Improved ECG Watermarking Technique Using Curvelet Transform.

Lalit Mohan Goyal,Mamta Mittal,Amit Verma,Iqbaldeep Kaur,Sudipta Roy,Ranjeeta Kaushik,Tai-Hoon Kim

doi:10.3390/s20102941

Abstract

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.

Highlights

The health of a patient can be constantly be monitored with the help of medical devices and digital communication
A curvelet transform-based ECG-watermarking approach using Euclidean and non-similarity distance clustering technique was presented and a 1D-ECG was converted into a 2D-ECG image by preserving the QRS complex
A Euclidean, non-similarity distance method was incorporated to make the clusters of the coefficients

Summary

Introduction

The health of a patient can be constantly be monitored with the help of medical devices and digital communication. The old patients can send their physiological signals to the hospitals to avoid a repeated visit to the hospital. This technology has some concern about the security and content of the information. If the patients broadcast their medical or private information on the Internet in that case security is an important issue. The privacy of the patient can be leaked and affects the ability to diagnose due to unauthorized access. United States was passed an Act in 1996 as the Health Insurance Portability and Accountability Act (HIPAA) to protect patient privacy (personal data)

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Conclusion