Person identification using ECG signal’s symbolic representation and dynamic time warping adaptation

Abstract

Since electrocardiogram (ECG) is a unique physiological signal which is existing only in the live people, it has been used in the novel biometric systems to identify people and to counter forge and fraud attacks. Most of existing methods suffer from restriction in detection of various points within ECG signal. In this paper, a new ECG-based identification algorithm is presented. In this method at first, the most important and reliable fiducial point (R peak in each ECG rhythm) is discovered. Then, to reduce redundant information the ECG signal is quantized. Finally, the ECG samples between two successive fiducial R points will be normalized and coded with character strands symbolically. These codes will be extracted at different times for each person and store as biometric feature. After extracting symbolic code of ECG signals, dynamic time warping technique is employed to calculate the similarity between input user symbolic code and reference codes of authorized users. The identity of input ECG is related to the authorized user that has maximum similarity. The proposed method has been tested over 100 subjects, and its identification accuracy was about 99.4%.

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