Probabilistic cardiac and respiratory based classification of sleep and apneic events in subjects with sleep apnea

T. Willemen, C. Varon, A. Caicedo Dorado, B. Haex, J. Vander Sloten, S. Van Huffel

Research output: Contribution to journalArticlepeer-review

7 Scopus citations

Abstract

Current clinical standards to assess sleep and its disorders lack either accuracy or user-friendliness. They are therefore difficult to use in cost-effective population-wide screening or long-term objective follow-up after diagnosis. In order to fill this gap, the use of cardiac and respiratory information was evaluated for discrimination between different sleep stages, and for detection of apneic breathing. Alternative probabilistic visual representations were also presented, referred to as the hypnocorrogram and apneacorrogram. Analysis was performed on the UCD sleep apnea database, available on Physionet. The presence of apneic events proved to have a significant impact on the performance of a cardiac and respiratory based algorithm for sleep stage classification. WAKE versus SLEEP discrimination resulted in a kappa value of κ = 0.439, while REM versus NREM resulted in κ = 0.298 and light sleep (N1N2) versus deep sleep (N3) in κ = 0.339. The high proportion of hypopneic events led to poor detection of apneic breathing, resulting in a kappa value of κ = 0.272. While the probabilistic representations allow to put classifier output in perspective, further improvements would be necessary to make the classifier reliable for use on patients with sleep apnea.

Original languageEnglish (US)
Pages (from-to)2103-2118
Number of pages16
JournalPhysiological Measurement
Volume36
Issue number10
DOIs
StatePublished - Aug 19 2015
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Biophysics
  • Physiology
  • Biomedical Engineering
  • Physiology (medical)

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