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...
| Publicado en: | BioMed Research International pp. 1 - 11 |
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| Autores principales: | , , |
| Formato: | equations & formulas research tables/charts tracings Journal Article |
| Publicado: |
Wiley-Blackwell
12/23/2019
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=140825412&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 140825412 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 23146133 FT2T jtl: BioMed Research International issn: 23146133 maglogo: N pubinfo: dt: 12/23/2019 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 140825412 140825412 140825412 10.1155/2019/9768072 140825412 ppf: 1 ppct: 10 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Detection of Sleep Apnea from Single-Lead ECG Signal Using a Time Window Artificial Neural Network. aug: au: Wang, Tao Lu, Changhua Shen, Guohao affil: School of Computer and Information, Hefei University of Technology, Hefei, Anhui, China sug: subj: Neural Networks (Computer) Methods Electrocardiography Methods Sleep Apnea Syndromes Diagnosis Human Polysomnography Machine Learning Methods Algorithms ab: 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. pubtype: Academic Journal doctype: equations & formulas research tables/charts tracings Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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