# Improved ECG Watermarking Technique Using Curvelet Transform
## 6 Experimental Results
### Kullback-Leibler divergence (KL):
Therefore, from Table 4 and Figure 7 demonstrate the robustness of the presented technique against image-processing attacks (Y-Axis). Some authors [14; 15; 16] have used the curvelet transform for embedding the information into the ECG signals as discussed above. In the adaptive threshold [15] and the quantization approach [16] authors proposed the steganography ECG using curvelet. In their study, they followed the steganography definition to make watermark invisible. Hence, these techniques first emphasized the imperceptibility of embedding information. Further improvement for the robustness of the watermark was still a critical parameter left in the previous work. Second, they used binary data as watermark. Binary data are very fragile because any mirror change may lose the data or change the meaning. High levels of false-positives were detected while using binary data as a watermark. To highlight the superiority of the proposed technique, HH scale DWT-SVD [12], adaptive threshold [15] and quantization approach [16] some latest ECG-watermarking techniques results were \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Operations**} & **Gaussian Noise** & **Salt \& Pepper** & **Rotation** & **Compression** & **Median** & **Cropping** \\  & **(0.01)** & **(0.01)** & **(5\({}^{\circ}\))** & **(5\%)** & **Filter (3 \(\times\) 3)** & **(5\%)** \\ \hline
**PSNR** & 43.6168 & 42.5698 & 41.49 & 38.2123 & 40.2738 & 32.1112 \\ \hline
**MSE** & \(2.10\times 10^{-3}\) & \(6.10\times 10^{-2}\) & 0.0421 & 0.0761 & 0.0213 & 0.1821 \\ \hline
**BER** & 0.321 & 0.3786 & 0.4001 & 03,986 & 0.4543 & 0.5743 \\ \hline
**NC** & 0.9992 & 0.9853 & 0.9798 & 0.9832 & 0.9783 & 0.9212 \\ \hline
**SSIM** & 0.9732 & 0.9653 & 0.9422 & 0.9489 & 0.9183 & 0.8833 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Quality of extracted watermark after different image-processing operations. Figure 7: Robustness of proposed techniques corresponding to different attacks. compared. In this technique, the author used clustering and some watermark-strength parameters to make the watermark invisible, as well as robust. The algorithm performance metrics like KL, PRD, MSE, etc., are being compared by existing ECG-watermarking techniques are shown in Table 5. If the \(67\times 67\) size of information was embedded into the ECG signals the value of PSNR value of the watermarked ECG was 78.0702 dB. Subsequently, the values of PSNR show the resemblance among the host ECG signal and watermarked ECG signal. As the value of PSNR achieved by the quantization approach was 73.75 dB, which can make a base when accompanied by the performance estimation. However, comparing the PSNR values of HH scale DWT-SVD and adaptive threshold techniques are simply 50.44 dB and 60.68 dB, respectively. Coefficients whose values are zero are exploited in the quantization approach. However, the embedding capacity of the quantization approach was very low; in this proposed technique further improvement to insert more watermarks bits with zero values coefficient may be achieved. To locate the watermark, an \(n\times n\) sequence was used in the adaptive threshold method. In this method, the author also compromised with the embedding capacity. The proposed technique was not restricted in terms of embedding capacity because the technique used the property of clustering to hide the watermark. The collection of coefficients depends on the size of the implanting watermark. Preferably, the coefficients with the smallest values are finest to insert the patient's information because they offers the best invisibility. To embed the information the text information was transformed into binary in HH scale DWT-SVD [12], adaptive threshold [15] and quantization approach [16] techniques. The whole information of any patient may be changed if there any false positive detection because the information was inserted into a binary form. However, in the projected technique image embedded into the ECG.