S-EMG signal compression based on domain transformation and spectral shape dynamic bit allocation

Area of Science:

• Biomedical Engineering
• Signal Processing
• Data Compression

Background:

• Surface electromyographic (S-EMG) signal processing is crucial for non-invasive muscle assessment.
• Digital technology advancements create large S-EMG datasets, necessitating efficient data handling.
• Research is needed to address the transmission and storage challenges of S-EMG data.

Purpose of the Study:

• To develop and evaluate a novel data compression algorithm for S-EMG signals.
• To improve the efficiency of S-EMG signal transmission and storage.

Main Methods:

• An algorithm combining discrete wavelet transform, dynamic bit allocation, and entropy coding was developed.
• Spectral decomposition and de-correlation were achieved using discrete wavelet transform.
• Four distinct bit allocation spectral shape methods were implemented and compared.

Main Results:

• The algorithm demonstrated effectiveness on both isometric and dynamic S-EMG protocols.
• Objective performance metrics confirmed the algorithm's efficiency.
• Comparisons with existing literature encoders showed promising results.

Conclusions:

• The proposed S-EMG data compression algorithm, using decreasing bit allocation and arithmetic coding, is highly efficient.
• The algorithm shows promising performance compared to established techniques in scientific literature.