This paper introduces the importance of biosignal quality assessment and presents a pattern classification approach to differentiate clean from contaminated electromyography (EMG) signals. Alternatively to traditional bottom-up approaches, which examine specific contaminants only, we present a top-down approach using a one-class support vector machine (SVM) trained on clean EMG and tested on artificially contaminated EMG. Both simulated and real EMG are used. Results are evaluated for each contaminant: 1) power line interference; 2) motion artifact; 3) ECG interference; 4) quantization noise; 5) analog-to-digital converter clipping; and 6) amplifier saturation, as a function of the level of signal contamination. Results show that different ranges of contamination can be detected in the EMG depending on the type of contaminant. At high levels of contamination, the SVM classifies all EMG signals as contaminated, whereas at low levels of contamination, it classifies the majority of EMG signals as contaminant free. A transition point for each contaminant is identified, where the classification accuracy drops and variance in classification increases. In some cases, contamination can be detected with the SVM when it is not visually discernible. This method is shown to be successful in detecting problems due to single contaminants but is generic to all forms of contamination in EMG.

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Keywords Biomedical measurements, biosignal quality analysis, electromyography (EMG), machine learning, myoelectric signals, support vector machines (SVMs).
Persistent URL
Journal IEEE Transactions on Instrumentation and Measurement
Fraser, G.D. (Graham D.), Chan, A, Green, J, & Macisaac, D.T. (Dawn T.). (2014). Automated biosignal quality analysis for electromyography using a one-class support vector machine. IEEE Transactions on Instrumentation and Measurement, 63(12), 2919–2930. doi:10.1109/TIM.2014.2317296