Med Biol Eng Comput - Automatic breath-to-breath analysis of nocturnal polysomnographic recordings.

Tópicos

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Resumo

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.

Resumo Limpo

diagnosi sleepdisord breath base presenc abnorm breath pattern sleep studi algorithm develop offlin breathtobreath analysi nocturn respiratori record purpos respiratori signal nasal airway pressur thorac abdomin movement divid half wave use period amplitud analysi individu breath character paramet half wave durat amplitud slope valu can use discrimin normal abnorm breath algorithm appli six polysomnograph record distinguish abnorm breath event apnea hypopnea algorithm robust identif breath sensit posit predict valu ppv detect apnea hypopnea compar manual score two experienc sleep technician sensit respect ppv classif apnea central obstruct mix concord observ apnea although algorithm tend detect hypopnea clinic standard studi show extract breathtobreath paramet use detect abnorm respiratori event provid basi character event

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