Versatile Machine Learning-Based Authentications by Using Enhanced Time-Sliced Electrocardiograms

This paper addresses the enhancement of modern security through the integration of electrocardiograms (ECGs) into biometric authentication systems. As technology advances, the demand for reliable identity authentication systems has grown, given the rise in breaches associated with traditional techniques that rely on unique biological and behavioral traits. These techniques are emerging as more reliable alternatives. Among the biological features used for authentication, ECGs offer unique advantages, including resistance to forgery, real-time detection, and continuous identification ability. A key contribution of this work is the introduction of a variant of the ECG time-slicing technique that outperforms existing ECG-based authentication methods. By leveraging machine learning algorithms and tailor-made compact data learning techniques, this research presents a more robust, reliable biometric authentication system. The findings could lead to significant advancements in network information security, with potential applications across various internet and mobile services.