Improving the performance of electrocardiogram sub-band coder by extensive Markov system

Area of Science:

• Biomedical Engineering
• Signal Processing
• Information Theory

Background:

• Digital coding of electrocardiogram (ECG) signals is crucial for efficient data storage and transmission.
• Previous sub-band coding (SBC) methods have limitations in achieving high compression ratios while maintaining signal fidelity.
• Redundancy in ECG signals, including coding and intersample redundancy, presents opportunities for improved compression algorithms.

Purpose of the Study:

• To improve the digital coding efficiency of ECG signals.
• To evaluate the performance of a six-band sub-band coding (SBC) system enhanced with extensive Markov processes.
• To compare the proposed system's compression ratio and signal fidelity against previous SBC methods.

Main Methods:

• Implementation of a six-band sub-band coding (SBC) architecture.
• Application of an extensive Markov process as a post-processor for each sub-band signal.
• Utilizing prediction of incoming samples based on previous samples to exploit coding and intersample redundancies.

Main Results:

• Achieved an average signal-to-noise ratio (SNR) of 27.21 dB and a peak SNR of 58.6 dB.
• Reached an average bit rate of 0.48 bits per sample, corresponding to a compression ratio of 25.
• Demonstrated superior performance compared to previous SBC systems, which achieved 0.714 bits per sample at similar signal fidelity.

Conclusions:

• The proposed six-band SBC system with extensive Markov processes significantly enhances ECG data compression.
• The advanced prediction mechanism effectively reduces redundancy, leading to higher compression ratios.
• This approach offers a more efficient solution for digital ECG data management and transmission.