Wrapping based curvelet transform approach for ECG watermarking in telemedicine application. (May 2022)
- Record Type:
- Journal Article
- Title:
- Wrapping based curvelet transform approach for ECG watermarking in telemedicine application. (May 2022)
- Main Title:
- Wrapping based curvelet transform approach for ECG watermarking in telemedicine application
- Authors:
- Khaldi, Amine
Kafi, Med Redouane
Moad, Med Sayah - Abstract:
- Highlights: A watermark composed of the patient's information and ECG acquisition parameters are concealed in ECG signal. To extract the image's frequency content, a fast discrete curvelet transform is performed which allows obtaining a better multi-scale representation than those provided by wavelets. In the proposed blind approach, the extraction of the mark is done without using the original signal. The tests demonstrated that our approach allows insertion of a large amount of data while preserving a reasonable imperceptibility. Abstract: Patient information is currently maintained separately from the physiological signals received in telemedicine. Furthermore, in recent years, the usage of portable devices to obtain such medical information has become more common. This form of storing can lead to several other authentication issues and compromise the accuracy of diagnostics. To address this issue, we propose a watermarking method for embedding patient information in the ECG signal in this study. In our technique, the watermark is a QR code that contains the patient's information as well as the Electrocardiogram signal. The 1D ECG signal is transformed to a 2D signal in the proposed technique. A fast discrete curvelet transform is used to extract the signal's frequency content, and then Schur decomposition is used to the frequency sub-bands obtained. Finally, the watermark bits are integrated by modifying the Eigen values produced. According to imperceptibilityHighlights: A watermark composed of the patient's information and ECG acquisition parameters are concealed in ECG signal. To extract the image's frequency content, a fast discrete curvelet transform is performed which allows obtaining a better multi-scale representation than those provided by wavelets. In the proposed blind approach, the extraction of the mark is done without using the original signal. The tests demonstrated that our approach allows insertion of a large amount of data while preserving a reasonable imperceptibility. Abstract: Patient information is currently maintained separately from the physiological signals received in telemedicine. Furthermore, in recent years, the usage of portable devices to obtain such medical information has become more common. This form of storing can lead to several other authentication issues and compromise the accuracy of diagnostics. To address this issue, we propose a watermarking method for embedding patient information in the ECG signal in this study. In our technique, the watermark is a QR code that contains the patient's information as well as the Electrocardiogram signal. The 1D ECG signal is transformed to a 2D signal in the proposed technique. A fast discrete curvelet transform is used to extract the signal's frequency content, and then Schur decomposition is used to the frequency sub-bands obtained. Finally, the watermark bits are integrated by modifying the Eigen values produced. According to imperceptibility experiments, this method produces a watermarked signal that is identical to the originals, keeping the diagnostic content of the signal. Robustness test indicated that the watermark could overcome the most popular watermarking attacks. … (more)
- Is Part Of:
- Biomedical signal processing and control. Volume 75(2022)
- Journal:
- Biomedical signal processing and control
- Issue:
- Volume 75(2022)
- Issue Display:
- Volume 75, Issue 2022 (2022)
- Year:
- 2022
- Volume:
- 75
- Issue:
- 2022
- Issue Sort Value:
- 2022-0075-2022-0000
- Page Start:
- Page End:
- Publication Date:
- 2022-05
- Subjects:
- Telemedicine -- Watermarking -- Electrocardiogram signals -- Quick response code -- Schur decomposition -- Fast discrete curvelet transform
Signal processing -- Periodicals
Biomedical engineering -- Periodicals
Signal Processing, Computer-Assisted -- Periodicals
Image Processing, Computer-Assisted -- Periodicals
Biomedical Engineering -- Periodicals
610.28 - Journal URLs:
- http://www.sciencedirect.com/science/journal/17468094 ↗
http://www.elsevier.com/journals ↗
http://www.sciencedirect.com/science?_ob=PublicationURL&_tockey=%23TOC%2329675%232006%23999989998%23626449%23FLA%23&_cdi=29675&_pubType=J&_auth=y&_acct=C000045259&_version=1&_urlVersion=0&_userid=836873&md5=664b5cf9a57fc91971a17faf20c32ec1 ↗ - DOI:
- 10.1016/j.bspc.2022.103540 ↗
- Languages:
- English
- ISSNs:
- 1746-8094
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - 2087.880400
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British Library HMNTS - ELD Digital store - Ingest File:
- 21275.xml