ECG Steganography Research Articles

A New ECG Steganography Based on Enhanced Coefficient Adjustment Technique and Least Significant Bit Substitution

A New ECG Steganography Based on Enhanced Coefficient Adjustment Technique and Least Significant Bit Substitution

Hybrid chaotic substitution and block-DCT-based ECG steganography with boosted embedding capacity

A novel idea of exploiting the chaotic sequences for substitution in the DCT coefficients of ECG signal according to the secret data is proposed in this work. The chaotic values are selected as per the decimal number generated from a bundle of secret bits. The selected chaotic values sensibly manipulates only the low- energy DCT coefficients, thus preservinge the diagnostic features of the ECG signal. The approach engenders unprecedented rise in the eEmbedding cCapacity (EC) as every substitution involves secret bits equal to the bundle size. The remarkable feature of the approach is that unlike existing steganography techniques, the perceptual quality of the stego-ECG is minimally affected with increase in the EC. The performance of the proposed technique is evaluated in terms of non-diagnostic distortion measures and clinically diagnostic error measures, while EC and bit error rate (BER) assess the steganography efficiency. The security analysis done in terms of key space verifies the robustness of the algorithm. The experimentation is done on the standard databases (both normal and abnormal) as well as on the self-recorded database. An average PRD, PSNR, WWPRD, WEDD and EC measured on all 48 records of MIT-BIH arrhythmia database of 5 min come out to be 2.51 × 10−3, 55.82, 0.088, 4.45 × 10−3 and 4.16, respectively marking the competency of the proposed technique in ECG steganography applications.

AFD and chaotic map‐based integrated approach for ECG compression, steganography and encryption in E‐healthcare paradigm

AFD and chaotic map‐based integrated approach for ECG compression, steganography and encryption in E‐healthcare paradigm

A new approach of ECG steganography and prediction using deep learning

In this paper, a new approach of ECG steganography of hiding patient’s confidential information is proposed. As steganography results in distortion within the ECG signal which hampers the clinical features, in this work, encryption was performed within TP-segment of ECG. Additionally, segment classification and feature extraction were used for data concealing within normal TP-segments, while keeping abnormal segments unaffected. To reduce the computational complexity and execution time, encryption was performed in time domain signal, using a new approach. Finally, after decryption of hidden data, to predict original sample values of modified TP-segments, a long short-term memory recurrent neural network (LSTM-RNN) was used which efficiently reduced the error between the original and predicted signal. This algorithm was successfully implemented on mitdb, ptbdb and European ST-T database, available in physionet and percent root mean square difference (PRD), PRD normalized (PRDN) were obtained less than 1% along with signal to noise ratio (SNR) and peak SNR (PSNR) more than 80 dB. It was observed that this algorithm provided better result among other frequency domain techniques and recently published works.

ECG steganography based on tunable Q‐factor wavelet transform and singular value decomposition

AbstractThe article presents a novel ECG steganography scheme based on the tunable Q‐factor wavelet transformation (TQWT) and also singular value decomposition (SVD) techniques that ensure better safety and confidentiality of patient information. Initial parameters such as Q, r, and J are used to decompose the cover signal into individual frequency sub‐bands with the tunable Q‐factor wavelet transform (TQWT). The singular value decomposition (SVD) technique is used to further decompose high‐frequency sub‐band coefficients into singular values. The watermark information is then embedded with high‐frequency sub‐band coefficients by involving the quantization process. The performance of this proposed system is successfully evaluated by considering various metrics, such as peak signal to noise ratio (PSNR), structural similarity index (SSIM), percentage residual difference (PRD), and bit error rate (BER). The simulation results of the proposed scheme are observed to be better than other traditional algorithms.

A morphologically robust chaotic map based approach to embed patient's confidential data securely in non-QRS regions of ECG signal.

