Detection of Multiple Sclerosis Disease by EEG Coherence Analysis

Abstract

Multiple sclerosis (MS) is a chronic and inflammatory disease affecting the brain and spinal cord. Although the exact cause of MS is not known, genetic, environmental and immunological factors are involved in the etiology of the disease. The lack of a single diagnostic test for early diagnosis of MS and the similarity of clinical features in MS to other diseases is a serious problem. Early detection of MS is important, and therefore a rapid and reliable pre-diagnosis of MS is important for the treatment and prognosis of the disease. Electroencephalography (EEG) signals provide important information about brain and nerve diseases. Therefore, in the proposed study, a decision support system has been developed which will contribute to the pre-diagnosis by using EEG signals. In this context, coherence analysis of bipolar channel pairs of EEG signals obtained from MS patients and healthy individuals was performed and feature extraction was performed from certain frequency bands. Using the obtained features, the "Subspace Discriminant" classifier was trained with 95.8% accuracy and then the system was tested. As a result, accuracy, sensitivity and specificity rates were 91.67%, 85.71% and 100%, respectively.