Biometric Authentication Based on Electrocardiogram

Bogdanov, Momcilo; Filippova, A.S.; Shakhmametova, G. R.; Oskin, Nikolai N.

doi:10.5772/intechopen.91172

Cited by 2 publications

(2 citation statements)

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“…As mentioned above, we used the public ECG ID database of 62 subjects because it was originally recorded for biometric purposes [25]. For each subject, we selected two ECG recordings.…”

Section: The Ecg Referencing and Testing Datamentioning

confidence: 99%

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The Feasibility of Human Identification from Multiple ECGs using Maximal Overlap Discrete Wavelet Transform (MODWT) and Weighted Majority Voting Method (WMVM)

Biran

Jeremić

2023

DMHT

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Electrocardiography (ECG) has been a subject of research interest in human identification because it is a promising biometric trait that is believed to have discriminatory characteristics. However, features of ECGs that are recorded at different times are often likely to vary significantly. To address the variability of ECG features over multiple records, we propose a new methodology for human identification using ECGs recorded on different days. To demonstrate the applicability of our method, we use the publicly available ECG ID dataset. The main goal of this work is to extract the most significant and discriminative wavelet components of the ECG signal, followed by utilizing the ECG spectral change for human identification using multi-level filtering technique. Our proposed multi-channel identification system is based on using the Maximal Overlap Discrete Wavelet Transform (MODWT) and its inverse (the IMODWT) to create multiple filtered ECG signals. The discriminative feature that we utilize for human identification is based on modeling the dynamic change of the frequency components in these multiple filtered signals. To reach the best possible identification performance, we use the Weighted Majority Voting Method (WMVM) for ECG classification. We evaluated the robustness of our proposed method over several random experiments and obtained 92.29% average identification accuracy, 0.9495 precision, 0.9229 recall, 0.0771 FRR and 0.0013 FAR. These results indicate that filtering some of the ECG wavelet components along with performing data fusion technique can be utilized for human identification.

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“…As mentioned above, we used the public ECG ID database of 62 subjects because it was originally recorded for biometric purposes [25]. For each subject, we selected two ECG recordings.…”

Section: The Ecg Referencing and Testing Datamentioning

confidence: 99%

“…For each subject, two 20-second ECG recordings were chosen. These ECG signals were recorded over a six-month period [24,25]. subjects were excluded because their T wave is tall and higher than the QRS complex.…”

Section: Introductionmentioning

confidence: 99%