Key result
A novel reversible watermarking architecture using DnCNN for ECG signals achieved a PSNR value of 83.48 dB and an NC score of 0.9991, surpassing current methodologies in robustness and signal quality.
Why the study?
ECG signals are vulnerable to tampering, forgery, and unauthorized access during digital transmission, while current watermarking methodologies suffer from inadequate robustness and signal deterioration.
The proposed DnCNN-assisted watermarking architecture provides highly secure and robust ECG data transmission for telemedicine applications while preserving clinical signal quality.
May enable secure ECG telemedicine; leaves open prospective clinical validation before adoption.
ECG signals are vulnerable to tampering, forgery, and unauthorized access due to their digital transmission and lack of built-in security. Digital watermarking helps protect ECG data by embedding hidden information for authentication and tamper detection without affecting its clinical quality. This study presents a novel reversible watermarking architecture utilizing ECG signals for secure data transfer of telemedicine, tackling issues such as inadequate robustness and signal deterioration found in current methodologies. Pan-Tompkins++ transforms the 1D ECG into 2D extracted features, RDWT, and MSVD, and encrypted watermarks are hidden in the transformed components. DnCNN denoises the retrieved watermarks, offering robustness against common attacks while preserving signal quality. Experimental findings indicate a notable improvement, with a PSNR value of 83.48 dB and NC score of 0.9991, surpassing current methodologies, with enhanced resilience to prevalent attacks including Gaussian noise, speckle noise, and JPEG compression.
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Shivendra Shivani (2025) studied ECG signal watermarking for secure transmission. Reversible watermarking architecture utilizing Pan-Tompkins++, RDWT, MSVD, and DnCNN vs. Current methodologies was evaluated on PSNR value and NC score for robustness and signal quality. A novel reversible watermarking architecture using DnCNN for ECG signals achieved a PSNR value of 83.48 dB and an NC score of 0.9991, surpassing current methodologies in robustness and signal quality.
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