A Machine Learning-based ECG Biometric Authentication
DOI: https://doi.org/10.46610/IJAIMLECT.2025.v001i02.005
DOI:
https://doi.org/10.46610/IJAIMLECT.2025.v001i02.005Keywords:
Biometric authentication, ECG, Identification, Machine learning, MATLAB, SecurityAbstract
Biometric authentication is increasingly crucial for ensuring the highest security standards in security-sensitive applications, as it uses individual-specific traits to identify individuals. The informative features of an ECG signal are determined by its characteristic points, namely R-peak anchoring, which are highly unique to each individual, cannot be faked, and are increasingly popular among users as a potential tool for authentication. However, the ECG signals vary widely with time, stress, etc., even for an individual; accurate extraction of the ECG signal is difficult, and efficient recognition is cumbersome. Being the safest and most secure biometric authentication tool, therefore, overcoming the challenges faced by the ECG has drawn the attention of the scientific community in recent years. This work focuses on the authentication process using a machine-learning (ML) based approach. This approach utilises the R-peak anchoring with an appropriate slicing window to obtain high-quality training and testing data for ML. The efficacy of the ML training and testing has been assessed through metrics and data analysis using a MATLAB toolbox with all suggested methods, metrics, and sampled data.
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