Electromyogram Signal Compression Based on Empirical-Mode-Decomposition-Based Approximation and DCT-Based Smoothing

Electromyogram (EMG) signals are useful in muscle behavior assessment and have some clinical applications. Today, there is a great tendency to transmit and store long-term EMG recordings which implies the importance of EMG signal compression. In this paper, we have proposed an EMG signal compression approach based on Empirical-Mode-Decomposition-based signal approximation, Discrete-Cosine-Transform-based signal smoothing, two-dimensional signal processing, wavelet transform, and SPIHT coding. We have evaluated the compression performance of the proposed approach by two sets of measures: The compression throughput and clinical-information-preserving measures. The former include two measures of PRD and CF while the latter uses four spectral parameters as the appropriate measures.