AIMC Topic: Sleep
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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 ...
Electroencephalogram Spectral Moments for the Detection of Nocturnal Hypoglycemia.
IEEE journal of biomedical and health informatics
Jul 29, 2019
Hypoglycemia or low blood glucose is the most feared complication of insulin treatment of diabetes. For people with diabetes, the mismatch between the insulin therapy and the body's physiology could increase the risk of hypoglycemia. Nocturnal hypogl...
Generalizability of A Neural Network Model for Circadian Phase Prediction in Real-World Conditions.
Scientific reports
Jul 29, 2019
A neural network model was previously developed to predict melatonin rhythms accurately from blue light and skin temperature recordings in individuals on a fixed sleep schedule. This study aimed to test the generalizability of the model to other slee...
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 ...
SPINDLE: End-to-end learning from EEG/EMG to extrapolate animal sleep scoring across experimental settings, labs and species.
PLoS computational biology
Apr 18, 2019
Understanding sleep and its perturbation by environment, mutation, or medication remains a central problem in biomedical research. Its examination in animal models rests on brain state analysis via classification of electroencephalographic (EEG) sign...
A Novel CNN-Based Framework for Classification of Signal Quality and Sleep Position from a Capacitive ECG Measurement.
Sensors (Basel, Switzerland)
Apr 11, 2019
The further exploration of the capacitive ECG (cECG) is hindered by frequent fluctuations in signal quality from body movement and changes in sleep position. The processing framework must be fundamentally adapted to make full use of this signal. Ther...
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...
Modelling PTSD diagnosis using sleep, memory, and adrenergic metabolites: An exploratory machine-learning study.
Human psychopharmacology
Feb 22, 2019
OBJECTIVE: Features of posttraumatic stress disorder (PTSD) typically include sleep disturbances, impaired declarative memory, and hyperarousal. This study evaluated whether these combined features may accurately delineate pathophysiological changes ...
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