Automated recognition of patients with obstructive sleep apnoea using wavelet-based features of electrocardiogram recordings

Ahsan H. Khandoker, Chandan K. Karmakar, Marimuthu Palaniswami

Research output: Contribution to journalArticlepeer-review

65 Scopus citations

Abstract

Patients with obstructive sleep apnoea syndrome (OSAS) are at increased risk of developing hypertension and other cardiovascular diseases. This paper explores the use of support vector machines (SVMs) for automated recognition of patients with OSAS types (±) using features extracted from nocturnal ECG recordings, and compares its performance with other classifiers. Features extracted from wavelet decomposition of heart rate variability (HRV) and ECG-derived respiration (EDR) signals of whole records (30 learning sets from physionet) are presented as inputs to train the SVM classifier to recognize OSAS± subjects. The optimal SVM parameter set is then determined by using a leave-one-out procedure. Independent test results have shown that an SVM using a subset of a selected combination of HRV and EDR features correctly recognized 30/30 of physionet test sets. In comparison, classification performance of K-nearest neighbour, probabilistic neural network, and linear discriminant classifiers on test data was lower. These results, therefore, demonstrate considerable potential in applying SVM in ECG-based screening and can aid sleep specialists in the initial assessment of patients with suspected OSAS.

Original languageBritish English
Pages (from-to)88-96
Number of pages9
JournalComputers in Biology and Medicine
Volume39
Issue number1
DOIs
StatePublished - Jan 2009

Keywords

  • ECG-derived respiration
  • Heart rate variability
  • Obstructive sleep apnoea
  • Support vector machines
  • Wavelet

Fingerprint

Dive into the research topics of 'Automated recognition of patients with obstructive sleep apnoea using wavelet-based features of electrocardiogram recordings'. Together they form a unique fingerprint.

Cite this