ECG analysis for person identification

Author

Pathoumvanh, Somsanuk ; Airphaiboon, Surapan ; Prapochanung, Benjawan ; Leauhatong, Thurdsak

Author_Institution

Electron. Dept., King Mongkut´s Inst. of Technol., Bangkok, Thailand

fYear

2013

fDate

23-25 Oct. 2013

Firstpage

1

Lastpage

4

Abstract

Electrocardiogram (ECG) has been actively proposed as aliveness biometric. In this paper, the study which concern to a realistic application is proposed. Firstly, a single lead normal ECG signal is acquired from individuals of 10 subjects. Then, each single beat ECG is segmented and analyzed in Continuous Wavelet Transform (CWT) domain. Total energy of wavelet coefficients for each P, QRS, and T segment is calculated. Next, the Fisher Linear Discriminant Analysis (FLDA) is applied. Finally, normalized Euclidean distance is implemented as a classifier. In experimental results, 97% of classification accuracy is achieved in case of a normal ECG (with non-variation of heart rate).

Keywords

biometrics (access control); electrocardiography; medical signal processing; signal classification; wavelet transforms; CWT; ECG analysis; Euclidean distance; FLDA; Fisher linear discriminant analysis; P segment; QRS segment; T segment; aliveness biometric; continuous wavelet transform; electrocardiogram; heart rate; person identification; signal classification; signal segmentation; single lead normal ECG signal; wavelet coefficients; Accuracy; Continuous wavelet transforms; Electrocardiography; Feature extraction; Heart rate variability; Support vector machine classification; ECG Biometrics; ECG Identifications; Single Beat ECG features extraction;

fLanguage

English

Publisher

ieee

Conference_Titel

Biomedical Engineering International Conference (BMEiCON), 2013 6th

Conference_Location

Amphur Muang

Print_ISBN

978-1-4799-1466-1

Type

conf

DOI

10.1109/BMEiCon.2013.6687703

Filename

6687703