HGSmark: An efficient ECG watermarking scheme using hunger games search and Bayesian regularization BPNN

Abstract

The extensive use of telemedicine has led to an exponential increase in the exchange of medical data via insecure networks. For example, diagnosis in telemedicine practice often requires electrocardiograms to be shared via Internet. This raises security and authenticity concerns. In this paper, we have proposed HGSmark—an ECG signal watermarking scheme based on multiple embedding strength (MES). We have used the hunger games search (HGS) algorithm to optimize the MES values locally, while maintaining the imperceptibility-robustness trade-off. However, as the optimization using metaheuristic techniques is time-intensive, we have used HGS only once to train a Bayesian regularization-backpropagation neural network (BR-BPNN) on a set of ECG signals. Subsequently, the trained BR-BPNN is employed to quickly compute the optimum MES values. For watermarking, we have employed a wrapping-based fast discrete curvelet transform (FDCT) and singular value decomposition (SVD) to extract the principal component (PC) coefficients of the ECG signal, and embedded a watermark into PC coefficients using the MES values. We have used PC coefficients for watermarking in order to avoid the problem of false-positive. Further, to enhance security, we have encoded the watermark using the logistic chaotic map. We evaluated the performance of HGSmark on MIT-BIH normal sinus rhythm and MIT-BIH arrhythmia databases. The experimental results show that HGSmark achieved average PSNR and PRD values of 57.725 dB and 0.271, respectively and is robust to common interferences. Moreover, the comparison with the state-of-the-art ECG signal watermarking schemes establishes that HGSmark is superior to others in terms of imperceptibility and robustness.