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Double-layer data-hiding mechanism for ECG signals

Version 2 2025-04-09, 06:14
Version 1 2025-04-07, 05:38
journal contribution
posted on 2025-04-09, 06:14 authored by Iynkaran NatgunanathanIynkaran Natgunanathan, Chandan KarmakarChandan Karmakar, Sutharshan RajasegararSutharshan Rajasegarar, Tianrui Zong
AbstractDue to the advancement in biomedical technologies, to diagnose problems in people, a number of psychological signals are extracted from patients. We should be able to ensure that psychological signals are not altered by adversaries and it should be possible to relate a patient to his/her corresponding psychological signal. As far as our awareness extends, none of the existing methods possess the capability to both identify and verify the authenticity of the ECG signals. Consequently, this paper introduces an innovative dual-layer data-embedding approach for electrocardiogram (ECG) signals, aiming to achieve both signal identification and authenticity verification. Since file name-based signal identification is vulnerable to modifications, we propose a robust watermarking method which will embed patient-related details such as patient identification number, into the medically less-significant portion of the ECG signals. The proposed robust watermarking algorithm adds data into ECG signals such that the patient information hidden in an ECG signal can resist the filtering attack (such as high-pass filtering) and noise addition. This is achieved via the use of error buffers in the embedding algorithm. Further, modification-sensitive fragile watermarks are added to ECG signals. By extracting and checking the fragile watermark bits, we can determine whether an ECG signal is modified or not. To ensure the security of the proposed mechanism, two secret keys are used. Our evaluation demonstrates the usefulness of the proposed system.

History

Journal

Eurasip Journal on Advances in Signal Processing

Volume

2024

Article number

85

Pagination

1-22

Location

Berlin, Germany

Open access

  • Yes

ISSN

1687-6172

eISSN

1687-6180

Language

eng

Publication classification

C1 Refereed article in a scholarly journal

Publisher

SpringerOpen

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