Improved ECG Watermarking Technique Using Curvelet Transform.

Area of Science:

• Biomedical Engineering
• Digital Signal Processing
• Information Security

Background:

• Embedding patient data in electrocardiogram (ECG) signals is crucial for security and authenticity.
• Existing non-blind watermarking techniques for ECG data lack robustness against noise and attacks, showing poor performance metrics.
• Challenges include maintaining disease detection accuracy while ensuring data integrity.

Purpose of the Study:

• To propose an improved blind ECG-watermarking technique for secure and robust embedding of patient data.
• To enhance the performance of ECG watermarking against various attacks and improve data extraction accuracy.
• To validate the effectiveness of the proposed method using standard performance metrics.

Main Methods:

• A novel blind ECG-watermarking technique utilizing curvelet transform is developed.
• Euclidean distance is computed between curvelet coefficients for clustering.
• Data is embedded into selected clusters within the curvelet domain.

Main Results:

• The proposed technique demonstrates improved robustness against image-processing attacks and noise.
• Performance metrics including Structure Similarity Index Measure (SSIM), Normalized Correlation (NC), Peak Signal to Noise Ratio (PSNR), and Bit Error Rate (BER) show superiority.
• KL divergence and Percentage Residual Difference (PRD) confirm data hiding without significant disturbance to the original ECG signal.

Conclusions:

• The developed blind ECG-watermarking technique using curvelet transform and clustering offers enhanced security and robustness.
• The method effectively embeds large amounts of data while preserving the integrity of the ECG signal.
• This approach represents a significant improvement for secure patient data management in healthcare applications.