Innovative Electrocardiogram Authentication System by Using Tailor-Made Compact Data Learning

Conventional authentication systems often rely on alphanumeric or graphical passwords, or token-based methods. The disadvantages of these systems include the risk of forgetfulness, loss, and theft. Biometric authentication which is a solution to these issues is quickly taking the place of traditional methods and becoming a ubiquitous part of daily life. The electrocardiogram (ECG) is one of the most recent traits considered for biometric purposes. A notable contribution of this work is the introduction of a novel ECG time-slicing technique that outperforms other ECG-based methods. By leveraging machine learning algorithms and tailor-made compact data learning techniques, this research presents a more robust, reliable biometric authentication system. Upon evaluation, the proposed system showed up to 95% identification accuracy when using the optimal machine learning model. These findings could lead to substantial advancements in network information security, with potential applications across various internet and mobile services.