ECG bio-identification using Fréchet classifiers: A proposed methodology based on modeling the dynamic change of the ECG features

Abstract

Recently, the use of electrocardiogram (ECG) for human identification has attracted great attention. Generally, most existing ECG based biometric systems relay on extracting the static fiducial or non-fiducial features of the cardiac signal. However, the recorded ECG data is more likely to be different whenever it is measured. Such problem can be addressed by utilizing the dynamic change in ECG features. This paper proposes a new methodology for human identification via ECG, based on tracking the dynamic change in ECG features and utilizing the Fréchet distance measures for multiclass classification of feature matrices. The proposed dynamic feature matrices can be utilized to model nonstationary signals because they provide continues information on feature variability. Technically, we utilize the consecutive change of ECG power spectral density as significant feature. In addition, we use the dynamic change of QRS features as a distinguishable characteristic. At the classification stage, we use equations of Fréchet distances to perform multiclass classification because the covariance matrices of the dynamic feature matrices are symmetric positive definite, and their relative geometric space is not Euclidian. The performance of our methodology was evaluated using the publicly available ECG ID database of 62 subjects. To support real world applicability of our method, we randomized the reference / test data selection using data windowing techniques for examining the stability of our method by changing the datasets. The experimental results show that our methodology was able to achieve an identification accuracy of 97.03% with 0.971 precision, 0.999 specificity, 0.97 recall, 0.029 false rejection rate and 0.00048 false acceptance rate. Furthermore, the findings of our work show that Fréchet distances perform better than the Euclidian distance for ECG data classification in the context of multiclass classification problems.