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Related Experiment Video

Updated: May 3, 2026

Automatic Detection of Highly Organized Theta Oscillations in the Murine EEG
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A key-controlled watermarking framework for tamper localization in physiological signals.

Saifur Rahman1, Iynkaran Natgunanathan1, Chandan Karmakar1

  • 1School of Information Technology, Deakin University, Melbourne, Victoria, Australia.

Computers in Biology and Medicine
|September 17, 2025
PubMed
Summary

This study introduces a secure digital watermarking framework to detect tampering in electrocardiogram (ECG) signals. The lightweight system ensures perfect detection accuracy, preserving signal integrity for healthcare applications.

Keywords:
ECGKey-based secure embeddingLightweightTamper detectionWatermarking

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Area of Science:

  • Biomedical Engineering
  • Cybersecurity
  • Digital Signal Processing

Background:

  • Authenticity and integrity of physiological signals like electrocardiogram (ECG) are crucial in healthcare.
  • Transmitting and storing sensitive ECG data across networks poses security risks, necessitating robust tamper detection.

Purpose of the Study:

  • To develop a lightweight and secure digital watermarking framework for tamper detection in ECG signals.
  • To embed patient-specific identifiers securely while preserving signal quality and diagnostic accuracy.

Main Methods:

  • A digital watermarking approach embedding patient identifiers in the least significant bits of ECG samples.
  • Utilizing a secret key to determine embedding positions, preventing unauthorized access and extraction.
  • Evaluating robustness against amplification, Gaussian noise, and re-equalization tampering.

Main Results:

  • The framework achieved 100% detection accuracy and F1 scores across all tested conditions and secret key lengths (6, 12, 18 bits).
  • Demonstrated high robustness against various tampering methods without visible distortion.
  • The system precisely localizes tampered regions within the ECG signal.

Conclusions:

  • The proposed lightweight framework offers reliable and comprehensive tamper detection for ECG signals.
  • It is suitable for resource-constrained environments like Internet of Things (IoT) healthcare devices.
  • The method is applicable to other physiological signals (EEG, PPG) and represents a novel solution for secure physiological data handling.