Title: Detecting obstructive sleep apnea by extracting multimodal HRV features using ensemble subspace discriminant classifier

Authors: Nivedita Singh; R.H. Talwekar

Addresses: RCET, BHILAI, CG, India ' CG GEC, Raipur, CG, India

Abstract: Obstructive sleep apnea disorder is very peculiar sleep disorder which is triggered because of rapid and repeated transition of breathing. Hypopnea is also known as partial blockage of respiration during sleep. Polysomnography is gold standard to detect OSA but it is very expensive and complex which motivates us to detect OSA through multimodal heart rate variability (HRV) feature analysis using single channel ECG. The comparison among three classifiers SVM, weighted KNN and ensemble subspace discriminant are investigated for OSA detection. The accuracy obtained by the ESD classifier is 100%. True positive rate (TPR) and the true negative rate (TNR) have been attained 100% which is best suitable classifier for our experiment.

Keywords: obstructive sleep apnea; ensemble subspace discriminant; heart rate variability; multimodal features.

DOI: 10.1504/IJMEI.2023.134539

International Journal of Medical Engineering and Informatics, 2023 Vol.15 No.6, pp.573 - 587

Received: 27 May 2021
Accepted: 12 Sep 2021
Published online: 27 Oct 2023 *

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