Development of cardioid based graph ECG heart abnormalities classification technique

In this study, the development of Cardioid based graph electrocardiogram heart abnormalities classification technique is presented. ECG signals in this work were acquired from a public online database UCD Sleep Apnea database (UCDB) with sampling rate of 250 Hz. Each recording has 60 seconds of elec...

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Main Authors:	Mohd Azam, Siti Nurfarah Ain, Zainal, Nur Izzati, Sidek, Khairul Azami
Format:	Article
Language:	English
Published:	Asian Research Publishing Network (ARPN) 2015
Subjects:	TK7885 Computer engineering
Online Access:	http://irep.iium.edu.my/46305/ http://irep.iium.edu.my/46305/ http://irep.iium.edu.my/46305/1/jeas_1115_2979.pdf

id	iium-46305
recordtype	eprints
spelling	iium-463052017-11-15T06:58:55Z http://irep.iium.edu.my/46305/ Development of cardioid based graph ECG heart abnormalities classification technique Mohd Azam, Siti Nurfarah Ain Zainal, Nur Izzati Sidek, Khairul Azami TK7885 Computer engineering In this study, the development of Cardioid based graph electrocardiogram heart abnormalities classification technique is presented. ECG signals in this work were acquired from a public online database UCD Sleep Apnea database (UCDB) with sampling rate of 250 Hz. Each recording has 60 seconds of electrocardiogram signals. Unique features were extracted using the Pan Tompkins algorithm, later Cardioid based graph was formed as the result of the differentiation process. The various shapes of closed-loop created were then observed. From the Cardioid loop, we evaluated the area and standard deviation to differentiate between normal and abnormal heartbeats. As a result, the area and standard deviation values of abnormal heartbeat were twice the value of a normal heartbeat thus indicating the differences between two types of heart morphologies. In order to justify the results, the signal is then classified by using Bayes Network classifier. Classification outcomes suggests that the proposed technique gives heart abnormality identification with a classification accuracy of as low as 12.5% when normal and abnormal heartbeat are matched (two different conditions). Thus, the output of the study suggests the proof-of-concept of our proposed mechanisms to detect heart abnormalities and has the potential to act as an alternative to the current techniques. Asian Research Publishing Network (ARPN) 2015-11 Article PeerReviewed application/pdf en http://irep.iium.edu.my/46305/1/jeas_1115_2979.pdf Mohd Azam, Siti Nurfarah Ain and Zainal, Nur Izzati and Sidek, Khairul Azami (2015) Development of cardioid based graph ECG heart abnormalities classification technique. ARPN Journal of Engineering and Applied Sciences, 10 (21). pp. 9759-9765. ISSN 1819-6608 http://www.arpnjournals.org/jeas/research_papers/rp_2015/jeas_1115_2979.pdf
repository_type	Digital Repository
institution_category	Local University
institution	International Islamic University Malaysia
building	IIUM Repository
collection	Online Access
language	English
topic	TK7885 Computer engineering
spellingShingle	TK7885 Computer engineering Mohd Azam, Siti Nurfarah Ain Zainal, Nur Izzati Sidek, Khairul Azami Development of cardioid based graph ECG heart abnormalities classification technique
description	In this study, the development of Cardioid based graph electrocardiogram heart abnormalities classification technique is presented. ECG signals in this work were acquired from a public online database UCD Sleep Apnea database (UCDB) with sampling rate of 250 Hz. Each recording has 60 seconds of electrocardiogram signals. Unique features were extracted using the Pan Tompkins algorithm, later Cardioid based graph was formed as the result of the differentiation process. The various shapes of closed-loop created were then observed. From the Cardioid loop, we evaluated the area and standard deviation to differentiate between normal and abnormal heartbeats. As a result, the area and standard deviation values of abnormal heartbeat were twice the value of a normal heartbeat thus indicating the differences between two types of heart morphologies. In order to justify the results, the signal is then classified by using Bayes Network classifier. Classification outcomes suggests that the proposed technique gives heart abnormality identification with a classification accuracy of as low as 12.5% when normal and abnormal heartbeat are matched (two different conditions). Thus, the output of the study suggests the proof-of-concept of our proposed mechanisms to detect heart abnormalities and has the potential to act as an alternative to the current techniques.
format	Article
author	Mohd Azam, Siti Nurfarah Ain Zainal, Nur Izzati Sidek, Khairul Azami
author_facet	Mohd Azam, Siti Nurfarah Ain Zainal, Nur Izzati Sidek, Khairul Azami
author_sort	Mohd Azam, Siti Nurfarah Ain
title	Development of cardioid based graph ECG heart abnormalities classification technique
title_short	Development of cardioid based graph ECG heart abnormalities classification technique
title_full	Development of cardioid based graph ECG heart abnormalities classification technique
title_fullStr	Development of cardioid based graph ECG heart abnormalities classification technique
title_full_unstemmed	Development of cardioid based graph ECG heart abnormalities classification technique
title_sort	development of cardioid based graph ecg heart abnormalities classification technique
publisher	Asian Research Publishing Network (ARPN)
publishDate	2015
url	http://irep.iium.edu.my/46305/ http://irep.iium.edu.my/46305/ http://irep.iium.edu.my/46305/1/jeas_1115_2979.pdf
first_indexed	2023-09-18T21:05:56Z
last_indexed	2023-09-18T21:05:56Z
_version_	1777410932601782272

Development of cardioid based graph ECG heart abnormalities classification technique

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