Effects of diseased ECG on the robustness of ECG biometric systems

Author

Loong, Justin Leo Cheang ; Swee, Sim Kok ; Bear, Rosli ; Subari, Khazaimatol S. ; Abdullah, Muhammad Kamil

Author_Institution

Fac. of Eng. & Technol., Multimedia Univ., Ayer Keroh, Malaysia

fYear

2010

fDate

Nov. 30 2010-Dec. 2 2010

Firstpage

307

Lastpage

310

Abstract

This paper looks into the effects of diseased subjects on the recognition rate of an ECG biometric system. A novel technique for feature extraction, linear predictive coding, is implemented along with neural networks for the classifier. Diseased ECG has been shown reduce the recognition rate of the system by only less than 1% and thus the system is robust towards diseased ECG. This allows for the system incorporating linear predictive coding to be used in practical situations where some users may not be aware of their health state and may have diseased ECG signals.

Keywords

biometrics (access control); diseases; electrocardiography; feature extraction; linear predictive coding; medical signal processing; neural nets; signal classification; ECG biometric system recognition rate; ECG biometric system robustness; diseased ECG effects; diseased ECG signals; feature extraction; linear predictive coding; neural network classifier; Artificial neural networks; Diseases; Electrocardiography; Feature extraction; Humans; Security; Training; ECG; biometrics; disease; human identification;

fLanguage

English

Publisher

ieee

Conference_Titel

Biomedical Engineering and Sciences (IECBES), 2010 IEEE EMBS Conference on

Conference_Location

Kuala Lumpur

Print_ISBN

978-1-4244-7599-5

Type

conf

DOI

10.1109/IECBES.2010.5742250

Filename

5742250