Tonic-Myoclonic Epileptic Seizure Classification based on Surface Electromyography

Abstract

Epilepsy diagnosis is one of the critical subjects of research in health fields in recent years. The aim of this paper is to detect and classify two types of seizures: Tonic and myoclonic based on surface electromyography signals. Based on data from eight sEMG electrodes on the biceps brachii, flexor carpi ulnaris, gastrocnemius and quadriceps muscle. Several time domain features were investigated, including waveform length, mean absolute value, variance, mobility, complexity, kurtosis, skewness, simple square integral, integrated EMG, root mean square, average amplitude change, standard deviation and entropy. These features are used as inputs to the Artificial Neural Network (ANN) classifier to determine if a data segment corresponds to a seizure or not. Following the development of the ANN model, an evaluation performance in terms of accuracy is presented to validate the results, which reached an accuracy of 93.33%. The overall performance of the presented machine learning algorithm is sufficient for clinical implementation.