A Secure and Efficient Framework for ECG Signal Digitization and Encryption for Internet of Medical Things...

Area of Science:

• Biomedical Engineering
• Cybersecurity
• Digital Health

Background:

• The Internet of Medical Things (IoMT) enables advanced healthcare through connected devices.
• Secure transmission and storage of sensitive Electrocardiogram (ECG) signals remain a significant challenge.
• Existing methods often process ECG data as raw signals or images separately, limiting efficiency.

Purpose of the Study:

• To develop a novel content-based encryption framework specifically for ECG data.
• To improve the security and efficiency of ECG data handling within IoMT systems.
• To ensure broad applicability across various ECG data formats, including paper-based records.

Main Methods:

• A preprocessing and segmentation pipeline to accurately extract ECG waveforms from 2D images, handling noise and overlapping traces.
• A two-layer chaotic encryption scheme involving Arnold Cat Map (ACM) for spatial scrambling and Double Random Phase Encoding for frequency-domain transformation.
• Direct application of cryptographic modules to native digital ECG signals and seamless integration with paper-based ECG datasets.

Main Results:

• High digitization accuracy of ECG waveforms from images, with a Structural Similarity Index (SSIM) of 0.94.
• Demonstrated fast execution time of 0.0036 seconds for the encryption process.
• Achieved strong encryption with high entropy (7.3) and low correlation (0.0006), alongside perfect signal recovery (PSNR = ∞).

Conclusions:

• The proposed framework offers a secure and efficient solution for ECG data encryption in IoMT healthcare.
• The method outperforms existing ECG encryption techniques based on standard evaluation metrics.
• The framework's versatility ensures compatibility with diverse ECG acquisition formats, enhancing its practical utility.