Wavelet-Based Watermarking and Compression for ECG Signals with Verification Evaluation

Sensors. 2014;14(2):3721-3736 DOI 10.3390/s140203721

 

Journal Homepage

Journal Title: Sensors

ISSN: 1424-8220 (Online)

Publisher: MDPI AG

LCC Subject Category: Technology: Chemical technology

Country of publisher: Switzerland

Language of fulltext: English

Full-text formats available: PDF, HTML, ePUB, XML

 

AUTHORS

Kuo-Kun Tseng (Department of Computer Science and Technology, Harbin Institute of Technology Shenzhen Graduate School, Shenzhen 518055, China)
Xialong He (Department of Computer Science and Technology, Harbin Institute of Technology Shenzhen Graduate School, Shenzhen 518055, China)
Woon-Man Kung (Department of Exercise and Health Promotion, College of Education, Chinese Culture University (CCU) and Department of Neurosurgery, Lo-Hsu Foundation, Lotung Poh-Ai Hospital, Luodong, Yilan 265, Taiwan)
Shuo-Tsung Chen (Department of Applied Mathematics, Tunghai University, Taichung 40704, Taiwan)
Minghong Liao (Department of Software Engineering, Xiamen University, Xiamen 361005, China)
Huang-Nan Huang (Department of Applied Mathematics, Tunghai University, Taichung 40704, Taiwan)

EDITORIAL INFORMATION

Blind peer review

Editorial Board

Instructions for authors

Time From Submission to Publication: 11 weeks

 

Abstract | Full Text

In the current open society and with the growth of human rights, people are more and more concerned about the privacy of their information and other important data. This study makes use of electrocardiography (ECG) data in order to protect individual information. An ECG signal can not only be used to analyze disease, but also to provide crucial biometric information for identification and authentication. In this study, we propose a new idea of integrating electrocardiogram watermarking and compression approach, which has never been researched before. ECG watermarking can ensure the confidentiality and reliability of a user’s data while reducing the amount of data. In the evaluation, we apply the embedding capacity, bit error rate (BER), signal-to-noise ratio (SNR), compression ratio (CR), and compressed-signal to noise ratio (CNR) methods to assess the proposed algorithm. After comprehensive evaluation the final results show that our algorithm is robust and feasible.