SVM-based validation of motor unit potential trains extracted by EMG signal decomposition

Motor unit potential trains (MUPTs) extracted via electromyographic (EMG) signal decomposition can aid in the diagnosis of neuromuscular disorders and the study of the neural control of movement, but only if they are valid. In this paper, support vector machine (SVM)-based supervised classifiers are proposed to estimate the validity of extracted MUPTs. The classifiers use either the MU firing pattern or the MUP shape consistency of an MUPT, or both, to estimate its validity. The developed classifiers estimate the class label of an MUPT (i.e., valid/invalid) and a degree of support for the decision being made. A single SVM that estimates the validity of a given MUPT using extracted MU firing pattern and MUP shape features was investigated. In addition, the effectiveness of multiclassifier techniques which estimate the overall validity of a train by fusing the MU firing pattern and MUP shape validity of a given MUPT, determined separately by two distinct SVMs, was also investigated. Training based only on simulated data showed robust classification performance of the several multiclassifier methods when tested using both simulated and real test data. Of the methods studied, the multiclassifier constructed using trainable logistic regression to aggregate base classifier outputs had the best performance. Assuming 12.7% of extracted MUPTs are on average invalid, the estimated accuracy for this method in correctly categorizing MUPTs extracted during decomposition was 99.4% and 98.8% for simulated and real data, respectively.