Key result
The robust measure of kurtosis (KR2) performed best among statistical descriptors in identifying noisy EMG signals from their probability density functions across all contamination types and SNR levels.
The robust measure of kurtosis KR2 is the most effective statistical descriptor for identifying noisy EMG signals from their probability density functions across various noise types and SNR levels.
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The probability density function (pdf) of an electromyography (EMG) signal provides useful information for choosing an appropriate feature extraction technique. The pdf is influenced by many factors, including the level of contraction force, muscle type, and noise. In this paper, we investigated the pdfs of noisy EMG signals artificially contaminated with five different noise types: 1) Electrocardiography (ECG) interference; 2) many spurious background spikes; 3) white Gaussian noise; 4) motion artifact; and 5) power line interference at various levels of signal-to-noise ratio (SNR). In addition, we evaluated a set of statistical descriptors for identifying a noisy EMG signal from its pdf, specifically kurtosis, negentropy, L-kurtosis, and robust measures of kurtosis (KR 1 and KR 2 ). The results show that at low SNR (<;5 dB), all noise types affect the statistical descriptors for the pdf of a noisy EMG signal. In addition, KR 2 performs the best among these descriptors in identifying a noisy EMG signal from its pdf, because it is computed based on the quantiles of the data. As a result, it can avoid the effects of outliers resulting in the correct identification of pdf shape of noisy EMGs with all contamination types and all levels of SNR.
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KR2 may aid noisy EMG identification; hypothesis-generating and requires prospective validation before clinical use.
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Thongpanja et al. (2016) studied EMG signal noise. Statistical descriptors (kurtosis, negentropy, L-kurtosis, KR1, KR2) was evaluated on Identification of noisy EMG signal from its pdf. The robust measure of kurtosis (KR2) performed best among statistical descriptors in identifying noisy EMG signals from their probability density functions across all contamination types and SNR levels.
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