AIMC Topic: Polysomnography

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Machine learning prediction of sleep stages in dairy cows from heart rate and muscle activity measures.

Scientific reports May 25, 2021
Sleep is important for cow health and shows promise as a tool for assessing welfare, but methods to accurately distinguish between important sleep stages are difficult and impractical to use with cattle in typical farm environments. The objective of ...
Heart Rate Polysomnography Sleep Stages Myocardium Models, Biological Neural Networks, Computer Machine Learning Animals Cattle ROC Curve
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Towards More Accurate Automatic Sleep Staging via Deep Transfer Learning.

IEEE transactions on bio-medical engineering May 21, 2021
BACKGROUND: Despite recent significant progress in the development of automatic sleep staging methods, building a good model still remains a big challenge for sleep studies with a small cohort due to the data-variability and data-inefficiency issues....
Sleep Polysomnography Humans Sleep Stages Neural Networks, Computer Machine Learning
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A Deep Learning Strategy for Automatic Sleep Staging Based on Two-Channel EEG Headband Data.

Sensors (Basel, Switzerland) May 11, 2021
Sleep disturbances are common in Alzheimer's disease and other neurodegenerative disorders, and together represent a potential therapeutic target for disease modification. A major barrier for studying sleep in patients with dementia is the requiremen...
Sleep Stages Electroencephalography Humans Sleep Deep Learning Polysomnography
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Multitask fMRI and machine learning approach improve prediction of differential brain activity pattern in patients with insomnia disorder.

Scientific reports Apr 30, 2021
We investigated the differential spatial covariance pattern of blood oxygen level-dependent (BOLD) responses to single-task and multitask functional magnetic resonance imaging (fMRI) between patients with psychophysiological insomnia (PI) and healthy...
Adult Male Middle Aged Brain Female Sleep Initiation and Maintenance Disorders Polysomnography Psychophysiologic Disorders Case-Control Studies Support Vector Machine Magnetic Resonance Imaging Humans
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A model for obstructive sleep apnea detection using a multi-layer feed-forward neural network based on electrocardiogram, pulse oxygen saturation, and body mass index.

Sleep & breathing = Schlaf & Atmung Mar 22, 2021
PURPOSE: To develop and evaluate a model for obstructive sleep apnea (OSA) detection using an artificial neural network (ANN) based on the combined features of body mass index (BMI), electrocardiogram (ECG), and pulse oxygen saturation (SpO2).
Models, Biological Sleep Apnea, Obstructive Oxygen Saturation Polysomnography Oximetry Middle Aged Electrocardiography Body Mass Index Sensitivity and Specificity Male Humans Neural Networks, Computer Female Adult
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DeepSleep convolutional neural network allows accurate and fast detection of sleep arousal.

Communications biology Jan 4, 2021
Sleep arousals are transient periods of wakefulness punctuated into sleep. Excessive sleep arousals are associated with symptoms such as sympathetic activation, non-restorative sleep, and daytime sleepiness. Currently, sleep arousals are mainly annot...
Polysomnography Humans Arousal Deep Learning Sleep
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Deep Neural Network Sleep Scoring Using Combined Motion and Heart Rate Variability Data.

Sensors (Basel, Switzerland) Dec 23, 2020
Performance of wrist actigraphy in assessing sleep not only depends on the sensor technology of the actigraph hardware but also on the attributes of the interpretative algorithm (IA). The objective of our research was to improve assessment of sleep ...
Humans Neural Networks, Computer Actigraphy Female Male Polysomnography Heart Rate Sleep
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Detection of Snore from OSAHS Patients Based on Deep Learning.

Journal of healthcare engineering Dec 12, 2020
Obstructive sleep apnea-hypopnea syndrome (OSAHS) is extremely harmful to the human body and may cause neurological dysfunction and endocrine dysfunction, resulting in damage to multiple organs and multiple systems throughout the body and negatively ...
Oximetry Sleep Apnea, Obstructive Snoring Humans Polysomnography Deep Learning
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Long Short-Term Memory Networks for Unconstrained Sleep Stage Classification Using Polyvinylidene Fluoride Film Sensor.

IEEE journal of biomedical and health informatics Dec 4, 2020
Sleep stage scoring is the first step towards quantitative analysis of sleep using polysomnography (PSG) recordings. However, although PSG is a gold standard method for assessing sleep, it is obtrusive and difficult to apply for long-term sleep monit...
Adult Male Middle Aged Adolescent Signal Processing, Computer-Assisted Polyvinyls Retrospective Studies Female Young Adult Polysomnography Neural Networks, Computer Sleep Stages Humans Machine Learning
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Machine learning for nocturnal mass diagnosis of atrial fibrillation in a population at risk of sleep-disordered breathing.

Physiological measurement Nov 6, 2020
OBJECTIVE: In this research, we introduce a new methodology for atrial fibrillation (AF) diagnosis during sleep in a large population sample at risk of sleep-disordered breathing.
Atrial Fibrillation Humans Polysomnography Machine Learning Risk Factors Electrocardiography Sleep Apnea Syndromes
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