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Automatic breath-to-breath analysis of nocturnal polysomnographic recordings

PJ van Houdt, PPW Ossenblok, MG van Erp, KE Schreuder, RJJ Krijn, Paul Boon UGent and PJM Cluitmans (2011) MEDICAL & BIOLOGICAL ENGINEERING & COMPUTING. 49(7). p.819-830
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.
Please use this url to cite or link to this publication:
author
organization
year
type
journalArticle (original)
publication status
published
subject
keyword
PLETHYSMOGRAPHY, CLASSIFICATION, INSPIRATORY FLOW LIMITATION, OBSTRUCTIVE SLEEP-APNEA, Computerized detection, Polysomnography, Flow-volume curve, Period amplitude analysis, Sleep-disordered breathing, DIAGNOSIS, EVENTS, PRESSURE, EEG
journal title
MEDICAL & BIOLOGICAL ENGINEERING & COMPUTING
Med. Biol. Eng. Comput.
volume
49
issue
7
pages
819 - 830
Web of Science type
Article
Web of Science id
000292965800012
JCR category
COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
JCR impact factor
1.878 (2011)
JCR rank
25/99 (2011)
JCR quartile
2 (2011)
ISSN
0140-0118
DOI
10.1007/s11517-011-0755-x
language
English
UGent publication?
no
classification
A1
copyright statement
I have transferred the copyright for this publication to the publisher
id
3025094
handle
http://hdl.handle.net/1854/LU-3025094
date created
2012-10-11 15:32:20
date last changed
2013-07-17 11:41:29
@article{3025094,
  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       = {van Houdt, PJ and Ossenblok, PPW and van Erp, MG and Schreuder, KE and Krijn, RJJ and Boon, Paul and Cluitmans, PJM},
  issn         = {0140-0118},
  journal      = {MEDICAL \& BIOLOGICAL ENGINEERING \& COMPUTING},
  keyword      = {PLETHYSMOGRAPHY,CLASSIFICATION,INSPIRATORY FLOW LIMITATION,OBSTRUCTIVE SLEEP-APNEA,Computerized detection,Polysomnography,Flow-volume curve,Period amplitude analysis,Sleep-disordered breathing,DIAGNOSIS,EVENTS,PRESSURE,EEG},
  language     = {eng},
  number       = {7},
  pages        = {819--830},
  title        = {Automatic breath-to-breath analysis of nocturnal polysomnographic recordings},
  url          = {http://dx.doi.org/10.1007/s11517-011-0755-x},
  volume       = {49},
  year         = {2011},
}

Chicago
van Houdt, PJ, PPW Ossenblok, MG van Erp, KE Schreuder, RJJ Krijn, Paul Boon, and PJM Cluitmans. 2011. “Automatic Breath-to-breath Analysis of Nocturnal Polysomnographic Recordings.” Medical & Biological Engineering & Computing 49 (7): 819–830.
APA
van Houdt, PJ, Ossenblok, P., van Erp, M., Schreuder, K., Krijn, R., Boon, P., & Cluitmans, P. (2011). Automatic breath-to-breath analysis of nocturnal polysomnographic recordings. MEDICAL & BIOLOGICAL ENGINEERING & COMPUTING, 49(7), 819–830.
Vancouver
1.
van Houdt P, Ossenblok P, van Erp M, Schreuder K, Krijn R, Boon P, et al. Automatic breath-to-breath analysis of nocturnal polysomnographic recordings. MEDICAL & BIOLOGICAL ENGINEERING & COMPUTING. 2011;49(7):819–30.
MLA
van Houdt, PJ, PPW Ossenblok, MG van Erp, et al. “Automatic Breath-to-breath Analysis of Nocturnal Polysomnographic Recordings.” MEDICAL & BIOLOGICAL ENGINEERING & COMPUTING 49.7 (2011): 819–830. Print.