Classification of ALS Disease Using Support Vector Machines

Abstract

In this study, SVM (Support Vector Machine) algorithm is used for the diagnosis of ALS which is the most common type of motor neuron disease. Before classification of EMG data with SVM (Support Vector Machine); pre-processing, segmentation, feature extraction and clustering stages of data are completed. In the stage of clustering, hybrid and hierarchical clustering methods are employed. After that, feature vectors in time and frequency domains and their different combinations (a total of 11 feature vectors) are fed to the SVM and the obtained results are observed. It is understood that the advantages of clustering methods dependent on the feature vectors; multiple feature vectors provide high performance in the diagnosis of ALS disease and exhibit much lower discrepancy.