Individual identification based on chaotic electrocardiogram signals during muscular exercise

Abstract

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.

Recognition System Using Fusion Normalization Based on Morphological Features of Post-Exercise ECG for Intelligent Biometrics

“…An optimized bandpass filter (OBPF) method was proposed for normalizing the ECG measured when the subject was in the post-exercise state after raising a foot, using a bandpass filter after selecting the optimal frequency band [ 20 ]. A method of normalizing the ECG measured, in the phase domain, when the subject was in the post-exercise state after the upright magnetic bike exercise was proposed in [ 29 ].…”

Off-Person ECG Biometrics Using Spatial Representations and Convolutional Neural Networks

“…This approach has also motivated the use of ECG analysis tools rooted in nonlinear dynamic systems theory, including reconstructed phase space portraits, recurrence plots, Lyapunov exponents, and correlation dimension. Following previous approaches [34], [35], we considered state-space trajectories generated according to the celebrated Takens embedding theorem [24] to be applied as inputs to a CNN network for classification purposes. Using a set of time-delayed versions of a generic scalar time series…”

Driver Identification System Using Normalized Electrocardiogram Based on Adaptive Threshold Filter for Intelligent Vehicles

“…The ECG signal measured from the chest at the seat belt position is filtered in the optimal frequency band with the optimized band pass filter (OBPF) method. Lin et al [ 49 ] proposed a normalization method that considers the state of the ECG, which is measured with noises included by movement. In the dynamic state, the ECG cycles measured from the metal rod electrodes are equally normalized in the phase domain.…”