Palaniappan Ramu

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.

Connected health enables patient centric interventions resulting in better healthcare and hence better living. In order to accomplish this, bio-signals, medical and diagnosis information are shared and accessed by multiple actors and it is important to protect the privacy of patient data. Steganography is widely used to protect patient data by hiding it in the medical information. Current work investigates ECG steganography using Discrete Wavelet Transform (DWT) and Quick Response (QR) code. Steganography deteriorates the ECG signal and it is important to minimize this deterioration to preserve diagnosability. 1D ECG signal is converted to 2D ECG image and decomposed into sub-bands by subjecting it to DWT. The novelty of the proposed approach lies in converting the patient data into QR code and using it as watermark in ECG steganography. The QR code is embedded in the 2D image using additive quantization scheme. The performance of proposed method is measured using Peak Signal to Noise Ratio, Percentage Residual Difference and Kullback–Leibler distance. These metrics are used as a measure of imperceptibility while the data loss during retrieval is measured by Bit Retrieval Rate. The proposed method is demonstrated on normal ECG signals obtained from MIT-BIH database for different QR code versions. Metrics reveal that imperceptibility decreased for increasing patient data size and increasing scaling factors. Metrics were independent of the sub-band and the proposed method allows reliable patient data protection with full retrieval ability.

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.

ECG steganography hides patient data inside their ECG signal to ensure the protection of patient's identity. In this work, an attempt has been made to evaluate ECG Steganography where Quantization Index Modulation (QIM) method is used to embed patient data into the Contourlet transform coefficients of ECG image. Tompkins QRS detection algorithm is used to construct ECG image and which is decomposed into a series of multiscale, local and directional image expansion using contour segments. QIM scheme is applied on the appropriate coefficients of the selected frequency sub-bands. QIM divides the selected frequency sub-bands into non overlapping blocks of matrix size 2×2. Two quantizers are used to embed binary watermark 0 and 1. The reverse Contourlet transform provides the watermarked ECG image from which 1D stego-ECG signal is re-ordered. The proposed scheme retrieves the watermark without the need of cover image. The deterioration due to the modified coefficients are reduced using adaptive selection of a coefficient to hide watermark bit. The efficiency of ECG steganography is measured using imperceptibility of watermark and Bit Error Rate of the retrieved watermark. Similarity metrics such as Peak Signal to Noise ratio, Percentage Residual Difference and Kullback-Leibler distance are used to estimate the imperceptibility of watermark on the stego-ECG signal. It is demonstrated that the proposed approach provides higher imperceptibility and less Bit Error Rate in the retrieved watermark, which is closed to zero. ECG signals obtained from MITBIH normal sinus rhythm data base are used to evaluate the performance of the proposed ECG Steganography approach.

ECG Steganography ensures protection of patient data when ECG signals embedded with patient data are transmitted over the internet. Steganography algorithms strive to recover the embedded patient data entirely and to minimize the deterioration in the cover signal caused by the embedding. This paper presents a Continuous Ant Colony Optimization (CACO) based ECG Steganography scheme using Discrete Wavelet Transform and Singular Value Decomposition. Quantization techniques allow embedding the patient data into the ECG signal. The scaling factor in the quantization techniques governs the tradeoff between imperceptibility and robustness. The novelty of the proposed approach is to use CACO in ECG Steganography, to identify Multiple Scaling Factors (MSFs) that will provide a better tradeoff compared to uniform Single Scaling Factor (SSF). The optimal MSFs significantly improve the performance of ECG steganography which is measured by metrics such as Peak Signal to Noise Ratio, Percentage Residual Difference, Kullback-Leibler distance and Bit Error Rate. Performance of the proposed approach is demonstrated on the MIT-BIH database and the results validate that the tradeoff curve obtained through MSFs is better than the tradeoff curve obtained for any SSF. The results also advocate appropriate SSFs for target imperceptibility or robustness.

ECG steganography hides patient's confidential data into their Electrocardiogram during transmission/storage of medical records, in order to ensure protection of patient privacy. Efficiency of a steganography method can be estimated using imperceptibility and robustness of hidden data which are estimated using Peak Signal to Noise Ratio and Bit Error Rate metrics, respectively. In this research work, an attempt has been made to perform ECG steganography using Discrete Wavelet Transform - Singular Value Decomposition method with additive quantization scheme. A vital challenge in steganography is that affects the imperceptibility and robustness of watermark. The novelty of proposed work is that improves the robustness of hidden data using BCH error-correcting codes. MIT-BIH database is used to evaluate the performance of the proposed ECG steganography method. It is observed that the robustness of hidden data is improved owing to BCH codes.