AIMC Topic: Sleep Apnea Syndromes

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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 Signal Processing, Computer-Assisted Sleep Apnea Syndromes Respiratory Physiological Phenomena Aged Male 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...
Aged Adult Male Middle Aged Humans Neural Networks, Computer Female Respiratory Rate Polysomnography Signal Processing, Computer-Assisted Sleep Apnea Syndromes Sleep
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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 ...
Sleep Apnea Syndromes Sleep Apnea, Obstructive Humans Neural Networks, Computer Female Electrocardiography Male Signal Processing, Computer-Assisted Middle Aged
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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 ...
Humans Neural Networks, Computer Middle Aged Sleep Apnea Syndromes Female Oximetry Sensitivity and Specificity Male
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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...
Machine Learning Adult Severity of Illness Index Oximetry Home Care Services Signal Processing, Computer-Assisted Sleep Apnea Syndromes Humans Neural Networks, Computer
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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 ...
Polysomnography Acoustics Adult Sleep Body Mass Index Snoring Male Biomarkers Adolescent Sleep Apnea Syndromes Middle Aged Female Child Humans Aged Machine Learning Young Adult
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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...
Humans Neural Networks, Computer Fuzzy Logic Acoustics Respiration Monitoring, Physiologic Sleep Apnea Syndromes 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...
Wakefulness Sleep Oximetry Support Vector Machine Regression Analysis Middle Aged Sleep Apnea Syndromes Severity of Illness Index Humans Female Diagnosis, Computer-Assisted Algorithms Sensitivity and Specificity Photoplethysmography Male
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Multi-channel EEG-based sleep stage classification with joint collaborative representation and multiple kernel learning.

Journal of neuroscience methods Jul 17, 2015
BACKGROUND: Electroencephalography (EEG) based sleep staging is commonly used in clinical routine. Feature extraction and representation plays a crucial role in EEG-based automatic classification of sleep stages. Sparse representation (SR) is a state...
Polysomnography Sleep Stages Sensitivity and Specificity Machine Learning Electroencephalography Brain Humans Sleep Apnea Syndromes
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A Novel Algorithm for the Automatic Detection of Sleep Apnea From Single-Lead ECG.

IEEE transactions on bio-medical engineering Apr 13, 2015
GOAL: This paper presents a methodology for the automatic detection of sleep apnea from single-lead ECG.
Support Vector Machine Male Principal Component Analysis Middle Aged Signal Processing, Computer-Assisted Sleep Apnea Syndromes Humans Electrocardiography Female Adult
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