AIMC Topic: Sleep Apnea Syndromes

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Non-invasive machine learning estimation of effort differentiates sleep-disordered breathing pathology.

Physiological measurement Feb 26, 2019
OBJECTIVE: Obstructive sleep-disordered breathing (SDB) events, unlike central events, are associated with increased respiratory effort. Esophageal pressure (P ) monitoring is the gold standard for measuring respiratory effort, but it is typically po...
Humans Machine Learning Software Male Signal Processing, Computer-Assisted Sleep Apnea Syndromes Polysomnography Databases, Factual Female Adult
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An Intelligent Sleep Apnea Classification System Based on EEG Signals.

Journal of medical systems Jan 8, 2019
Sleep Apnea is a sleep disorder which causes stop in breathing for a short duration of time that happens to human beings and animals during sleep. Electroencephalogram (EEG) plays a vital role in detecting the sleep apnea by sensing and recording the...
Polysomnography Electroencephalography Signal Processing, Computer-Assisted Sleep Apnea Syndromes Humans Adult Middle Aged Neural Networks, Computer Support Vector Machine Male Machine Learning
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Automated Sleep Apnea Detection in Raw Respiratory Signals Using Long Short-Term Memory Neural Networks.

IEEE journal of biomedical and health informatics Dec 10, 2018
Sleep apnea is one of the most common sleep disorders and the consequences of undiagnosed sleep apnea can be very severe, ranging from increased blood pressure to heart failure. However, many people are often unaware of their condition. The gold stan...
Neural Networks, Computer Machine Learning Databases, Factual Female Humans Monitoring, Physiologic Aged Sleep Apnea Syndromes Signal Processing, Computer-Assisted Male Respiratory Physiological Phenomena Middle Aged
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Real-time apnea-hypopnea event detection during sleep by convolutional neural networks.

Computers in biology and medicine Jun 28, 2018
Sleep apnea-hypopnea event detection has been widely studied using various biosignals and algorithms. However, most minute-by-minute analysis techniques have difficulty detecting accurate event start/end positions. Furthermore, they require hand-engi...
Sleep Respiratory Rate Polysomnography Middle Aged Signal Processing, Computer-Assisted Sleep Apnea Syndromes Humans Neural Networks, Computer Adult Male Female Aged
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Multiclass classification of obstructive sleep apnea/hypopnea based on a convolutional neural network from a single-lead electrocardiogram.

Physiological measurement Jun 20, 2018
OBJECTIVE: In this paper, we propose a convolutional neural network (CNN)-based deep learning architecture for multiclass classification of obstructive sleep apnea and hypopnea (OSAH) using single-lead electrocardiogram (ECG) recordings. OSAH is the ...
Male Middle Aged Electrocardiography Signal Processing, Computer-Assisted Humans Neural Networks, Computer Female Sleep Apnea Syndromes Sleep Apnea, Obstructive
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A Novel Artificial Neural Network Based Sleep-Disordered Breathing Screening Tool.

Journal of clinical sleep medicine : JCSM : official publication of the American Academy of Sleep Medicine Jun 15, 2018
STUDY OBJECTIVES: This study evaluated a novel artificial neural network (ANN) based sleep-disordered breathing (SDB) screening tool incorporating nocturnal pulse oximetry with demographic, anatomic, and clinical data. The tool was compatible with 6 ...
Neural Networks, Computer Female Sensitivity and Specificity Sleep Apnea Syndromes Oximetry Male Middle Aged Humans
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Evaluation of Machine-Learning Approaches to Estimate Sleep Apnea Severity From At-Home Oximetry Recordings.

IEEE journal of biomedical and health informatics Apr 5, 2018
Complexity, costs, and waiting list issues demand a simplified alternative for sleep apnea-hypopnea syndrome (SAHS) diagnosis. The blood oxygen saturation signal (SpO) carries useful information about SAHS and can be easily acquired from overnight ox...
Humans Home Care Services Severity of Illness Index Signal Processing, Computer-Assisted Neural Networks, Computer Machine Learning Sleep Apnea Syndromes Adult Oximetry
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Detection of sleep disordered breathing severity using acoustic biomarker and machine learning techniques.

Biomedical engineering online Feb 1, 2018
PURPOSE: Breathing sounds during sleep are altered and characterized by various acoustic specificities in patients with sleep disordered breathing (SDB). This study aimed to identify acoustic biomarkers indicative of the severity of SDB by analyzing ...
Body Mass Index Sleep Apnea Syndromes Acoustics Polysomnography Sleep Snoring Male Middle Aged Adolescent Child Aged Young Adult Humans Adult Machine Learning Biomarkers Female
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Adaptive neuro-fuzzy inference system for breath phase detection and breath cycle segmentation.

Computer methods and programs in biomedicine Apr 14, 2017
BACKGROUND: The monitoring of the respiratory rate is vital in several medical conditions, including sleep apnea because patients with sleep apnea exhibit an irregular respiratory rate compared with controls. Therefore, monitoring the respiratory rat...
Neural Networks, Computer Fuzzy Logic Monitoring, Physiologic Sleep Apnea Syndromes Humans Acoustics Respiration Respiratory Rate
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Automatic classification of apnea/hypopnea events through sleep/wake states and severity of SDB from a pulse oximeter.

Physiological measurement Aug 11, 2015
This study proposes a method of automatically classifying sleep apnea/hypopnea events based on sleep states and the severity of sleep-disordered breathing (SDB) using photoplethysmogram (PPG) and oxygen saturation (SpO2) signals acquired from a pulse...
Sleep Oximetry Severity of Illness Index Sensitivity and Specificity Sleep Apnea Syndromes Diagnosis, Computer-Assisted Humans Male Photoplethysmography Wakefulness Algorithms Middle Aged Support Vector Machine Female Regression Analysis
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