E-healthcare systems are rapidly gaining importance with the evolution of remote monitoring devices for patient diagnosis. While transmitting patient classified information, it is essential to ensure that patient confidentiality is maintained. ECG signals are used as a medium to mask the patient's confidential data. ECG steganography is achieved by using Discrete Wavelet Transform along with Singular Value Decomposition. To assure that minimal distortion is reported after embedding the data, two distortion metrics namely, Percentage Residual Difference and Bit Error Rate are used. The proposal is implemented on MIT-BIH database and it has been observed that the proposed approach offers minimum distortion hence retaining the authenticity of the original signal. © 2016 ACM.

Geetha S

Computer Science

School of Computer Science and Engineering

Chennai Campus

Meghani D

Vellore Institute of Technology (VIT) is a private university located in&nbsp;Tamil Nadu, India. Founded in 1984, as Vellore Engineering College, the institution offers 20 undergraduate, 34 postgraduate, four integrated and four research programs. It has campuses in Vellore, Amravati, Bhopal and Chennai.

VIT is one of the top ranked private universities in India according to NIRF, THE and QS Rankings.&nbsp;Govt. of India has recognized&nbsp;VIT, Vellore as an&nbsp;Institution of Eminence. This has allowed VIT to take independent quality initiatives and move up in world ranking.

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VIT University

ECG Steganography to secure patient data in an E-Healthcare System

Proceedings of the ACM Symposium on Women in Research 2016 - WIR '16

ECG steganography allows secured transmission of patient data that are tagged to the ECG signals. Signal deterioration leading to loss of diagnosis information and inability to retrieve patient data fully are the major challenges with ECG steganography. In this work, an attempt has been made to use curvelet transforms which permit identifying the coefficients that store the crucial information about diagnosis. The novelty of the proposed approach is the usage of curvelet transform for ECG steganography, adaptive selection of watermark location and a new threshold selection algorithm. It is observed that when coefficients around zero are modified to embed the watermark, the signal deterioration is the least. In order to avoid overlap of watermark, an n × n sequence is used to embed the watermark. The imperceptibility of the watermark is measured using metrics such as Peak Signal to Noise Ratio, Percentage Residual Difference and Kullback-Leibler distance. The ability to extract the patient data is measured by the Bit Error Rate. Performance of the proposed approach is demonstrated on the MIT-BIH database and the results validate that coefficients around zero are ideal for watermarking to minimize deterioration and there is no loss in the data retrieved. For an increased patient data size, the cover signal deteriorates but the Bit Error Rate is zero. Therefore the proposed approach does not affect diagnosability and allows reliable steganography. © 2015 Elsevier Ltd. All rights reserved.

Biomedical Signal Processing and Control

ECG steganography using curvelet transform

Expert Systems with Applications

Optimized gray-scale image watermarking using DWT–SVD and Firefly Algorithm

Journal of Medical Systems

Hiding Patients Confidential Datainthe ECG Signal viaa Transform-Domain Quantization Scheme

IEEE Transactions on Biomedical Engineering

Wavelet-Based ECG Steganography for Protecting Patient Confidential Information in Point-of-Care Systems

2009 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT)

Reversible blind watermarking for medical images based on wavelet histogram shifting

Journal	Data powered by TypesetProceedings of the ACM Symposium on Women in Research 2016 - WIR '16
Publisher	Data powered by TypesetACM Press
Open Access	0