ECG Steganography provides secured transmission of secret information such as patient personal information through ECG signals. This paper proposes an approach that uses discrete wavelet transform to decompose signals and singular value decomposition (SVD) to embed the secret information into the decomposed ECG signal. The novelty of the proposed method is to embed the watermark using SVD into the two dimensional (2D) ECG image. The embedding of secret information in a selected sub band of the decomposed ECG is achieved by replacing the singular values of the decomposed cover image by the singular values of the secret data. The performance assessment of the proposed approach allows understanding the suitable sub-band to hide secret data and the signal degradation that will affect diagnosability. Performance is measured using metrics like Kullback---Leibler divergence (KL), percentage residual difference (PRD), peak signal to noise ratio (PSNR) and bit error rate (BER). A dynamic location selection approach for embedding the singular values is also discussed. The proposed approach is demonstrated on a MIT-BIH database and the observations validate that HH is the ideal sub-band to hide data. It is also observed that the signal degradation (less than 0.6 %) is very less in the proposed approach even with the secret data being as large as the sub band size. So, it does not affect the diagnosability and is reliable to transmit patient information.

Discrete Wavelet Transform and Singular Value Decomposition Based ECG Steganography for Secured Patient Information Transmission

Watermarking is the most widely used technology in the field of copyright and biological information protection. In this paper, we use quantization based digital watermark encryption technology on the Electrocardiogram (ECG) to protect patient rights and information. Three transform domains, DWT, DCT, and DFT are adopted to implement the quantization based watermarking technique. Although the watermark embedding process is not invertible, the change of the PQRST complexes and amplitude of the ECG signal is very small and so the watermarked data can meet the requirements of physiological diagnostics. In addition, the hidden information can be extracted without knowledge of the original ECG data. In other words, the proposed watermarking scheme is blind. Experimental results verify the efficiency of the proposed scheme.

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

In mobile health care monitoring, compression is an essential tool for solving storage and transmission problems. The important issue is able to recover the original signal from the compressed signal. The main purpose of this paper is compressing the ECG signal with no loss of essential data and also encrypting the signal to keep it confidential from everyone, except for physicians. In this paper, mobile processors are used and there is no need for any computers to serve this purpose. After initial preprocessing such as removal of the baseline noise, Gaussian noise, peak detection and determination of heart rate, the ECG signal is compressed. In compression stage, after 3 steps of wavelet transform (db04), thresholding techniques are used. Then, Huffman coding with chaos for compression and encryption of the ECG signal are used. The compression rates of proposed algorithm is 97.72 %. Then, the ECG signals are sent to a telemedicine center to acquire specialist diagnosis by TCP/IP protocol.

Compression and Encryption of ECG Signal Using Wavelet and Chaotically Huffman Code in Telemedicine Application

A study on ECG signal characterization and practical implementation of some ECG characterization techniques

Recently, by means of technological innovation in communication networks and information, it has assisted healthcare experts across the world to seek high-quality diagnosis as well as to communicate each other as second opinions via enabling extensive and faster access to the patients’ electronic medical records, such as medical images. Medical images are extremely precious owing to its importance in diagnosis, education, and research. Recently, telemedicine applications in telediagnoisis, teleconsulting, telesurgery, and remote medical education play an imperative role in the advancement of the healthcare industry. Nevertheless, medical images are endured security risk, such as images tampering to comprise false data which may direct to wrong diagnosis and treatment. Consequently, watermarking of medical images offers the compulsory control over the flow of medical information. It is the typically used data hiding technique in the biomedical information security domain and legal authentication. In the field of telemedicine, exchange of medical signals is a very common practice. The signals are transmitted through the web and the wireless unguided media. The security and the authenticity are the matter of concern due to the various attacks on the web. Any type of the signals vulnerability in the biomedical data is not acceptable for the sake of proper diagnosis. A watermark is used to prove the ownership of the exchanged data. The logos of the hospitals or medical centers and electronic patient’s report card can be added to the biomedical signals as a watermark to establish the property right. This work provides an extensive view about the existing research works in the field of watermarking techniques on different biomedical signals. It includes the design and evaluation parameters serving as a guideline in the watermarking schemes′ development and benchmarking. This work also provides the comparative study between different watermarking methods. It reviews several aspects about digital watermarking in the medical domain. Also, it presented the properties of watermarking and several applications of watermarking. Meanwhile, it discusses the requirements and challenges that the biomedical watermarking process face.

Watermarking in Biomedical Signal Processing

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.

https://www.mdpi.com/1424-8220/14/2/3721/pdf

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

In this article, we use a self-synchronized watermark technology [7], to achieve the purpose of protection of electrocardiogram (ECG) signal. A Harr wavelet transform with 7 levels decomposition is adopted to transform the ECG signal and the synchronization code, combined with watermark, are quantized embedded in the low-frequency sub-band of level 7. The signal to noise ratio (SNR) between the embedded ECG and original one is greater than 30 such that the difference between these two ECG signals is very small and negligible in general. To test the robustness under the network transfer of ECG data, a white noise attack with various strengths is simulated that the bit error rate is quite small unless the SNR of the noise is very large. This study confirms the use of wavelet-based quantization watermarking scheme on ECG signal for patient protection is adequate.

Xialong He

Papers

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

Wavelet-Based Quantization Watermarking for ECG Signals