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

Khairul Azami Sidek, Herbert F. Jelinek, Ibrahim Khalil

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

14 Scopus citations


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.

Original languageBritish English
Title of host publicationComputing in Cardiology 2011, CinC 2011
Number of pages4
StatePublished - 2011
EventComputing in Cardiology 2011, CinC 2011 - Hangzhou, China
Duration: 18 Sep 201121 Sep 2011

Publication series

NameComputing in Cardiology
ISSN (Print)2325-8861
ISSN (Electronic)2325-887X


ConferenceComputing in Cardiology 2011, CinC 2011


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