Open AccessIndividual identification based on chaotic electrocardiogram signals during muscular exercise
Author(s) -
Lin ShyanLung,
Chen ChingKun,
Lin ChunLiang,
Yang WenChan,
Chiang ChengTang
Publication year - 2014
Publication title -
iet biometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.434
H-Index - 28
eISSN - 2047-4946
pISSN - 2047-4938
DOI - 10.1049/iet-bmt.2013.0014
Subject(s) - support vector machine , computer science , biometrics , pattern recognition (psychology) , artificial intelligence , chaotic , lyapunov exponent , correlation dimension , identification (biology) , kernel (algebra) , nonlinear system , polynomial kernel , speech recognition , machine learning , mathematics , kernel method , fractal dimension , botany , biology , mathematical analysis , physics , combinatorics , quantum mechanics , fractal
An electrocardiogram (ECG) records changes in the electric potential of cardiac cells using a noninvasive method. Previous studies have shown that each person's cardiac signal possesses unique characteristics. Thus, researchers have attempted to use ECG signals for personal identification. However, most studies verify results using ECG signals taken from databases which are obtained from subjects under the condition of rest. Therefore, the extraction and analysis of a subject's ECG typically occurs in the resting state. This study presents experiments that involve recording ECG information after the heart rate of the subjects was increased through exercise. This study adopts the root mean square value, nonlinear Lyapunov exponent, and correlation dimension to analyse ECG data, and uses a support vector machine (SVM) to classify and identify the best combination and the most appropriate kernel function of a SVM. Results show that the successful recognition rate exceeds 80% when using the nonlinear SVM with a polynomial kernel function. This study confirms the existence of unique ECG features in each person. Even in the condition of exercise, chaotic theory can be used to extract specific biological characteristics, confirming the feasibility of using ECG signals for biometric verification.