A novel two-dimensional ECG feature extraction and classification algorithm based on convolution neural network for human authentication

A biometrics-based authentication system is usually a better security solution than traditional systems which are heavily reliant on passwords, personal identification numbers or smart cards. Electrocardiogram (ECG) is one of the most promising approaches for biometrics-based authentication in recent years, because, unlike other biometrics, it assures aliveness of the person being authenticated. In this paper, we present a novel authentication system using an efficient feature detection algorithm and a convolutional neural network (CNN) based on ECG for human authentication. Our system processes ECG signals through two main phases: a feature detection phase and an authentication phase. In the feature detection phase, preprocessing was performed first to remove as much noise as possible and straighten the ECG signals. Then, the proposed scanning and removing methods are used to extract the main features from the signals that have negative peaks, high-grade noise and baseline drifts with higher accuracy than existing algorithms. In the authentication phase, we proposed a 12-layer CNN to authenticate the ECG signals. We also introduced a new database (MWM-HIT database), which is suitable for training and validating authentication systems. In addition, we used all records from the PTB, CYBHi, and MIT-BIH arrhythmia databases for comparison between the proposed system and other systems. We achieved an average accuracy, sensitivity and positive predictivity of 97.92%, 96.96% and 98.79%, respectively for detecting all peaks on all records from the collected database and an accuracy of 99.96%, sensitivity of 99.99% and positive predictivity of 99.98% for detecting all peaks on the MIT-BIH database for ten seconds of lead II ECG signals. The proposed model is able to authenticate the ECG signals with an equal error rate (EER) of 1.63%, 4.47%, and 4.86% when using lead II from PTB, CYBHi and the collected databases, respectively. The proposed system is highly usable in a real-time authentication system.