Individual identification based on chaotic electrocardiogram signals during muscular exercise

• Published in: IET biometrics 2014-12, Vol.3 (4), p.257-266
• Main Authors: Lin, Shyan-Lung, Chen, Ching-Kun, Lin, Chun-Liang, Yang, Wen-Chan, Chiang, Cheng-Tang
• Format: Article
• Language: English
• Subjects: Biometric identification; biometric verification; Biometrics; biometrics (access control); cardiac cells; cardiac signal; Chaos theory; chaotic electrocardiogram signals; correlation dimension; correlation methods; Data compression; ECG features; EKG; Electric potential; Electrocardiography; Feasibility studies; Feature recognition; Fourier transforms; Heart; Heart rate; Kernel functions; Liapunov exponents; Lyapunov exponents; Lyapunov methods; mean square error methods; medical signal processing; Morphology; muscular exercise; Neural networks; nonlinear Lyapunov exponent; Nonlinearity; non‐invasive method; personal identification; polynomial kernel function; Polynomials; root mean square value; Signal processing; specific biological characteristics; support vector machine; Support vector machines; SVM; System theory; Time series; Wavelet transforms
• ISSN: 2047-4938; 2047-4946; 2047-4946
• DOI: 10.1049/iet-bmt.2013.0014

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.