ECG authentication system design incorporating a convolutional neural network and generalized S-Transformation

Electrocardiogram (ECG) is gaining increased attention as a biometric method in a wide range of applications, such as access control and security/privacy requirements. The majority of reported investigations using the ECG biometric method are usually based on fiducial or nonfiducial methods, which are always accompanied by a series of issues, such as locating fiducial points accurately is difficult, feature selection is subjective, and classifiers are limited by the quantity and structure of data. This paper proposes a new biometric authentication system for human identification that uses ECG signals as a biometric trait and integrates a generalized S-transformation and a convolutional neural network (CNN). Specifically, we first introduce a blind segmentation strategy that effectively avoids difficult data-specific heartbeat recognition and segmentation techniques. Then, a generalized S-transformation is performed on the blind signal-processed ECG signal, capturing the ECG trajectory at each time point in the frequency domain. Next, the getframe technology is used to capture an image of the ECG trajectories and convert the one-dimensional signal to a two-dimensional image, which serves as the input layer of the CNN, thus fully reflecting the changing trend in the ECG signal spectrum characteristics over a continuous period. Finally, the CNN is used for automatic discriminative feature learning and representations, which avoids a tedious feature extraction algorithm. In addition, considering the possible impact of ECG signals with different signal behaviors on identification, experiments are performed on three ECG databases with diverse features, comprising normal individuals, atrial fibrillation patients, and a noisy database, to evaluate the effectiveness of the proposed algorithm. Promising identification rates of 99%, 98%, and 99% were achieved, respectively. Thus, our proposed ECG authentication system can be effectively used for identity recognition under various conditions.