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Systematic comparison of respiratory signals for the automated detection of sleep apnea

Tom Van Steenkiste, W. Groenendaal, Joeri Ruyssinck (UGent) , P. Dreesen, S. Klerkx, C. Smeets, R. de Francisco, Dirk Deschrijver (UGent) and Tom Dhaene (UGent)
(2018) 2018 40TH ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY (EMBC). In IEEE Engineering in Medicine and Biology Society Conference Proceedings p.449-452
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Abstract
Sleep apnea is one of the most common sleep disorders. It is characterized by the cessation of breathing during sleep due to airway blockages (obstructive sleep apnea) or disturbances in the signals from the brain (central sleep apnea). The gold standard for diagnosing sleep apnea is performing an overnight polysomnography recording which contains, among others, a wide array of respiratory signals. Respiration information can also be extracted from other physiological signals such as an electrocardiogram or from a bio-impedance measurement on the chest. Studies have shown that algorithms can be developed for automated sleep apnea detection using one of these many respiratory signals. In this work, the predictive power of these different respiratory signals is analyzed and compared. The results provide useful insights into the comparative predictive power of the different respiratory signals in a realistic setting for automated sleep apnea detection and provide a basis for the development of less obtrusive measurement techniques.
Keywords
DIAGNOSIS

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Citation

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MLA
Van Steenkiste, Tom, et al. “Systematic Comparison of Respiratory Signals for the Automated Detection of Sleep Apnea.” 2018 40TH ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY (EMBC), 2018, pp. 449–52.
APA
Van Steenkiste, T., Groenendaal, W., Ruyssinck, J., Dreesen, P., Klerkx, S., Smeets, C., … Dhaene, T. (2018). Systematic comparison of respiratory signals for the automated detection of sleep apnea. 2018 40TH ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY (EMBC), 449–452.
Chicago author-date
Van Steenkiste, Tom, W. Groenendaal, Joeri Ruyssinck, P. Dreesen, S. Klerkx, C. Smeets, R. de Francisco, Dirk Deschrijver, and Tom Dhaene. 2018. “Systematic Comparison of Respiratory Signals for the Automated Detection of Sleep Apnea.” In 2018 40TH ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY (EMBC), 449–52.
Chicago author-date (all authors)
Van Steenkiste, Tom, W. Groenendaal, Joeri Ruyssinck, P. Dreesen, S. Klerkx, C. Smeets, R. de Francisco, Dirk Deschrijver, and Tom Dhaene. 2018. “Systematic Comparison of Respiratory Signals for the Automated Detection of Sleep Apnea.” In 2018 40TH ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY (EMBC), 449–452.
Vancouver
1.
Van Steenkiste T, Groenendaal W, Ruyssinck J, Dreesen P, Klerkx S, Smeets C, et al. Systematic comparison of respiratory signals for the automated detection of sleep apnea. In: 2018 40TH ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY (EMBC). 2018. p. 449–52.
IEEE
[1]
T. Van Steenkiste et al., “Systematic comparison of respiratory signals for the automated detection of sleep apnea,” in 2018 40TH ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY (EMBC), Honolulu, USA, 2018, pp. 449–452.
@inproceedings{8580843,
  abstract     = {{Sleep apnea is one of the most common sleep disorders. It is characterized by the cessation of breathing during sleep due to airway blockages (obstructive sleep apnea) or disturbances in the signals from the brain (central sleep apnea). The gold standard for diagnosing sleep apnea is performing an overnight polysomnography recording which contains, among others, a wide array of respiratory signals. Respiration information can also be extracted from other physiological signals such as an electrocardiogram or from a bio-impedance measurement on the chest. Studies have shown that algorithms can be developed for automated sleep apnea detection using one of these many respiratory signals. In this work, the predictive power of these different respiratory signals is analyzed and compared. The results provide useful insights into the comparative predictive power of the different respiratory signals in a realistic setting for automated sleep apnea detection and provide a basis for the development of less obtrusive measurement techniques.}},
  author       = {{Van Steenkiste, Tom and Groenendaal, W. and Ruyssinck, Joeri and Dreesen, P. and Klerkx, S. and Smeets, C. and de Francisco, R. and Deschrijver, Dirk and Dhaene, Tom}},
  booktitle    = {{2018 40TH ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY (EMBC)}},
  isbn         = {{9781538636466}},
  issn         = {{1557-170X}},
  keywords     = {{DIAGNOSIS}},
  language     = {{eng}},
  location     = {{Honolulu, USA}},
  pages        = {{449--452}},
  title        = {{Systematic comparison of respiratory signals for the automated detection of sleep apnea}},
  year         = {{2018}},
}
Web of Science
Times cited: