# Improved ECG Watermarking Technique Using Curvelet Transform
## 6 Experimental Results
### Kullback-Leibler divergence (KL):
In Table 2 the performance measures metric's KL, MSE, PRD and BER confirm the superiority of the discussed technique. Table 2 verified the similarity of the original ECG signal and watermarked ECG signal with reference to PSNR (which was above 65). Further, the values of NC and SSIM are one or near to one. Figure 4: QRS detection (**a**) pulse train of the QRS on ECG signal (**b**) QRS on filtered signal (**c**) QRS on signal and noise level (black), signal level (red) and adaptive threshold (green). Figure 5: Watermarked ECG. The quality of extracted information was furthermore compared with the original information. Figure 6 demonstrates the extracted patient information and whereas the performance of the extracted information was being compared by the embedded one. The values of PSNR, i.e., 65.31, NC = 1, SSIM = 0.991 and MSE = 1.864 \(\times\) 10\({}^{4}\) verified the quality of extracted information was similar to embedded one. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Cluster** & **0–1** & **0, 1** & **0, 1, 2** & **0, 1, 2, 3** & **0–7** \\ \hline
**Watermark Size** & \(32\times 32\) & \(64\times 64\) & \(80\times 80\) & \(100\times 100\) & \(128\times 128\) \\
**PSNR** & 84.5131 & 79.7489 & 83.6277 & 74.2855 & 66.8254 \\
**NC** & 1 & 1 & 1 & 1 & 1 \\ KL & \(2.00\times 10^{-5}\) & \(2.15\times 10^{-5}\) & 0.0001 & 0.0007 & 0.0012 \\ MSE & \(7.67\times 10^{-5}\) & \(7.26\times 10^{-5}\) & 0.0001 & 0.0005 & 0.0011 \\ PRD & 0.0421 & 0.0545 & 0.0494 & 0.0674 & 0.0967 \\ BER & 0 & 0 & 0 & 0.011 & 0.214 \\ SSIM & 1 & 1 & 1 & 1 & 1 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Performance of proposed techniques with different watermark sizes and clusters. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Sr. No. ** & **ECG Signal** & **PSNR** & **NC** & **KL** & **MSE** & **PRD** & **BER** & **SSIM** \\ \hline
1 & 0.0000 & 0.0000 & 0.0000 & 0.0000 & 0.0000 & 0.0000 & 0.0000 \\
2 & 0.0000 & 0.0000 & 0.0000 & 0.0000 & 0.0000 & 0.0000 & 0.0000 \\
3 & 0.0000 & 0.0000 & 0. Table 3 shows the quality of the extracted patient's information. The text information of patients was converted into an image and embedded into the curvelet transform by using clustering. Table 3 shows the visual quality of the patient's information. The cluster was 0-7 and the size of the embedded information was \(128\times 128\). The quality of the extracted information was analyzed by PSNR, NC and SSIM metrics. The values of PSNR verify the visual quality of the extracted information was above 64. The values of NC are one for all ECG signals and values of SSIM are near to one that shows the similarity between the embedded and extracted images. \begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Sr. No. ** & **ECG Signal** & **Extracted Watermark** & **PSNR** & **NC** & **SSIM** \\ \hline \hline \multirow{6}{*}{1} & \multirow{6}{*}{0.911} & **Patient Confidential information** & & & \\  & & Name: XXXXXXX & & & \\  & & & Av: 12 & & \\  & & & Adder: City YYYY & & & \\  & & & Pac Paci: & 22222 & & \\  & & & Paci: & 22222  
The implanted information may be affected by image-processing operations like rotation, filtering, cropping, compression, etc. To demonstrate the superiority against the image-processing operation, the technique also extracted the information from the noised, filtered, rotated, compressed and cropped ECG signals. Table 3 shows the values of the PSNR and MSE after compared the extracted and embedded information from distorted ECG. Table 4 verified the robustness of the technique because the extracted watermark was very similar to the original one. Figure 7 shows the values of BER, NC and SSIM of extracted patient images and embedded images under different image-processing attacks. The values of BER was less than 0.5 in all cases even the values of NC and SSIM are near to one. The values of BER, SSIM and NS confirm the resistance of the image-processing attacks on the extracted watermark.