Identification of cardiac autonomic neuropathy patients using cardioid based graph for ECG biometric

Sidek, Khairul Azami and Jelinek, Herbert and Khalil, Ibrahim (2011) Identification of cardiac autonomic neuropathy patients using cardioid based graph for ECG biometric. Computing in Cardiology, 38. pp. 517-520. ISSN 0276-6574

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Abstract

In this paper, the application of data mining applied on Cardioid based person identification mechanism using electrocardiogram (ECG) is presented. A total of 50 subjects with Cardiac Autonomic Neuropathy (CAN) were obtained from participants with diabetes from the Charles Sturt Diabetes Complication Screening Initiative (DiScRi). The patients can be categorized into two types of CAN which are early CAN and definite/severe CAN. Euclidean distances obtained as a result of the formation of the Cardioid based graph were used as extracted features. These distances were then applied in Multilayer Perceptron to confirm the identity of individuals. Our experimentation results suggest that person identification is possible by obtaining classification accuracies of 99.6% for patients with early CAN, 99.1% for patients with severe/definite CAN and 99.3% for all the CAN patients. These results indicate that ECG biometric is possible and QRS complex is not severely affected by CAN with the ability to identify and differentiate individuals.

Item Type:	Article (Journal)
Additional Information:	4698/31996
Subjects:	T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK7800 Electronics. Computer engineering. Computer hardware. Photoelectronic devices > TK7885 Computer engineering
Kulliyyahs/Centres/Divisions/Institutes (Can select more than one option. Press CONTROL button):	Kulliyyah of Engineering > Department of Electrical and Computer Engineering
Depositing User:	Assoc Prof Dr Khairul Azami Sidek
Date Deposited:	17 Sep 2013 09:35
Last Modified:	17 Sep 2013 09:35
URI:	http://irep.iium.edu.my/id/eprint/31996

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