AI Medical Compendium Topic

Explore the latest research on artificial intelligence and machine learning in medicine.

Sleep

Showing 171 to 180 of 269 articles

Clear Filters

Hybrid scattering-LSTM networks for automated detection of sleep arousals.

Physiological measurement
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
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
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
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)
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
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
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 ...

Efficient sleep classification based on entropy features and a support vector machine classifier.

Physiological measurement
OBJECTIVE: Sleep quality helps to reflect on the physical and mental condition, and efficient sleep stage scoring promises considerable advantages to health care. The aim of this study is to propose a simple and efficient sleep classification method ...

Sleeping posture recognition using fuzzy c-means algorithm.

Biomedical engineering online
BACKGROUND: Pressure sensors have been used for sleeping posture detection, which meet privacy requirements. Most of the existing techniques for sleeping posture recognition used force-sensitive resistor (FSR) sensors. However, lower limbs cannot be ...

Universal method for robust detection of circadian state from gene expression.

Proceedings of the National Academy of Sciences of the United States of America
Circadian clocks play a key role in regulating a vast array of biological processes, with significant implications for human health. Accurate assessment of physiological time using transcriptional biomarkers found in human blood can significantly imp...