An EMG-based feature extraction method using a normalized weight vertical visibility algorithm for myopathy and neuropathy detection

Background: Electromyography (EMG) signals recorded from healthy, myopathic, and amyotrophic lateral sclerosis (ALS) subjects are nonlinear, non-stationary, and similar in the time domain and the frequency domain. Therefore, it is difficult to classify these various statuses. Methods: This study proposes an EMG-based feature extraction method based on a normalized weight vertical visibility algorithm (NWVVA) for myopathy and ALS detection. In this method, sampling points or nodes based on sampling theory are extracted, and features are derived based on relations among the vertical visibility nodes with their amplitude differences as weights. The features are calculated via selective statistical mechanics and measurements, and the obtained features are assembled into a feature matrix as classifier input. Finally, powerful classifiers, such as k-nearest neighbor, multilayer perceptron neural network, and support vector machine classifiers, are utilized to differentiate signals of healthy, myopathy, and ALS cases. Results: Performance evaluation experiments are carried out, and the results revealed 98.36% accuracy, which corresponds to approximately a 2% improvement compared with conventional methods. Conclusions: An EMG-based feature extraction method using a NWVVA is proposed and implemented to detect healthy, ALS, and myopathy statuses.