DIAGNOSING SLEEP APNEA VIA FEATURE SELECTION ON SINGLE CHANNEL ECG

Dublin Core

Title

DIAGNOSING SLEEP APNEA VIA FEATURE SELECTION ON SINGLE CHANNEL ECG

Author

GURULER, Huseyin
FERIKOGLU, Abdullah

Abstract

This article is based on a combination of time-frequency domain functions, and nonlinear techniques in the analysis of heart rate variability (HRV) for diagnosing obstructive sleep apnea (OSA) using only single-lead electrocardiography (ECG) signals. The contribution of the presented study to earlier ones is that it enables numerically determining what type of HRV features better represent the aforementioned target by using correlation matrices and neural networks (NNs). Keywords: Diagnosing disease, neural network, sleep apnea, heart rate variability, feature selection, correlation matrices

Keywords

Article
PeerReviewed

Identifier

ISSN 978-9958-834-36-3

Publisher

International Burch University

Date

2014-05-15

Extent

2516

Document Viewer