QT correction for fiducial ECG features based biometric systems

Author

Tantawi, M. ; Tolba, M.F. ; Salem, Ashraf ; Revett, K.

Author_Institution

Fac. of Comput. & Inf. Sci., Ain Shams Univ., Cairo, Egypt

fYear

2013

fDate

26-28 Nov. 2013

Firstpage

172

Lastpage

176

Abstract

In this paper, an electrocardiogram (ECG) based biometric system is proposed. A QT correction step is introduced to obviate the impact of heart rate variability, instead of just normalizing the features by the corresponding RR duration. Consequently, both approaches were examined in this work. Two sets of fiducial features were investigated: a super set of 36 features and a reduced version of it. Radial basis functions neural network is used as a classifier. The evaluation of the system was performed on the basis of subject identification (SI) accuracy and heartbeat recognition (HR) accuracy. The experiments were conducted using a 50-subject database and the results revealed the superiority of the QT correction approach, especially over time.

Keywords

biometrics (access control); electrocardiography; feature extraction; medical signal processing; radial basis function networks; signal classification; QT correction; RR duration; electrocardiogram based biometric system; feature normalization; fiducial ECG features based biometric systems; heart rate variability; heartbeat recognition accuracy; radial basis functions neural network classifier; subject identification accuracy; Accuracy; Electrocardiography; Feature extraction; Heart beat; Heart rate variability; Silicon; Biometrics; Electrocardiogram (ECG); Fiducial features; QT correction; Radial basis functions (RBF) neural networks; Subject identification;

fLanguage

English

Publisher

ieee

Conference_Titel

Computer Engineering & Systems (ICCES), 2013 8th International Conference on

Conference_Location

Cairo

Print_ISBN

978-1-4799-0078-7

Type

conf

DOI

10.1109/ICCES.2013.6707196

Filename

6707196