Feature selection on single-lead ECG for obstructive sleep apnea diagnosis

Hüseyin Gürüler, Mesut Şahin, Abdullah Ferikoǧlu

Research output: Contribution to journalArticlepeer-review

7 Scopus citations

Abstract

Many articles that appeared in the literature agreed upon the feasibility of diagnosing obstructive sleep apnea (OSA) with a single-lead electrocardiogram. Although high accuracies have been achieved in detection of apneic episodes and classification into apnea/hypopnea, there has not been a consensus on the best method of selecting the feature parameters. This study presents a classification scheme for OSA using common features belonging to the time domain, frequency domain, and nonlinear calculations of heart rate variability analysis, and then proposes a method of feature selection based on correlation matrices (CMs). The results show that the CMs can be utilized in minimizing the feature sets used for any type of diagnosis.

Original languageEnglish (US)
Pages (from-to)465-478
Number of pages14
JournalTurkish Journal of Electrical Engineering and Computer Sciences
Volume22
Issue number2
DOIs
StatePublished - 2014

All Science Journal Classification (ASJC) codes

  • Computer Science(all)
  • Electrical and Electronic Engineering

Keywords

  • Classification
  • Correlation matrices
  • Diagnosing
  • Feature selection
  • Heart rate variability
  • Sleep apnea

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