QRS-complex of ECG-based biometrics in a two-level classifier

Author

Hou, Loh Sik ; Subari, Khazaimatol S. ; Syahril, Syed

Author_Institution

Fac. of Eng., Multimedia Univ., Cyberjaya, Malaysia

fYear

2011

fDate

21-24 Nov. 2011

Firstpage

1159

Lastpage

1163

Abstract

This research is based on an ECG biometrics system which segments the QRS-complex, extracts the non-fiducial features and sends the data to a two-level classifier. For spectral analysis, the discrete Fourier transform (DFT), and discrete cosine transform (DCT) were used to transform the signal, before principal component analysis (PCA) is used to reduce the feature vectors. From here, statistical parameters were computed for the classifier, where the first level is denoted called feature matching (FM) and the second level is the Neural Networks algorithm (NN). The system is tested on two databases. Database I consists of 45 subjects with 10 recordings each (recorded on the same day) while Database II consists of 35 subjects with 20 recordings each (recorded on separate days). The accuracy measures were is 99.176% and 96.67% respectively.

Keywords

discrete Fourier transforms; discrete cosine transforms; electrocardiography; feature extraction; medical signal processing; neural nets; principal component analysis; signal classification; ECG-based biometrics; QRS-complex segmentation; discrete Fourier transform; discrete cosine transform; feature matching; feature vectors; neural networks algorithm; nonfiducial feature extraction; principal component analysis; spectral analysis; two-level classifier; Accuracy; Artificial neural networks; Biometrics; Databases; Electrocardiography; Feature extraction; Frequency modulation;

fLanguage

English

Publisher

ieee

Conference_Titel

TENCON 2011 - 2011 IEEE Region 10 Conference

Conference_Location

Bali

ISSN

2159-3442

Print_ISBN

978-1-4577-0256-3

Type

conf

DOI

10.1109/TENCON.2011.6129294

Filename

6129294