eprintid: 12207
rev_number: 2
eprint_status: archive
userid: 1
dir: disk0/00/01/22/07
datestamp: 2023-11-10 03:26:45
lastmod: 2023-11-10 03:26:45
status_changed: 2023-11-10 01:17:08
type: article
metadata_visibility: show
creators_name: Ali, S.Q.
creators_name: Khalid, S.
creators_name: Belhaouari, S.B.
title: A Novel Technique to Diagnose Sleep Apnea in Suspected Patients Using Their ECG Data
ispublished: pub
keywords: Electrocardiography; Probability density function; Signal processing, Breathing disorders; Decision variables; ECG data; Novel techniques; Polysomnography; RR intervals; Sleep apnea; Wavelet packet transforms, Sleep research
note: cited By 5
abstract: Sleep Apnea is a breathing disorder that occurs while the patient is sleeping. Traditionally, Polysomnography is used to diagnose it. However, it is quite inconvenient and expensive. Because of the troublesome diagnosis, this ailment often remained undiagnosed. This paper aims at the development of such a method that provides an easy diagnostic solution to the doctors. Electrocardiogram (ECG) is one of the most common tests done at the hospitals. In this paper, we aim to develop a method which deploys ECG data to diagnose the sleep ailment, Apnea. A technique deploying wavelet packet transform on RR interval of ECG has been presented. Probability density functions of data, both when Apnea is present and when it is not, are obtained by constructing histograms of decision variable for each signal segment. From the overlapping PDFs of the normal and abnormal cases, a threshold is then derived. This helped in segregating the Apnea cases from normal cases. The stated method provided a 100 accuracy in diagnosing Sleep Apnea. © 2013 IEEE.
date: 2019
publisher: Institute of Electrical and Electronics Engineers Inc.
official_url: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85063769951&doi=10.1109%2fACCESS.2019.2904601&partnerID=40&md5=0398384a3e40417b5cd57eb4db992a8d
id_number: 10.1109/ACCESS.2019.2904601
full_text_status: none
publication: IEEE Access
volume: 7
pagerange: 35184-35194
refereed: TRUE
issn: 21693536
citation:   Ali, S.Q. and Khalid, S. and Belhaouari, S.B.  (2019) A Novel Technique to Diagnose Sleep Apnea in Suspected Patients Using Their ECG Data.  IEEE Access, 7.  pp. 35184-35194.  ISSN 21693536