Automatic breath-to-breath analysis of nocturnal polysomnographic recordings

P.J. Houdt, van, P.P.W. Ossenblok, M.G. Erp, van, K.E. Schreuder, R.J.J. Krijn, P. Boon, P.J.M. Cluitmans

Research output: Contribution to journalArticleAcademicpeer-review

11 Citations (Scopus)
1 Downloads (Pure)

Abstract

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.
Original languageEnglish
Pages (from-to)819-830
Number of pages12
JournalMedical and Biological Engineering and Computing
Volume49
Issue number7
DOIs
Publication statusPublished - 2011

Fingerprint

Sleep

Cite this

@article{edc28e411dba49b7aa91d9827088bda9,
title = "Automatic breath-to-breath analysis of nocturnal polysomnographic recordings",
abstract = "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.",
author = "{Houdt, van}, P.J. and P.P.W. Ossenblok and {Erp, van}, M.G. and K.E. Schreuder and R.J.J. Krijn and P. Boon and P.J.M. Cluitmans",
year = "2011",
doi = "10.1007/s11517-011-0755-x",
language = "English",
volume = "49",
pages = "819--830",
journal = "Medical and Biological Engineering and Computing",
issn = "0140-0118",
publisher = "Springer",
number = "7",

}

Automatic breath-to-breath analysis of nocturnal polysomnographic recordings. / Houdt, van, P.J.; Ossenblok, P.P.W.; Erp, van, M.G.; Schreuder, K.E.; Krijn, R.J.J.; Boon, P.; Cluitmans, P.J.M.

In: Medical and Biological Engineering and Computing, Vol. 49, No. 7, 2011, p. 819-830.

Research output: Contribution to journalArticleAcademicpeer-review

TY - JOUR

T1 - Automatic breath-to-breath analysis of nocturnal polysomnographic recordings

AU - Houdt, van, P.J.

AU - Ossenblok, P.P.W.

AU - Erp, van, M.G.

AU - Schreuder, K.E.

AU - Krijn, R.J.J.

AU - Boon, P.

AU - Cluitmans, P.J.M.

PY - 2011

Y1 - 2011

N2 - 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.

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.

U2 - 10.1007/s11517-011-0755-x

DO - 10.1007/s11517-011-0755-x

M3 - Article

C2 - 21445719

VL - 49

SP - 819

EP - 830

JO - Medical and Biological Engineering and Computing

JF - Medical and Biological Engineering and Computing

SN - 0140-0118

IS - 7

ER -