Detection of Sleep Apnea from Single-Lead ECG Signal Using a Time Window Artificial Neural Network
Joint Authors
Wang, Tao
Lu, Changhua
Shen, Guohao
Source
Issue
Vol. 2019, Issue 2019 (31 Dec. 2019), pp.1-9, 9 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2019-12-23
Country of Publication
Egypt
No. of Pages
9
Main Subjects
Abstract EN
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.
American Psychological Association (APA)
Wang, Tao& Lu, Changhua& Shen, Guohao. 2019. Detection of Sleep Apnea from Single-Lead ECG Signal Using a Time Window Artificial Neural Network. BioMed Research International،Vol. 2019, no. 2019, pp.1-9.
https://search.emarefa.net/detail/BIM-1128828
Modern Language Association (MLA)
Wang, Tao…[et al.]. Detection of Sleep Apnea from Single-Lead ECG Signal Using a Time Window Artificial Neural Network. BioMed Research International No. 2019 (2019), pp.1-9.
https://search.emarefa.net/detail/BIM-1128828
American Medical Association (AMA)
Wang, Tao& Lu, Changhua& Shen, Guohao. Detection of Sleep Apnea from Single-Lead ECG Signal Using a Time Window Artificial Neural Network. BioMed Research International. 2019. Vol. 2019, no. 2019, pp.1-9.
https://search.emarefa.net/detail/BIM-1128828
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1128828