Curvelets‐based ECG steganography for data security

Jero, S. Edward; Ramu, Palaniappan

doi:10.1049/el.2015.3218

Cited by 41 publications

(27 citation statements)

References 9 publications

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“…By considering the ECG signal distortion due to the addition of watermark bit (i.e., patient's detail), a Curveletsbased method is proposed by Jero et al in [10]. In [10], a Curvelet transform is used to decompose the ECG signal into frequency sub-bands. Then, in the high frequency subbands, watermark bits are embedded using QIM.…”

Section: A Data Hiding Methods Developed For Ecg Signalsmentioning

confidence: 99%

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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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Section: A Data Hiding Methods Developed For Ecg Signalsmentioning

confidence: 99%

“…Using the position information and the threshold selection algorithm, watermark bits are extracted. The method in [10] can disturb certain important parts of an ECG signal. The method in [10], alters the signal even in the medically significant region and therefore limiting the usability in practice.…”

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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“…Recently, Jero and Ramu studied the watermarking of arrhythmic ECG signal [ 22 ] and proposed replacing regular wavelets by curvelets-based transform to hide valuable patient information into the ECG signal [ 23 , 24 ]. Their paper also proposed an adaptive selection of the watermarks location, however without relation to the ECG medical content.…”

Section: Related Workmentioning

confidence: 99%

Optimizing Wavelet ECG Watermarking to Maintain Measurement Performance According to Industrial Standard

Świerkosz

Augustyniak

2018

Sensors

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Watermarking is currently investigated as an efficient and safe method of embedding additional patient or environment-related data into the electrocardiogram. This paper presents experimental work on the assessment of the loss of ECG (electrocardiogram signal) diagnostic quality from the industrial standard EN60601-2-25:2015 point of view. We implemented an original time-frequency watermarking technique with an adaptive beat-to-beat lead-independent data container design. We tested six wavelets, six coding bit depth values (including the automatic noise-dependent one) and two types of watermark content to find the conditions that are necessary for watermarked ECG to maintain the compliance with International Electrotechnical Commission (IEC) requirements for interpretation performance. Unlike other authors, we did not assess the differences of signal values, but errors in ECG wave delineation results. The results of a total of 7300 original and watermarked 10 s ECGs were statistically processed to reveal possible interpretation quality degradation due to watermarking. Finally we found (1) the Symlet of 11-th order as the best of the wavelets that were tested; (2) the important role of ECG wave delineation and noise tracking procedures; (3) the high influence of the watermark-to-noise similarity of amplitude and values distribution and (4) the stability of the watermarking capacity for different heart rates in atrial rhythms.

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“…Recently, data hiding has played a crucial role in protecting secret data in multimedia files, such as documents, images, and videos [1][2][3][4][5]. In addition, several researchers have presented data hiding for biomedical signals such as an electrocardiogram (ECG) to secure personal sensitive information, including patients' diagnoses [6][7][8][9][10][11][12][13][14][15][16].…”

Section: Introductionmentioning

confidence: 99%

“…However, their proposed method provided a payload size of 4,489 bits. By using curvet transform and quantization techniques, Jero and Ramu [10] developed a simple ECG steganography for data security. Simulations indicated that the percentage residual difference (PRD) and BER of the method were 0.110 and 31.84%, respectively; the size of the payload was 4,016 bits.…”

Section: Introductionmentioning

confidence: 99%

Progressive Data Hiding in Integer Wavelet Transform of Electrocardiogram by Using Simple Decision Rule and Coefficient Calibration

Yang¹,

Wang²

2020

RIA

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Based on one dimensional (1D) integer wavelet transform (IWT) domain, patient's data can be effectively embedded in electrocardiogram (ECG) via the proposed criterion and coefficient alignment. Multiple data bits can be sequentially hidden in host bundles (with different lengths) of the low and high subbands of IWT coefficients. Simulations indicated that the average signal-to-noise ratio (SNR) and the payload of the proposed method are superior to those of existing techniques. Additionally, the proposed method exhibited a robustness that has rarely been observed in conventional ECG steganography. Our method is capable of resisting attacks such as cropping, inversion, scaling, translation, truncation, and Gaussian noise addition. Because bit embedding and extraction procedures are quite simple, our method can be applied in portable biometric devices.

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Curvelets‐based ECG steganography for data security

Cited by 41 publications

References 9 publications

Robust Patient Information Embedding and Retrieval Mechanism for ECG Signals

Robust Patient Information Embedding and Retrieval Mechanism for ECG Signals

Optimizing Wavelet ECG Watermarking to Maintain Measurement Performance According to Industrial Standard

Progressive Data Hiding in Integer Wavelet Transform of Electrocardiogram by Using Simple Decision Rule and Coefficient Calibration

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