Extreme Gradient Boosting for Surface Electromyography Classification on Time-Domain Features

Abstract

Surface electromyography (sEMG) signals play an essential role in disease diagnosis and rehabilitation. This study applied a powerful machine learning algorithm called extreme gradient boosting (XGBoost) to classify sEMG signals acquired from muscles around the knee for distinguishing patients with knee osteoarthritis (KOA) from healthy subjects. First, to improve data quality, we preprocessed the data via interpolation and normalization. Next, to ensure the description integrity of model input, we extracted nine time-domain features based on the statistical characteristics of sEMG signals over time. Finally, we classified the samples using XGBoost and cross-validation (CV) and compared the results to those produced by the support vector machine (SVM) and the deep neural network (DNN). Experimental results illustrate that the presented method effectively improves classification performance. Moreover, compared with the SVM and the DNN, XGBoost has higher accuracy and better classification performance, which indicates its advantages in the classification of patients with KOA based on sEMG signals.