In e-healthcare paradigm, the physiological signals along with patient's personal information need to be transmitted to remote healthcare centres. Before sharing this sensitive information over the unsecured channel, it is prerequisite to protect it from unauthorised access. The proposed method explores ECG signal as the cover signal to hide patient's personal information without disturbing its diagnostic features. Chaotic maps are used to randomly select the embedding locations in the non-QRS region while excluding the sensitive QRS region of ECG train. Optimum Location Selection algorithm has been designed to select the embedding locations in non-QRS embedding region. The proposed algorithm is thoroughly examined and the distortion is measured in terms of statistical parameters and clinical measures such as PRD, PRDN, PRD1024, PSNR, SNR, MSE, MAE, KL-Divergence, WWPRD and WEDD. The robustness of the algorithm is verified using the parameters such as key space and key sensitivity. The implementation has been extensively tested on all the 48 records of the standard MIT-BIH Arrhythmia database, abnormal databases [CU-VT, BIDMC-CHF and PTB (leads I, II and III)] and self-recorded data of 20 subjects. The algorithm yields average PRD, MSE, KL-Divergence, PSNR, WWPRD and WEDD of 4.7 × 10-3, 1.13 × 10-5, 1.29 × 10-5, 50.28, 0.15 and 0.04at an average maximum EC of 0.45(96876 bits) on MIT-BIH Arrhythmia database and 0.016, 3.38 × 10-5, 1.8 × 10-4, 46.03, 0.13 and 0.03 respectively at an average maximum EC of 0.47 (102571 bits) on self-recorded data which clearly reveals the competency of the proposed algorithm in comparison with the other state of the art ECG steganography approaches.

QR code based patient data protection in ECG steganography.

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.

An Enhanced Cryptography for ECG Steganography to Satisfy HIPAA Privacy and Security Regulation for Bio-Medical Datas

Introduction The number of elderly patients is increasing dramatically due to the recent medical advancements. Accordingly, to reduce the medical labor cost, the use of remote healthcare monitoring systems and Point-of-Care (PoC) technologies have become popular [1], [2]. Monitoring patients at t

Curvelets‐based ECG steganography for data security

Biomedical signals transmitted over the internet are usually tagged with patient information. Data hiding techniques such as steganography ensures the security of such data by hiding the data into signals. However, data hiding results in signal deterioration that might affect diagnosability. A novel technique which uses curvelet transforms to hide patient information into their ECG signal is presented. Curvelet transform decomposes the ECG signal into frequency sub-bands. A quantisation approach is used to embed patient data into coefficients whose values are around zero, in the high-frequency sub-band. Performance metrics provide the measure of watermark imperceptibility of the proposed approach. BER is used to measure the ability to extract patient data. The proposed approach is demonstrated on the MIT-BIH database and the observations validate that its performance is superior compared with the random locations approach. Although the performance of the proposed approach decreases as patient information size increases, the peak signal-to-noise ratio values are high. Therefore, the proposed approach can be used for the safe transfer of patient data.

Imperceptibility—Robustness tradeoff studies for ECG steganography using Continuous Ant Colony Optimization

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.

Wavelet based ECG Steganography for Protecting Patient Confidential Information

With the growing number of aging population and most of the patients are suffering from cardiac diseases, it is conceivable that remote ECG patient monitoring systems are expected to be widely used as Point-of-Care (PoC) applications in hospitals around the world. Therefore, huge amount of ECG signal collected by Body Sensor Networks (BSNs) from remote patients at homes will be transmitted along with other physiological readings such as blood pressure, temperature, glucose level etc. and are then diagnosed by remote patient monitoring systems. It is utterly important that patient confidentiality is protected while data is being transmitted over the public network as well as when they are stored in hospital servers used by remote monitoring systems. In this paper, a wavelet based steganography technique has been introduced which combines encryption and scrambling technique to protect patient confidential data. The proposed method allows ECG signal to hide its corresponding patient confidential data and other physiological information thus guaranteeing the integration between ECG and the rest. It is found that the proposed technique provides high security protection for patients data and ECG data remains diagnosable after watermarking (i.e. hiding patient confidential data) and as well as after watermarks (i.e. hidden data) are removed from the watermarked data.

ECG steganography using curvelet transform

Abstract 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.

Steganography in arrhythmic electrocardiogram signal.

Security and privacy of patient data is a vital requirement during exchange/storage of medical information over communication network. Steganography method hides patient data into a cover signal to prevent unauthenticated accesses during data transfer. This study evaluates the performance of ECG steganography to ensure secured transmission of patient data where an abnormal ECG signal is used as cover signal. The novelty of this work is to hide patient data into two dimensional matrix of an abnormal ECG signal using Discrete Wavelet Transform and Singular Value Decomposition based steganography method. A 2D ECG is constructed according to Tompkins QRS detection algorithm. The missed R peaks are computed using RR interval during 2D conversion. The abnormal ECG signals are obtained from the MIT-BIH arrhythmia database. Metrics such as Peak Signal to Noise Ratio, Percentage Residual Difference, Kullback-Leibler distance and Bit Error Rate are used to evaluate the performance of the proposed approach.

Discrete wavelet transform and singular value decomposition based ECG steganography for secured patient information transmission.

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.

Ecg Steganography Based Privacy Protection of Medical Datas for Telemedicine Application

Ecg Steganography Based Privacy Protection of Medical Datas for Telemedicine Application