A reversible and multipurpose ECG data hiding technique for telemedicine applications

Bhalerao, Siddharth; Ansari, Irshad Ahmad; Kumar, Anil; Jain, Deepak Kumar

doi:10.1016/j.patrec.2019.06.004

Cited by 48 publications

(12 citation statements)

References 21 publications

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“…Araghi et al 46,49 have proposed an image steganography technique using DWT and 2D SVD that has achieved an average PSNR value of 46 dB with variable payload capacity is found to be better than Siddharth et al 48 and Mathivanan et al 45 Mathivanan et al 47 have proposed an ECG steganography using DWT and pixel swapping achieved a PSNR value of 45.63 dB with a payload efficiency of 21 k‐bits which is observed to be better than Siddharth et al 48 and Mathivanan et al 45 The proposed scheme described in this study considers the payload capacity of 64 k‐bits, and the obtained results are found to be better than Siddharth et al 48 and Mathivanan et al 45 Moreover, the imperceptibility of the proposed scheme is found to be good in comparison with Yang and Wang, 28 Siddharth et al, 48 Mathivanan et al, 47 Patil et al 50 and Mathivanan et al, 45 and is replicated in the PSNR value of 59.64 dB. Mathivanan et al and Yang and Wang 28 PSNR values are 51.4 dB and 56.34 dB which are found to be better than Siddharth et al, 48 Mathivanan et al 47 and Patil et al 50 However, it considers a very low payload of 229 bits and 7500 bits compared to other traditional algorithms as shown in Table 4.…”

Section: Resultsmentioning

confidence: 99%

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

Mathivanan

Ganesh

2020

Int J Imaging Syst Tech

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The 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.

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Section: Resultsmentioning

confidence: 99%

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

Mathivanan

Ganesh

2020

Int J Imaging Syst Tech

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“…PEE was also used for reversible data hiding of medical signals. In [14, 15 ], it was used for ECG watermarking.…”

Section: Methodsmentioning

confidence: 99%

Protection of BCI system via reversible watermarking of EEG signal

2020

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The brain–computer interface (BCI) system is doing wonders for people suffering from restricted physical abilities due to accidents or diseases. BCI requires recording of electroencephalogram (EEG) from the subject in order to control electrical devices. In the wireless BCI system, there is always a possibility of EEG signal tampering/attacking during transmission, which may result in malfunction of the BCI system. This work introduces a security block in the BCI system that can ensure that the recorded EEG signals are intact at the receiving end of wireless BCI system. The security block identifies any tampering as well as authenticates the EEG signals at receiving end. Any change in EEG signal can result into classification error; because of that prediction error expansion based reversible watermarking approach has been used. The proposed scheme works efficiently and performance of BCI system remains unaltered after inclusion of security block.

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“…A reversible ECG watermarking mechanism is presented in [13]. The primary goal of this paper is to address false ownership claims and detect the tampered region of the ECG data.…”

Section: A Data Hiding Methods Developed For Ecg Signalsmentioning

confidence: 99%

“…The prediction error (i.e., the difference between the predicted value and the actual value) is modified to embed watermark bits. The mechanism in [13] embeds watermark bits by modifying the least significant bits (LBSs). Since this method uses LBS technique, the method in [13] is vulnerable to typical ECG processing, which includes unintentional filtering.…”

Section: A Data Hiding Methods Developed For Ecg Signalsmentioning

confidence: 99%

Robust Patient Information Embedding and Retrieval Mechanism for ECG Signals

et al. 2020

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At present, a patient's demography, such as name, age, and gender are stored separately from the acquired electrocardiogram (ECG) signal. This multiple storage mechanisms can create a severe threat to the reliability of diagnostics if the link between the demography data and the ECG signal breaks, either intentionally or unintentionally. This issue has become more prominent in recent years due to the use of a large number of wearable devices for physiological signal collection, especially in remote or nonclinical settings. In order to address this problem, in this paper, we propose a novel mechanism to embed patient's information within an ECG signal without degrading the accuracy of the physiological information contained in the ECG signal. In this work, a methodology is presented to find the less-significant region of the ECG signal. Then, the patient information is hidden in this region by modifying the selected discrete cosine transform (DCT) coefficients of the signal using our proposed embedding and decoding algorithms. Moreover, the patient information hidden in the ECG signal is able to resist filtering attack, such as high-pass filtering, which generally occur with the ECG signal processing. This is achieved via the use of error buffers in the embedding algorithm. The proposed mechanism can extract the embedded patient information, either in the presence or without the filtering attack. Moreover, a specifically designed synchronization sequence is added to identify the patient data embedded regions of the ECG signal at the decoding end. Further, as a security measure, the embedded patient details are scrambled using a secret key to protect the privacy of the patient. Our evaluation demonstrates the usefulness of the proposed methodology in successfully embedding the patient information without distorting the important medical information in an ECG signal. INDEX TERMS ECG signal, information hiding, watermarking, physiological signal processing, discrete cosine transform.

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A reversible and multipurpose ECG data hiding technique for telemedicine applications

Cited by 48 publications

References 21 publications

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

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

Protection of BCI system via reversible watermarking of EEG signal

Robust Patient Information Embedding and Retrieval Mechanism for ECG Signals

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