Automatic breath-to-breath analysis of nocturnal polysomnographic recordings.
Diagnosis of sleep-disordered breathing is based on the presence of an abnormal breathing pattern during sleep. In this study, an algorithm was developed for the offline breath-to-breath analysis of the nocturnal respiratory recordings. For that purpose, respiratory signals (nasal airway pressure, t...
| Publicado en: | Medical & Biological Engineering & Computing Vol. 49; no. 7; pp. 819 - 831 |
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| Autores principales: | , , , , , , , , , , , , , |
| Formato: | research Journal Article |
| Publicado: |
Springer Nature
Jul2011
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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=104572686&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 104572686 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01400118 PO0 jtl: Medical & Biological Engineering & Computing issn: 01400118 maglogo: N pubinfo: dt: Jul2011 vid: 49 iid: 7 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 104572686 NLM21445719 2011187453 10.1007/s11517-011-0755-x NLM21445719 104572686 ppf: 819 ppct: 12 formats: fmt: @attributes: type: P tig: atl: Automatic breath-to-breath analysis of nocturnal polysomnographic recordings. aug: au: van Houdt PJ Ossenblok PP van Erp MG Schreuder KE Krijn RJ Boon PA Cluitmans PJ van Houdt, P J Ossenblok, P P W van Erp, M G Schreuder, K E Krijn, R J J Boon, P A J M Cluitmans, P J M affil: Department of Research and Development, Kempenhaeghe, Postbus 61, 5590 AB, Heeze, The Netherlands sug: subj: Polysomnography Methods Sleep Apnea Syndromes Diagnosis Adult Algorithms Artifacts Diagnosis, Computer Assisted Methods Pilot Studies Female Human Male Middle Age Respiratory Mechanics Physiology Signal Processing, Computer Assisted Adult: 19-44 years Middle Aged: 45-64 years Female Male ab: Diagnosis of sleep-disordered breathing is based on the presence of an abnormal breathing pattern during sleep. In this study, an algorithm was developed for the offline breath-to-breath analysis of the nocturnal respiratory recordings. For that purpose, respiratory signals (nasal airway pressure, thoracic and abdominal movements) were divided into half waves using period amplitude analysis. Individual breaths were characterized by the parameters of the half waves (duration, amplitude, and slope). These values can be used to discriminate between normal and abnormal breaths. This algorithm was applied to six polysomnographic recordings to distinguish abnormal breathing events (apneas and hypopneas). The algorithm was robust for the identification of breaths (sensitivity = 96.8%, positive prediction value (PPV) = 99.5%). The detection of apneas and hypopneas was compared to the manual scoring of two experienced sleep technicians: sensitivity was, respectively, 89.2 and 88.9%, PPV was 54.1 and 59.3%. The classification of apneas into central, obstructive, or mixed was in concordance with the observers in 68% of the apneas. Although the algorithm tended to detect more hypopneas than the clinical standard, this study shows that the extraction of breath-to-breath parameters is useful for detection of abnormal respiratory events and provides a basis for further characterization of these events. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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