DocumentCode :
2638608
Title :
Individual identification based on chaotic electrocardiogram signals
Author :
Chen, Ching-Kun ; Lin, Chun-Liang ; Chiu, Yen-Ming
Author_Institution :
Dept. of Electr. Eng., Nat. Chung Hsing Univ., Taichung, Taiwan
fYear :
2011
fDate :
21-23 June 2011
Firstpage :
1771
Lastpage :
1776
Abstract :
Electrocardiography (ECG) is a transthoracic interpretation of the electrical activity of the human´s heart over time captured and is highly irregular, random, and variable from person to person. Recently, the literature has revealed that this kind of signal is, in fact, chaotic. Because of people´s ECGs are extremely hard to be artificially duplicated, this paper intends to investigate the way of extracting the ECG signals´ biometric features for the possibility of biometric recognition. The ECG signal is converted into the phase plane by using phase space reconstruction. Then, chaos extractor is applied to capture the representative indices of chaotic ECG signals i.e., correlation dimension and Lyapunov exponents spectrum. The root mean square, Lyapunov exponent and correlation dimension are used as the key variables in neural network training and utilized in the identification scheme.
Keywords :
Lyapunov methods; biometrics (access control); chaos; correlation methods; electrocardiography; feature extraction; learning (artificial intelligence); medical signal processing; signal reconstruction; ECG; Lyapunov exponent spectrum; biometric feature extraction; biometric recognition; chaotic electrocardiogram signals; correlation dimension; electrical activity; human heart; individual identification; neural network training; phase space reconstruction; root mean square; transthoracic interpretation; Correlation; Electrocardiography; Lead; Root mean square; Testing; Time series analysis; Training; Biometric recognition; Chaos; Electrocardiogram;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Industrial Electronics and Applications (ICIEA), 2011 6th IEEE Conference on
Conference_Location :
Beijing
ISSN :
pending
Print_ISBN :
978-1-4244-8754-7
Electronic_ISBN :
pending
Type :
conf
DOI :
10.1109/ICIEA.2011.5975879
Filename :
5975879
Link To Document :
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