Key result
LDA, KNN, and MLP algorithms validate ECG signals as robust biometric markers for user identification.
Why the study?
The study evaluated the feasibility of using the ECG signal as an unequivocal biometric marker for authentication and identification across varying acquisition sources and conditions.
ECG signals can serve as robust biometric markers for authentication, with LDA, KNN, and MLP algorithms performing best without PCA compression.
May support ECG biometrics in small cohorts; leaves open scalability, security, and clinical validation.
In this paper, the possibility of using the ECG signal as an unequivocal biometric marker for authentication and identification purposes has been presented. Furthermore, since the ECG signal was acquired from 4 sources using different measurement equipment, electrodes positioning and number of patients as well as the duration of the ECG record acquisition, we have additionally provided an estimation of the extent of information available in the ECG record. To provide a more objective assessment of the credibility of the identification method, some selected machine learning algorithms were used in two combinations: with and without compression. The results that we have obtained confirm that the ECG signal can be acclaimed as a valid biometric marker that is very robust to hardware variations, noise and artifacts presence, that is stable over time and that is scalable across quite a solid (~100) number of users. Our experiments indicate that the most promising algorithms for ECG identification are LDA, KNN and MLP algorithms. Moreover, our results show that PCA compression, used as part of data preprocessing, does not only bring any noticeable benefits but in some cases might even reduce accuracy.
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Pelc et al. (2019) studied this question. Machine learning algorithms (LDA, KNN, MLP) vs. PCA compression vs no compression was evaluated on ECG identification accuracy. ECG signals serve as a valid and robust biometric marker for identification across approximately 100 users, with LDA, KNN, and MLP algorithms showing the most promise.
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