Individual identification with high frequency ECG : Preprocessing and classification by neural network

Author

Tashiro, Futoshi ; Aoyama, Takuya ; Shimuta, Toru ; Ishikawa, Hiroki ; Shimatani, Yuichi ; Ishijima, Masa ; Kyoso, Masaki

Author_Institution

Tokyo City Univ., Tokyo, Japan

fYear

2011

fDate

Aug. 30 2011-Sept. 3 2011

Firstpage

2749

Lastpage

2751

Abstract

In this research, we proposed that high frequency component of HFECG was applicable biometric feature for new identification system. We developed identification method by using neural network (NN), and aimed at the improvement of the classification rate. Preprocessing prior to NN is performed by justification on time axis and normalization on amplitude. As a result, an average of 99% classification rate was obtained from 9 subjects. We also made an attempt to identify in shorter time by shifting of the HFECG by a few samples to NN.

Keywords

biometrics (access control); electrocardiography; medical signal detection; medical signal processing; neural nets; signal classification; HFECG; biometric feature; classification; high frequency ECG; identification method; individual identification; neural network; preprocessing; time axis; Artificial neural networks; Band pass filters; Correlation; Electrocardiography; Indexes; Lead; Security; Electrocardiography; Neural Networks (Computer); Signal Processing, Computer-Assisted;

fLanguage

English

Publisher

ieee

Conference_Titel

Engineering in Medicine and Biology Society, EMBC, 2011 Annual International Conference of the IEEE

Conference_Location

Boston, MA

ISSN

1557-170X

Print_ISBN

978-1-4244-4121-1

Electronic_ISBN

1557-170X

Type

conf

DOI

10.1109/IEMBS.2011.6090753

Filename

6090753