ECG signal analysis for the assessment of sleep-disordered breathing and sleep pattern.
The diagnosis of sleep-disordered breathing (SDB) usually relies on the analysis of complex polysomnographic measurements performed in specialized sleep centers. Automatic signal analysis is a promising approach to reduce the diagnostic effort. This paper addresses SDB and sleep assessment solely ba...
| Publicado en: | Medical & Biological Engineering & Computing Vol. 50; no. 2; pp. 135 - 145 |
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| Autores principales: | , , , , , , , , , |
| Formato: | Journal Article |
| Publicado: |
Springer Nature
Feb2012
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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=104512670&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 104512670 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01400118 PO0 jtl: Medical & Biological Engineering & Computing issn: 01400118 maglogo: N pubinfo: dt: Feb2012 vid: 50 iid: 2 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 104512670 NLM22194020 2011450348 10.1007/s11517-011-0853-9 NLM22194020 104512670 ppf: 135 ppct: 10 formats: fmt: @attributes: type: P tig: atl: ECG signal analysis for the assessment of sleep-disordered breathing and sleep pattern. aug: au: Kesper K Canisius S Penzel T Ploch T Cassel W Kesper, K Canisius, S Penzel, T Ploch, T Cassel, W affil: Department for Internal Medicine, Section Respiratory Diseases, Faculty of Medicine, Philipps-University Marburg, Baldingerstr. 1, 35043 Marburg, Germany sug: subj: Signal Processing, Computer Assisted Sleep Apnea Syndromes Diagnosis Adult Aged Algorithms Electrocardiography Methods Heart Rate Physiology Human Middle Age Sleep Stages Physiology Young Adult Adult: 19-44 years Aged: 65+ years Middle Aged: 45-64 years ab: The diagnosis of sleep-disordered breathing (SDB) usually relies on the analysis of complex polysomnographic measurements performed in specialized sleep centers. Automatic signal analysis is a promising approach to reduce the diagnostic effort. This paper addresses SDB and sleep assessment solely based on the analysis of a single-channel ECG recorded overnight by a set of signal analysis modules. The methodology of QRS detection, SDB analysis, calculation of ECG-derived respiration curves, and estimation of a sleep pattern is described in detail. SDB analysis detects specific cyclical variations of the heart rate by correlation analysis of a signal pattern and the heart rate curve. It was tested with 35 SDB-annotated ECGs from the Apnea-ECG Database, and achieved a diagnostic accuracy of 80.5%. To estimate sleep pattern, spectral parameters of the heart rate are used as stage classifiers. The reliability of the algorithm was tested with 18 ECGs extracted from visually scored polysomnographies of the SIESTA database; 57.7% of all 30 s epochs were correctly assigned by the algorithm. Although promising, these results underline the need for further testing in larger patient groups with different underlying diseases. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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