Compared to other biometrics, electrocardiograms (ECGs) have gained widespread acceptability as mediums for validating animateness in numerous security applications, especially in new and emerging technologies. Our study utilizes this important trait to advance available machine and deep learning ECG authentication systems by leveraging the use of edge computing servers that offer connection to Internet of Things (IoT) devices while maintaining access to computational and storage resources. Specifically, in our proposed technique, the preprocessing, feature extraction and classification routines are combined into one unit, while individual ECG signals from the database are directly fed into a convolutional neural network (CNN) model and subsequently classified as an accepted or unaccepted (i.e., rejected) class. Additionally, we tailor our authentication system as a cost-efficient one focused on reducing latency, which makes it ideal for applications on edge computing platforms. To validate our proposed model, we applied it on standard ECG signals from the Physikalisch-Technische Bundesanstalt (PTB) database where outcomes of 99.50%, 99.73%, 100%, and 99.78%, respectively that are reported for accuracy, precision, recall, and F1-score indicate the tenability of deploying our technique in real-time authentication systems. Furthermore, we present discussions regarding the performance of the model relative to recent techniques that are built on traditional machine and deep learning techniques.

Deep Learning, Biometric Authentication, ECG, Information Security, IoT, Edge Computing, Industrial Data Integration