A Novel ECG Data Compression Method Using Adaptive Fourier Decomposition With Security Guarantee in e-Health...

Area of Science:

• Biomedical Engineering
• Signal Processing
• Digital Health

Background:

• Electrocardiogram (ECG) data is crucial for diagnosing cardiac conditions.
• Efficient ECG compression is vital for e-health applications, enabling data storage and transmission.
• Existing methods often face trade-offs between compression ratio and signal fidelity.

Purpose of the Study:

• To develop a novel, high-performance ECG compression method for e-health.
• To integrate lossy and lossless compression stages with data encryption.
• To validate the method's effectiveness using a standard ECG database.

Main Methods:

• Hybrid approach combining Adaptive Fourier Decomposition (AFD) for lossy compression.
• Symbol Substitution (SS) technique for lossless enhancement and data encryption.
• Validation using 48 ECG records from the MIT-BIH arrhythmia database.

Main Results:

• Achieved an average compression ratio (CR) ranging from 17.6 to 44.5.
• Maintained a low Percentage Root Mean Square Difference (PRD) between 0.8% and 2.0%.
• Demonstrated a robust and linear relationship between PRD and CR.

Conclusions:

• The proposed AFD-SS method offers superior ECG compression performance.
• The integrated encryption provides enhanced security for e-health data.
• This technique is a promising candidate for pervasive e-health systems.