graduate student from 01.01.2024 until now
Sankt-Peterburg, St. Petersburg, Russian Federation
employee from 01.01.2020 to 01.01.2024
Saint-Petersbueg, St. Petersburg, Russian Federation
62
Today, we are increasingly looking to biometric authentication systems to protect our crucial information and resources. Our contribution in this paper is a robust and novel biometric authentication system for ECG signals that are augmented using the deep learning techniques. A comprehensive methodology including signal processing, feature extraction, wavelet decomposition, QRS wave detection, internal modeling, distance and deviation calculations, and averaging threshold along with an artificial neural network (ANN) classifier are used in the proposed system. The quality of ECG signals is improved using preprocessing, and ECG signals are acquired. The ECG waveform features unique characteristics which are then extracted, and the signal is decomposed in both time and frequency domains using wavelet decomposition. Using QRS wave detection, critical components for biometric authentication are identified. The system constructs an internal representation of ECG waves, calculates parameters such as distance and deviation, and refines the feature set to improve robustness. An averaging threshold is applied to enhance resilience to noise and variability. Finally, an ANN classifier, trained on the extracted features, performs the authentication. The system outputs the authentication result and the verified identity of the individual. Extensive testing was conducted using a well-known ECG dataset, achieving an accuracy of 98%, demonstrating the system's effectiveness. The True Positive Rate (sensitivity) was 95%, indicating strong performance in identifying authentic individuals. With a processing time of 10 seconds the system would be appropriate for use in real-time applications. ROC curve analysis also demonstrated excellent performance in discriminating authentic from non-authentic individuals with an Area Under the Curve (AUC) of 0.98. Securing, dependable and adaptable ECG based biometric authentication system is provided with the integration of complicated signal processing and deep learning to deal with real ECG pattern variations, that improves on past work. Although the system is highly sensitive and accurate future work will be directed towards improving selectivity and decreasing false positives to improve performance as a whole.
Deep learning, ECG Signal, Artificial Neural Network (ANN), Security, Biometric Authentication, Signal Processing, QRS Complex, Wavelet Decomposition
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