Abstract
This article describes some filtering methods to remove artifacts from the EMG signal envelope. Diverse EMG waveforms are studied using the Kalman filter (KF) and unbiased finite impulse response (UFIR) filter. The filters are developed in discrete-time state-space for Gauss-Markov colored measurement noise (CMN) and termed as cKF and cUFIR. It is shown that a choice of a proper CMN factor allows extracting the EMG waveform envelope with a high robustness. Extensive investigation have shown that the cKF and cUFIR filter are most efficient when the density is low of the motor unit action potential (MUAP) of the EMG and the Hilbert transform is required. Otherwise, when the envelope is well-pronounced and well-shaped with sharp edges due to a high MUAP density, the filters can be applied directly without using the Hilbert transform.
Highlights
The electromyography (EMG) signals are records of the motor unit action potential (MUAP) used in clinical/biomedical applications to detect and analyze voltage changes within any organ of interest
Because noise in EMG data is strictly not white, no essential advantage of the Kalman filter (KF) was demonstrated so far against other available methods. Another approach developed by Shmaliy and referred to as unbiased finite impulse response (UFIR) filtering [3] completely ignores the zero mean noise and is considered as a robust alternative to KF [30]
To tune the KF, we suppose that the measurement noise has the standard deviation of σξ = 50 μV and set σw = 0.1 V/s2 for the KF estimate to be consistent to the UFIR estimate
Summary
The electromyography (EMG) signals are records of the motor unit action potential (MUAP) used in clinical/biomedical applications to detect and analyze voltage changes within any organ of interest Such signals are describable in terms of the amplitude, frequency, and phase [1, 2] and can be processed using different kinds of finite impulse response (FIR) estimators [3,4,5,6,7]. Because noise in EMG data is strictly not white, no essential advantage of the KF was demonstrated so far against other available methods Another approach developed by Shmaliy and referred to as unbiased finite impulse response (UFIR) filtering [3] completely ignores the zero mean noise and is considered as a robust alternative to KF [30]. Both the KF and UFIR filter may be more efficient under a supposition of the colored measurement noise (CMN)
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