Rhythmic Recognition: Harnessing Deep Learning for Cutting-Edge ECG Biometric Authentication

Abstract

Machine learning (ML) and deep learning (DL) are essential for analysing physiological signals, such as electrocardiograms (ECG), which can be continuously collected without requiring specific user engagement. Deep and machine learning demonstrate the ability to process large volumes of complex data with minimal human intervention, thereby contributing to their popularity in the health care sector. Electrocardiograms (ECG) represent a contemporary method of biometric verification closely linked to the distinctive characteristics of an individual's heartbeats. This study presents a convolutional neural network (CNN) model aimed at enhancing the efficiency and usability of user authentication processes. The proposed model demonstrates high accuracy and has undergone pretraining for classification across various disciplines. This was utilized for the authentication procedure following specific structural modifications. The proposed model generates an output suitable for patient authentication that is subsequently stored in a database. The model generates the patient’s output upon the registration of a new patient. This output was subsequently utilized to compute the Euclidean Distance between the current patient's output and previous outputs stored in the database, resulting in a similarity score. Convolutional Neural Networks (CNNs) have been employed to design classification models that determine the electrocardiogram (ECG) corresponding to a specific individual based on a combination of feature sets produced during the transformation phase. The results indicate that, when adequate data are available for training, deep neural networks (DNNs) can achieve greater accuracy than many alternative methods.