Detection of Sleep Apnea from Single-Lead ECG Signal Using a Time Window Artificial Neural Network

Sleep apnea (SA) is a ubiquitous sleep-related respiratory disease. It can occur hundreds of times at night, and its long-term occurrences can lead to some serious cardiovascular and neurological diseases. Polysomnography (PSG) is a commonly used diagnostic device for SA. But it requires suspected p...

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Bibliographic Details
Published in:BioMed research international 2019, Vol.2019 (2019), p.1-9
Main Authors: Wang, Tao, Shen, Guohao, Lu, Changhua
Format: Article
Language:English
Subjects:
Accuracy
Apnea
Artificial intelligence
Artificial neural networks
Biomedical research
Datasets
Diagnostic systems
EKG
Electrocardiogram
Electrocardiography
Learning algorithms
Machine learning
Nervous system diseases
Neural networks
Neurological diseases
Parameter estimation
Patients
Public health
Researchers
Respiratory diseases
Respiratory tract diseases
Signal monitoring
Signal processing
Sleep
Sleep apnea
Sleep apnea syndromes
Sleep disorders
Studies
Technology application
Time dependence
Windows (intervals)
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Summary:Sleep apnea (SA) is a ubiquitous sleep-related respiratory disease. It can occur hundreds of times at night, and its long-term occurrences can lead to some serious cardiovascular and neurological diseases. Polysomnography (PSG) is a commonly used diagnostic device for SA. But it requires suspected patients to sleep in the lab for one to two nights and records about 16 signals through expert monitoring. The complex processes hinder the widespread implementation of PSG in public health applications. Recently, some researchers have proposed using a single-lead ECG signal for SA detection. These methods are based on the hypothesis that the SA relies only on the current ECG signal segment. However, SA has time dependence; that is, the SA of the ECG segment at the previous moment has an impact on the current SA diagnosis. In this study, we develop a time window artificial neural network that can take advantage of the time dependence between ECG signal segments and does not require any prior assumptions about the distribution of training data. By verifying on a real ECG signal dataset, the performance of our method has been significantly improved compared to traditional non-time window machine learning methods as well as previous works.
ISSN:2314-6133
2314-6141
DOI:10.1155/2019/9768072