ECG signal compression using combined modified discrete cosine and discrete wavelet transforms

Area of Science:

• Biomedical Engineering
• Signal Processing
• Medical Informatics

Background:

• Electrocardiogram (ECG) signal compression is crucial for efficient storage and transmission in healthcare.
• Existing methods often face trade-offs between compression ratio and signal fidelity.
• The need for advanced compression techniques to handle large volumes of ECG data is increasing.

Purpose of the Study:

• To propose a novel hybrid two-stage ECG signal compression method.
• To evaluate the effectiveness of combining Modified Discrete Cosine Transform (MDCT) and Discrete Wavelet Transform (DWT).
• To achieve high compression ratios while maintaining the diagnostic quality of ECG signals.

Main Methods:

• A hybrid compression strategy employing MDCT followed by DWT on ECG signal blocks.
• Decorrelation of spectral information using MDCT and subsequent compression of subordinate components via DWT.
• Thresholding of wavelet coefficients and compression using energy packing and binary-significant map coding.

Main Results:

• The proposed method achieved an average compression ratio (CR) of 21.5.
• High-quality signal reconstruction was demonstrated with a percentage root mean square error (PRD) of 5.89%.
• The method outperformed various state-of-the-art ECG compression techniques in simulations.

Conclusions:

• The hybrid MDCT-DWT method offers a superior balance between compression efficiency and signal quality for ECG data.
• The achieved compression ratio and fidelity make it suitable for real-time monitoring and diagnostic applications.
• This approach represents a significant advancement in ECG signal processing and data management.