Classification of surface EMG signal with fractal dimension

Abstract

Surface EMG (electromyography) signal is a complex nonlinear signal with low signal to noise ratio (SNR). This paper is aimed at identifying different patterns of surface EMG signals according to fractal dimension. Two patterns of surface EMG signals are respectively acquired from the right forearm flexor of 30 healthy volunteers during right forearm supination (FS) or forearm pronation (FP). After the high frequency noise is filtered from surface EMG signal by a low-pass filter, fractal dimension is calculated from the filtered surface EMG signal. The results showed that the fractal dimensions of filtered FS surface EMG signals and those of filtered FP surface EMG signals distribute in two different regions, so the fractal dimensions can represent different patterns of surface EMG signals.

Characterization of surface EMG signals using improved approximate entropy

“…No mix region exists in the approximate entropy values of the two motions, and the division of the two clusters is apparent (Fig.1). Though fractal dimensions (Hu et al, 2005) of filtered FP and FS signals can be distinguished more possibly than those of raw FP and FS signals (Fig.2), there are still some mix points of the two clusters, so it is somewhat ambiguous when judging which cluster some of the points fall in. Comparing the parameters of approximate entropy with those of fractal dimension when characterizing the two motions graphically, we can see that the former works better than the latter.…”

“…For the two patterns of sEMG signals (Hu et al, 2005), we first calculated the approximate entropy values using the improved ApEn, and then compared the results with those of fractal dimension. The approximate entropy values of FP and FS signals distribute among 0.7~0.9 and 1.1~1.3 respectively.…”

“…Many parameters have been successfully applied to classify sEMG signals (Jang et al, 1994;Zardoshti-Kermani et al, 1995;Kang et al, 1995;Gupta et al, 1997;Englehart et al, 1999;Hu et al, 2005;Crawford et al, 2005;Huang et al, 2005;Kim et al, 2006), among which nonlinear dynamical analysis is a quite powerful approach. The calculations of most nonlinear dynamic measures, however, are frequently confronted with insufficient data points and noisy backgrounds.…”

“…We present here an improved ApEn where two vectors' similarity is based on their shapes rather than their absolute coordinates, and then employed it to extract features from sEMG signals. Results showed that, compared with fractal dimension as well as the standard ApEn, the improved ApEn can more effi-ciently characterize the same two different patterns of sEMG signals once dealt with by Hu et al(2005).…”

Recognition of Facial Movements and Hand Gestures Using Surface Electromyogram(sEMG) for HCI Based Applications

“…They also observed a linear relationship between the fractal dimension and the flexion-extension speeds and load. Hu et al [10] distinguished two different patterns of hand movements from forearm using fractal dimensions of sEMG signals.…”