From ECG to identity recognition: a scalable, image-based approach to biometric authentication

Biometric identification based on the electrocardiogram (ECG) is gaining attention as a secure and reliable approach to healthcare authentication, employing the unique physiological patterns detected in the ECG signal. Traditional approaches often depend on raw waveform analysis or the extraction of fiducial points, both of which are computationally intensive and challenging to implement in real-time systems. This work presents CardioIdNet, a lightweight convolutional neural network designed to perform biometric identification directly from ECG images, eliminating the need for complex signal preprocessing steps. ECG recordings from 21 subjects in the MIT-BIH arrhythmia database were segmented and converted to grayscale waveform plots, generating a comprehensive well-suited dataset for image-based deep learning classification. The CardioIdNet architecture consists of convolutional and pooling layers for hierarchical feature extraction, followed by fully connected layers for subject classification. Training was carried out using sparse categorical cross-entropy and the Adam optimizer. The dataset was split 80/20 for training and testing, and early stopping was applied to prevent overfitting and improve generalization. The results show that CardioIdNet achieves excellent performance, with accuracy of 99%, precision, recall, and F1-score of 98.18%, an AUC of 99%, and a false negative rate of 1.85%. CardioIdNet suggests to be a promising solution for biometric authentication in healthcare real-time settings, offering a balance of simplicity, interpretability, and efficiency through image-based deep learning.