Automated detection of obstructive sleep apnoea syndrome from oxygen saturation recordings using linear discriminant analysis.
Nocturnal polysomnography (PSG) is the gold-standard to diagnose obstructive sleep apnoea syndrome (OSAS). However, it is complex, expensive, and time-consuming. We present an automatic OSAS detection algorithm based on classification of nocturnal oxygen saturation (SaO(2)) recordings. The algorithm...
| Publicado en: | Medical & Biological Engineering & Computing Vol. 48; no. 9; pp. 895 - 903 |
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| Autores principales: | , , , , , , , , , |
| Formato: | research Journal Article |
| Publicado: |
Springer Nature
Sep2010
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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=104569310&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 104569310 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01400118 PO0 jtl: Medical & Biological Engineering & Computing issn: 01400118 maglogo: N pubinfo: dt: Sep2010 vid: 48 iid: 9 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 104569310 NLM20574725 2010755597 10.1007/s11517-010-0646-6 NLM20574725 104569310 ppf: 895 ppct: 8 formats: fmt: @attributes: type: P tig: atl: Automated detection of obstructive sleep apnoea syndrome from oxygen saturation recordings using linear discriminant analysis. aug: au: Marcos JV Hornero R Alvarez D Campo FD Aboy M Marcos, J Víctor Hornero, Roberto Alvarez, Daniel Del Campo, Félix Aboy, Mateo affil: ETSI de Telecomunicación, University of Valladolid, Valladolid, Spain sug: subj: Oxygen Blood Sleep Apnea, Obstructive Diagnosis Adult Aged Algorithms Female Human Linear Regression Male Middle Age Oximetry Methods Prospective Studies Signal Processing, Computer Assisted Adult: 19-44 years Aged: 65+ years Middle Aged: 45-64 years Female Male ab: Nocturnal polysomnography (PSG) is the gold-standard to diagnose obstructive sleep apnoea syndrome (OSAS). However, it is complex, expensive, and time-consuming. We present an automatic OSAS detection algorithm based on classification of nocturnal oxygen saturation (SaO(2)) recordings. The algorithm makes use of spectral and nonlinear analysis for feature extraction, principal component analysis (PCA) for preprocessing and linear discriminant analysis (LDA) for classification. We conducted a study to characterize and prospectively validate our OSAS detection algorithm. The population under study was composed of subjects suspected of suffering from OSAS. A total of 214 SaO(2) signals were available. These signals were randomly divided into a training set (85 signals) and a test set (129 signals) to prospectively validate the proposed method. The OSAS detection algorithm achieved a diagnostic accuracy of 93.02% (97.00% sensitivity and 79.31% specificity) on the test set. It outperformed other alternative implementations that either use spectral and nonlinear features separately or are based on logistic regression (LR). The proposed method could be a useful tool to assist in early OSAS diagnosis, contributing to overcome the difficulties of conventional PSG. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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