EMG Signal Feature Extraction, Normalization and Classification for Pain and Normal Muscles Using Genetic Algorithm and Support Vector Machine

Abstract

Electromyography (EMG) is the process of measuring neuromuscular activities generated during the contraction and expansion period of muscles throughout the body. The potential is recorded by inserting needle or by placing electrodes on the surface of body. In this research, an automatic EMG signal classification system is developed using machine learning oriented Support Vector Machine (SVM). The collected data is selected using Genetic Algorithm (GA). The purpose of GA is to select those rows from the dataset, which contains potential or electrical activities recorded while the patient is in motion. Furthermore, the selected features are neutralized using critic method. To improve the row selection cosine similarity is being used to determine an average value hence also helps for data reduction. Based on the average similarity values, SVM is trained and used for classification during the testing phase. The experiment has been performed in MATLAB tool and the classification accuracy for normal and pain EMG signal of 91.3% and 92.4% respectively is achieved.