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

Khairul A. Sidek; Herbert Jelinek; Ibrahim Khalil

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

Khairul A. Sidek, Herbert Jelinek, Ibrahim Khalil

RMIT University

Research output: Book chapter/Published conference paper › Conference paper › peer-review

14 Citations (Scopus)

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 categorised 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. This result indicates that ECG biometric is possible and QRS complex is not severely affected for individuals affected by CAN with the ability to identify and differentiate individuals.

Original language	English
Title of host publication	Computing in Cardiology 2011
Subtitle of host publication	Volume 38
Place of Publication	United States
Publisher	IEEE, Institute of Electrical and Electronics Engineers
Pages	517-520
Number of pages	4
Volume	38
ISBN (Print)	9781457706127
Publication status	Published - 2011
Event	38th Computing in Cardiology Conference, CinC 2011 - Zhejiang University, Hangzhou, China Duration: 18 Sept 2011 → 21 Sept 2011 https://web.archive.org/web/20110527173607/https://cinc.org/2011/ (Conference website) http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=6156579 (Conference proceedings)

Publication series

Name
ISSN (Print)	0276-6574

Conference

Conference	38th Computing in Cardiology Conference, CinC 2011
Country/Territory	China
City	Hangzhou
Period	18/09/11 → 21/09/11
Other	Computing in Cardiology provides an international forum for scientists and professionals from the fields of medicine, physics, engineering and computer science, and has been held annually since 1974.
Internet address	https://web.archive.org/web/20110527173607/https://cinc.org/2011/ (Conference website) http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=6156579 (Conference proceedings)

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Cite this

@inproceedings{fc2ab6c97a63428a8bd73d1d1834b37b,

title = "Identification of cardiac autonomic neuropathy patients using cardioid based graph for ECG biometric",

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 categorised 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. This result indicates that ECG biometric is possible and QRS complex is not severely affected for individuals affected by CAN with the ability to identify and differentiate individuals.",

keywords = "Electrocardiography , Diabetes , Feature extraction , Accuracy , Educational institutions , Vectors",

author = "Sidek, {Khairul A.} and Herbert Jelinek and Ibrahim Khalil",

year = "2011",

language = "English",

isbn = "9781457706127",

volume = "38",

publisher = "IEEE, Institute of Electrical and Electronics Engineers",

pages = "517--520",

booktitle = "Computing in Cardiology 2011",

address = "United States",

note = "38th Computing in Cardiology Conference, CinC 2011<br/> ; Conference date: 18-09-2011 Through 21-09-2011",

url = "https://web.archive.org/web/20110527173607/https://cinc.org/2011/, http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=6156579",

}

Identification of cardiac autonomic neuropathy patients using cardioid based graph for ECG biometric. / Sidek, Khairul A.; Jelinek, Herbert; Khalil, Ibrahim.
Computing in Cardiology 2011: Volume 38. Vol. 38 United States: IEEE, Institute of Electrical and Electronics Engineers, 2011. p. 517-520.

Research output: Book chapter/Published conference paper › Conference paper › peer-review

TY - GEN

T1 - Identification of cardiac autonomic neuropathy patients using cardioid based graph for ECG biometric

AU - Sidek, Khairul A.

AU - Jelinek, Herbert

AU - Khalil, Ibrahim

PY - 2011

Y1 - 2011

N2 - 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 categorised 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. This result indicates that ECG biometric is possible and QRS complex is not severely affected for individuals affected by CAN with the ability to identify and differentiate individuals.

AB - 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 categorised 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. This result indicates that ECG biometric is possible and QRS complex is not severely affected for individuals affected by CAN with the ability to identify and differentiate individuals.

KW - Electrocardiography , Diabetes , Feature extraction , Accuracy , Educational institutions , Vectors

M3 - Conference paper

SN - 9781457706127

VL - 38

SP - 517

EP - 520

BT - Computing in Cardiology 2011

PB - IEEE, Institute of Electrical and Electronics Engineers

CY - United States

T2 - 38th Computing in Cardiology Conference, CinC 2011<br/>

Y2 - 18 September 2011 through 21 September 2011

ER -

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

Abstract

Publication series

Conference

UN SDGs

Fingerprint

Cite this