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

Document Type : Original Article

Authors

Faculty of Electrical and Robotics Engineering, Shahrood University of Technology, Shahrood, Iran

Abstract

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.

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References

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