A Novel Compressive Sensing Method for Secure and Energy Efficient ECG Signal Transmission Applications.

Area of Science:

• Biomedical Engineering
• Signal Processing
• Cybersecurity

Background:

• Internet of Medical Things (IoMT) requires secure and efficient data transmission.
• Electrocardiogram (ECG) signals are crucial for cardiac monitoring but generate large datasets.
• Existing methods may struggle with balancing compression, security, and feature preservation.

Purpose of the Study:

• To develop a novel Compressive Sensing (CS)-based cryptosystem for ECG signals in IoMT.
• To ensure secure and efficient transmission of ECG data, maintaining privacy and integrity.
• To evaluate the system's performance in terms of compression, reconstruction accuracy, and feature preservation.

Main Methods:

• Utilized the sparsity of ECG signals in the wavelet domain.
• Implemented a CS-based cryptosystem with an additional encryption stage.
• Evaluated four sensing matrices and three reconstruction algorithms across multiple wavelet families.
• Simulated using the MIT-BIH Arrhythmia Database.

Main Results:

• The Low-Density Parity-Check (LDPC) matrix with the L1 optimization algorithm yielded the highest Quality Score.
• Achieved a Compression Ratio of up to 50%.
• Demonstrated excellent preservation of pathological features, even with abnormal heartbeats.
• Hardware implementation on an FPGA achieved real-time operation (1 MHz) with low power consumption (0.8nJ/sample).

Conclusions:

• The proposed CS cryptosystem offers a secure, efficient, and feature-preserving solution for ECG transmission in IoMT.
• The LDPC matrix and L1 optimization combination is highly effective for ECG signal compression and reconstruction.
• The system's hardware feasibility on low-resource devices enables practical real-time applications.