Techniques for Decomposition of EMG Signals

Techniques for Decomposition of EMG Signals

Arun Kumar Wadhwani, Sulochana Wadhwani
Copyright: © 2011 |Pages: 11
DOI: 10.4018/978-1-60960-561-2.ch216
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Abstract

The information extracted from the EMG recordings is of great clinical importance and is used for the diagnosis and treatment of neuromuscular disorders and to study muscle fatigue and neuromuscular control mechanism. Thus there is a necessity of efficient and effective techniques, which can clearly separate individual MUAPs from the complex EMG without loss of diagnostic information. This chapter deals with the techniques of decomposition based on statistical pattern recognition, cross-correlation, Kohonen self-organizing map and wavelet transform.
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Decomposition Of Emg

The parameters of the MUAPs have proven to be of major importance for the clinical diagnosis of myopathies and neuropathies [1]. For perfect diagnosis, accurate decomposition of EMG signal is needed. The success of EMG signal decomposition is decided by the accurate classification of the MUAPs. There is a long list of limiting factors, which hamper the process of successful decomposition. Some of them arise from the characteristics of the MUAPs. The MUAPs have different durations (finite) and overlap in time and frequency domains. They occur at different time instants are time varying in nature and contaminated by noise and background activity produced by non detectable MUAPs. The repetition frequencies are so high that individual MUAPs rarely appear as isolated potentials [1].

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