An Obstructive Sleep Apnea Detection Approach Using Kernel Density Classification Based on Single-Lead Electrocardiogram.
Obstructive sleep apnea (OSA) is a common sleep disorder that often remains undiagnosed, leading to an increased risk of developing cardiovascular diseases. Polysomnogram (PSG) is currently used as a golden standard for screening OSA. However, because it is time consuming, expensive and causes disco...
| Publicado en: | Journal of Medical Systems Vol. 39; no. 5; pp. 1 - 12 |
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| Autores principales: | , , |
| Formato: | equations & formulas research tables/charts Journal Article |
| Publicado: |
Springer Nature
May2015
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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=115925089&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 115925089 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01485598 4N0 jtl: Journal of Medical Systems issn: 01485598 maglogo: N pubinfo: dt: May2015 vid: 39 iid: 5 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 115925089 115925089 115925089 10.1007/s10916-015-0222-6 115925089 ppf: 1 ppct: 11 formats: fmt: @attributes: type: P tig: atl: An Obstructive Sleep Apnea Detection Approach Using Kernel Density Classification Based on Single-Lead Electrocardiogram. aug: au: Chen, Lili Zhang, Xi Wang, Hui affil: College of Engineering, Peking University, 100871 Beijing China sug: subj: Sleep Apnea, Obstructive Diagnosis Electrocardiography Evaluation Polysomnography Evaluation Human Electrocardiography Methods Polysomnography Methods Classification Methods Male Female Adult Middle Age Adult: 19-44 years Middle Aged: 45-64 years Male Female ab: Obstructive sleep apnea (OSA) is a common sleep disorder that often remains undiagnosed, leading to an increased risk of developing cardiovascular diseases. Polysomnogram (PSG) is currently used as a golden standard for screening OSA. However, because it is time consuming, expensive and causes discomfort, alternative techniques based on a reduced set of physiological signals are proposed to solve this problem. This study proposes a convenient non-parametric kernel density-based approach for detection of OSA using single-lead electrocardiogram (ECG) recordings. Selected physiologically interpretable features are extracted from segmented RR intervals, which are obtained from ECG signals. These features are fed into the kernel density classifier to detect apnea event and bandwidths for density of each class (normal or apnea) are automatically chosen through an iterative bandwidth selection algorithm. To validate the proposed approach, RR intervals are extracted from ECG signals of 35 subjects obtained from a sleep apnea database (). The results indicate that the kernel density classifier, with two features for apnea event detection, achieves a mean accuracy of 82.07 %, with mean sensitivity of 83.23 % and mean specificity of 80.24 %. Compared with other existing methods, the proposed kernel density approach achieves a comparably good performance but by using fewer features without significantly losing discriminant power, which indicates that it could be widely used for home-based screening or diagnosis of OSA. pubtype: Academic Journal doctype: equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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