on Comprehensive Feature Learning and Multi-Flow Sequence Learning.
IEEE journal of biomedical and health informatics
Aug 27, 2019
Automatic sleep staging methods usually extract hand-crafted features or network trained features from signals recorded by polysomnography (PSG), and then estimate the stages by various classifiers. In this study, we propose a classification approach...
Automated sleep scoring: A review of the latest approaches.
Sleep medicine reviews
Aug 9, 2019
Clinical sleep scoring involves a tedious visual review of overnight polysomnograms by a human expert, according to official standards. It could appear then a suitable task for modern artificial intelligence algorithms. Indeed, machine learning algor...
Hybrid scattering-LSTM networks for automated detection of sleep arousals.
Physiological measurement
Jul 23, 2019
OBJECTIVE: Early detection of sleep arousal in polysomnographic (PSG) signals is crucial for monitoring or diagnosing sleep disorders and reducing the risk of further complications, including heart disease and blood pressure fluctuations.
Sleep stage classification using covariance features of multi-channel physiological signals on Riemannian manifolds.
Computer methods and programs in biomedicine
Jun 10, 2019
BACKGROUND AND OBJECTIVE: The recognition of many sleep related pathologies highly relies on an accurate classification of sleep stages. Clinically, sleep stages are usually labelled by sleep experts through visually inspecting the whole-night polyso...
Using heart rate profiles during sleep as a biomarker of depression.
BMC psychiatry
Jun 7, 2019
BACKGROUND: Abnormalities in heart rate during sleep linked to impaired neuro-cardiac modulation may provide new information about physiological sleep signatures of depression. This study assessed the validity of an algorithm using patterns of heart ...
A RR interval based automated apnea detection approach using residual network.
Computer methods and programs in biomedicine
May 8, 2019
BACKGROUND AND OBJECTIVE: Apnea is one of the most common conditions that causes sleep-disorder breathing. With growing number of patients worldwide, more and more patients suffer from complications of apnea. But most of them stay untreated due to th...
A review of automated sleep stage scoring based on physiological signals for the new millennia.
Computer methods and programs in biomedicine
May 2, 2019
BACKGROUND AND OBJECTIVE: Sleep is an important part of our life. That importance is highlighted by the multitude of health problems which result from sleep disorders. Detecting these sleep disorders requires an accurate interpretation of physiologic...
DOSED: A deep learning approach to detect multiple sleep micro-events in EEG signal.
Journal of neuroscience methods
Apr 1, 2019
BACKGROUND: Electroencephalography (EEG) monitors brain activity during sleep and is used to identify sleep disorders. In sleep medicine, clinicians interpret raw EEG signals in so-called sleep stages, which are assigned by experts to every 30s windo...
Objective Relationship Between Sleep Apnea and Frequency of Snoring Assessed by Machine Learning.
Journal of clinical sleep medicine : JCSM : official publication of the American Academy of Sleep Medicine
Mar 15, 2019
STUDY OBJECTIVES: Snoring is perceived to be directly proportional to sleep apnea severity, especially obstructive sleep apnea (OSA), but this notion has not been thoroughly and objectively evaluated, despite its popularity in clinical practice. This...
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