AIMC Topic: Polysomnography

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Automatic sleep scoring: A deep learning architecture for multi-modality time series.

Journal of neuroscience methods Nov 4, 2020
BACKGROUND: Sleep scoring is an essential but time-consuming process, and therefore automatic sleep scoring is crucial and urgent to help address the growing unmet needs for sleep research. This paper aims to develop a versatile deep-learning archite...
Electroencephalography Sleep Polysomnography Sleep Stages Humans Deep Learning
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Sleep stage classification for child patients using DeConvolutional Neural Network.

Artificial intelligence in medicine Nov 2, 2020
Studies from the literature show that the prevalence of sleep disorder in children is far higher than that in adults. Although much research effort has been made on sleep stage classification for adults, children have significantly different characte...
Child Electroencephalography Sleep Polysomnography Neural Networks, Computer Aged Sleep Stages Humans Child, Preschool
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Photoplethysmographic-based automated sleep-wake classification using a support vector machine.

Physiological measurement Aug 11, 2020
OBJECTIVE: Sleep quality has a significant impact on human mental and physical health. The detection of sleep-wake states is thus of paramount importance in the study of sleep. The gold standard method for sleep-wake classification is multi-sensor-ba...
Algorithms Wearable Electronic Devices Photoplethysmography Polysomnography Humans Support Vector Machine Sleep
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Greedy based convolutional neural network optimization for detecting apnea.

Computer methods and programs in biomedicine Jul 4, 2020
BACKGROUND AND OBJECTIVE: Sleep apnea is a common sleep disorder, usually diagnosed using an expensive, highly specialized, and inconvenient test called polysomnography. A single SpO2 sensor based on an automated classification system can be develope...
Electrocardiography Sleep Apnea Syndromes Polysomnography Humans Neural Networks, Computer Databases, Factual
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Distinguishing Obstructive Versus Central Apneas in Infrared Video of Sleep Using Deep Learning: Validation Study.

Journal of medical Internet research May 22, 2020
BACKGROUND: Sleep apnea is a respiratory disorder characterized by an intermittent reduction (hypopnea) or cessation (apnea) of breathing during sleep. Depending on the presence of a breathing effort, sleep apnea is divided into obstructive sleep apn...
Humans Female Sleep Apnea, Obstructive Spectrophotometry, Infrared Polysomnography Male Sleep Apnea, Central Middle Aged Deep Learning
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Application of deep learning to improve sleep scoring of wrist actigraphy.

Sleep medicine May 15, 2020
BACKGROUND: Estimation of sleep parameters by wrist actigraphy is highly dependent on performance of the interpretative algorithm (IA) that converts movement data into sleep/wake scores.
Deep Learning Wrist Sleep Actigraphy Polysomnography Humans Female Adult
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Predicting polysomnographic severity thresholds in children using machine learning.

Pediatric research May 9, 2020
BACKGROUND: Approximately 500,000 children undergo tonsillectomy and adenoidectomy (T&A) annually for treatment of obstructive sleep disordered breathing (oSDB). Although polysomnography is beneficial for preoperative risk stratification in these chi...
Adenoidectomy Sleep Apnea, Obstructive Anthropometry Risk Polysomnography Oximetry Humans Tonsillectomy Actigraphy Machine Learning Signal Processing, Computer-Assisted Hypersensitivity Algorithms Child Sleep Apnea Syndromes Male Monitoring, Physiologic Surveys and Questionnaires Decision Making Female Asthma
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Recognition of Patient Groups with Sleep Related Disorders using Bio-signal Processing and Deep Learning.

Sensors (Basel, Switzerland) May 2, 2020
Accurately diagnosing sleep disorders is essential for clinical assessments and treatments. Polysomnography (PSG) has long been used for detection of various sleep disorders. In this research, electrocardiography (ECG) and electromayography (EMG) hav...
Deep Learning Biosensing Techniques Electrocardiography Signal Processing, Computer-Assisted Humans Algorithms Wavelet Analysis Entropy Polysomnography Sleep Wake Disorders Sleep Apnea, Obstructive Heart Rate Respiration
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Automatic detection of cortical arousals in sleep and their contribution to daytime sleepiness.

Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology Apr 2, 2020
OBJECTIVE: Significant interscorer variability is found in manual scoring of arousals in polysomnographic recordings (PSGs). We propose a fully automatic method, the Multimodal Arousal Detector (MAD), for detecting arousals.
Aged, 80 and over Young Adult Adult Male Neural Networks, Computer Female Cerebral Cortex Disorders of Excessive Somnolence Aged Polysomnography Humans Models, Neurological Arousal Middle Aged Electroencephalography Electromyography Adolescent Sleep Child
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Design of a deep learning model for automatic scoring of periodic and non-periodic leg movements during sleep validated against multiple human experts.

Sleep medicine Jan 23, 2020
OBJECTIVE: Currently, manual scoring is the gold standard of leg movement scoring (LMs) and periodic LMs (PLMS) in overnight polysomnography (PSG) studies, which is subject to inter-scorer variability. The objective of this study is to design and val...
Deep Learning Adult Humans Cohort Studies Nocturnal Myoclonus Syndrome Electromyography Algorithms Male Female Middle Aged Sleep Polysomnography
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