Digital Watermarking in Telemedicine an Example from ECG - Review of Challenges, Methods and Applications

Świerkosz, Agnieszka

doi:10.1007/978-3-319-47154-9_29

Cited by 2 publications

(2 citation statements)

References 3 publications

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“…The authors also found that “changing some parts of the ECG signal will not affect the overall utility of the ECG signal”, however they did not verify this against the industrial standard for ECG interpretation quality. These ideas correspond to a general principle of steganography [ 19 ] and were also followed in our previous research [ 20 ].…”

Section: Related Workmentioning

confidence: 86%

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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Section: Related Workmentioning

confidence: 86%

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

Świerkosz

Augustyniak

2018

Sensors

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“…Here we report all the standard methods found in the literature as illustrated in Table 1 and those used in this work to measure imperceptibility and robustness (along with capacity and computational cost) [34]. We use the methods for performance evaluation by ensuring [15] Discrete Wavelet Transform Robustness [16] Multiwavelet Transform Imperceptibility [17] Discrete Wavelet Transform Robustness [18] Fourier Transform Fidelity [19] Discrete wavelet Transform Imperceptibility [20] Discrete wavelet Transform Imperceptibility Frequency Domain + Radial Basis NN [21] Discrete Cosine Transform Robustness [22] Discrete wavelet Transform Imperceptible [23] Discrete wavelet Transform Invisible and Robust [24] Discrete wavelet Transform Robustness Frequency Domain + Hopfield NN [25] Capacity [26] Imperceptibility [27] Image Quality [28] Discrete Cosine Transform Invisible and Robust Frequency Domain + Full Counter Propagation NN [29] Robustness, Imperceptibility [30] Discrete Cosine Transform Robustness [31] Discrete Cosine Transform Complexity, Capacity, PSNR [32] Discrete Cosine Transform Imperceptibility, and robustness Frequency Domain + Synergetic NN [33] Discrete Wavelet Transform Robustness and Imperceptibility i) model and sources of distortion remain uniform ii)…”

Section: Literature Reviewmentioning

confidence: 99%

A Computational Modelling and Algorithmic Design Approach of Digital Watermarking in Deep Neural Networks

Kavitha¹,

Eranna²,

Giriprasad³

2020

Adv. sci. technol. eng. syst. j.

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In this paper we propose an algorithmic approach for Convolutional Neural Network (CNN) for digital watermarking which outperforms the existing frequency domain techniques in all aspects including security along with the criteria in the neural networks such as conditions embedded, and types of watermarking attack. This research addresses digital watermarking in deep neural networks and with comprehensive experiments through computational modeling and algorithm design, we examine the performance of the built system to demonstrate the potential of watermarking neural networks. The inability of intruder towards the retrieval of data without the knowledge of architecture and keys is also discussed and results of the proposed method are compared with the state of the art methods at different noises and attacks.

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Digital Watermarking in Telemedicine an Example from ECG - Review of Challenges, Methods and Applications

Cited by 2 publications

References 3 publications

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

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

A Computational Modelling and Algorithmic Design Approach of Digital Watermarking in Deep Neural Networks

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