Key result
An improved blind ECG-watermarking technique using curvelet transform provided robust data hiding against image-processing attacks without disturbing the original signal.
Why the study?
Existing non-blind ECG watermarking techniques are not robust against noise attacks and show low performance across standard signal quality and error metrics.
The proposed curvelet transform-based ECG watermarking technique improves data security and robustness against noise without disturbing the original signal.
May enhance ECG data security in transmission; leaves open clinical validation before any practice adoption.
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.
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Goyal et al. (2020) studied this question. Improved blind ECG-watermarking technique using curvelet transform vs. Non-blind watermarking techniques was evaluated on Performance metrics (SSIM, NC, PSNR, BER, KL divergence, PRD). An improved blind ECG-watermarking technique using curvelet transform provided robust data hiding against image-processing attacks without disturbing the original signal.
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