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Improved ECG Watermarking Technique Using Curvelet Transform

Department of Computer Engineering, J.C. Bose University of Sc. & Technology, YMCA, Faridaba 121006, India
Department of Computer Science & Engineering, G.B. Pant Govt. Engineering College, New Delhi 110020, India
Department of Computer Science & Engineering, Chandigarh Group of Colleges, Mohali 140307, India
PRTTL, Washington University in Saint Louis, Saint Louis, MO 63110, USA
Computer Sc. Department, School of Economics and Management, Beijing Jiaotong University, Beijing 100044, China
Author to whom correspondence should be addressed.
Sensors 2020, 20(10), 2941;
Received: 8 April 2020 / Revised: 16 May 2020 / Accepted: 20 May 2020 / Published: 22 May 2020
(This article belongs to the Section Biomedical Sensors)
Hiding data in electrocardiogram signals are a big challenge due to the embedded information that can hamper the accuracy of disease detection. On the other hand, hiding data into ECG signals provides more security for, and authenticity of, the patient’s data. Some recent studies used non-blind watermarking techniques to embed patient information and data of a patient into ECG signals. However, these techniques are not robust against attacks with noise and show a low performance in terms of parameters such as peak signal to noise ratio (PSNR), normalized correlation (NC), mean square error (MSE), percentage residual difference (PRD), bit error rate (BER), structure similarity index measure (SSIM). In this study, an improved blind ECG-watermarking technique is proposed to embed the information of the patient’s data into the ECG signals using curvelet transform. The Euclidean distance between every two curvelet coefficients was computed to cluster the curvelet coefficients and after this, data were embedded into the selected clusters. This was an improvement not only in terms of extracting a hidden message from the watermarked ECG signals, but also robust against image-processing attacks. Performance metrics of SSIM, NC, PSNR and BER were used to measure the superiority of presented work. KL divergence and PRD were also used to reveal data hiding in curvelet coefficients of ECG without disturbing the original signal. The simulation results also demonstrated that the clustering method in the curvelet domain provided the best performance—even when the hidden messages were large size. View Full-Text
Keywords: ECG; steganography; curvelet transform; clustering; performance metric ECG; steganography; curvelet transform; clustering; performance metric
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Goyal, L.M.; Mittal, M.; Kaushik, R.; Verma, A.; Kaur, I.; Roy, S.; Kim, T.-H. Improved ECG Watermarking Technique Using Curvelet Transform. Sensors 2020, 20, 2941.

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