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Analiza natančnosti dekompozicije kratkočasovnih površinskih signalov EMG z metodo kompenzacije konvolucijskih jeder
Glaser, Vojko ; Holobar, Aleš ; Zazula, Damjan
Surface EMG (sEMG) decomposition based on Convolution Kernel Compensation (CKC) method demonstrates robustness to noise and diversity in anatomical characteristics of muscles, but has been validated ...off-line only, on sEMG signals of length of 10 s or more. This study utilized synthetic sEMG to quantify the CKC performance on short sEMG signals and assess the feasibility of online CKC decomposition. Surface EMG simulation modalities were similar to those described in (Holobar et al. ĆEstimating motor unit discharge patterns from high-density surface EMGĆ, Clin. Neurophysiol., 2009, pp. 551-562). Two different levels of constant excitation to the muscle were tested: 10% and 30% of Maximum Voluntary Contraction (MVC). The CKC decomposition accuracy, i.e. the percentage of accurately identified discharges per MU with respect to all the simulated MU discharges, was assessed over 10 simulation runs. On average, 10 3/8 2 MUs with the accuracy .90% were identified from 20 s long sEMG signals at 10%/30% excitation level and 20 dB SNR. For 10 s (5 s) long signals, these numbers decreased to 9 2/7 2(4 2/6 3) and were further decreased to 4 3/5 2 and 0 1/0 2 for 3 s and 1 s long signals, respectively. In conclusion, for signal lengths down to 5 s, the performance of CKC method is relatively constant. For shorter signal epochs, the performance decreases significantly.
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