Use of sample entropy approach to study heart rate variability in obstructive sleep apnea syndrome

Haitham M. Al-Angari*, Alan Varteres Sahakian

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

171 Scopus citations

Abstract

Sample entropy, a nonlinear signal processing approach, was used as a measure of signal complexity to evaluate the cyclic behavior of heart rate variability (HRV) in obstructive sleep apnea syndrome (OSAS). In a group of 10 normal and 25 OSA subjects, the sample entropy measure showed that normal subjects have significantly more complex HRV pattern than the OSA subjects (p < 0.005). When compared with spectral analysis in a minute-by-minute classification, sample entropy had an accuracy of 70.3% (69.5% sensitivity, 70.8% specificity) while the spectral analysis had an accuracy of 70.4% (71.3% sensitivity, 69.9% specificity). The combination of the two methods improved the accuracy to 72.9% (72.2% sensitivity, 73.3% specificity). The sample entropy approach does not show major improvement over the existing methods. In fact, its accuracy in detecting sleep apnea is relatively low in the well classified data of the physionet. Its main achievement however, is the simplicity of computation. Sample entropy and other nonlinear methods might be useful tools to detect apnea episodes during sleep.

Original languageEnglish (US)
Pages (from-to)1900-1904
Number of pages5
JournalIEEE Transactions on Biomedical Engineering
Volume54
Issue number10
DOIs
StatePublished - Oct 1 2007

Keywords

  • Approximate entropy
  • Heart rate variability
  • Nonlinear signal processing
  • Obstructive sleep apnea
  • Power spectral density
  • Sample entropy

ASJC Scopus subject areas

  • Biomedical Engineering

Fingerprint

Dive into the research topics of 'Use of sample entropy approach to study heart rate variability in obstructive sleep apnea syndrome'. Together they form a unique fingerprint.

Cite this