ECG personal identification in subspaces using radial basis neural networks

Author

Boumbarov, Ognian ; Velchev, Yuliyan ; Sokolov, Strahil

Author_Institution

Tech. Univ. of Sofia, Sofia, Bulgaria

fYear

2009

fDate

21-23 Sept. 2009

Firstpage

446

Lastpage

451

Abstract

In this paper an approach for personal biometric identification is presented based on extraction of ECG features and classification with RBFNN. We perform denoising and segmentation on the input signal, after which we realize dimensionality reduction and feature extraction based on PCA transform. The separability of the selected features is improved by applying LDA. The final stage of the proposed approach is classification and recognition of the extracted features with classifier score fusion.

Keywords

biometrics (access control); electrocardiography; feature extraction; hidden Markov models; medical signal processing; principal component analysis; radial basis function networks; signal classification; signal denoising; transforms; ECG classification; ECG feature extraction; ECG personal biometric identification; HMM; LDA; PCA transform; RBFNN; classifier score fusion; dimensionality reduction; extracted feature recognition; hidden Markov model; linear discriminant analysis; radial basis neural network; signal denoising; signal segmentation; Autocorrelation; Biometrics; Discrete cosine transforms; Electrocardiography; Feature extraction; Humans; Linear discriminant analysis; Neural networks; Noise reduction; Principal component analysis; ECG personal identification; HMM; LDA; PCA; neural networks;

fLanguage

English

Publisher

ieee

Conference_Titel

Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications, 2009. IDAACS 2009. IEEE International Workshop on

Conference_Location

Rende

Print_ISBN

978-1-4244-4901-9

Electronic_ISBN

978-1-4244-4882-1

Type

conf

DOI

10.1109/IDAACS.2009.5342942

Filename

5342942