ESTIMATION OF EDSS FROM EEG SIGNALS OF MULTIPLE SCLEROSIS PATIENTS

Year 2023, , 169 - 188, 30.06.2023

Seda Şaşmaz Karacan , Hamdi Melih Saraoğlu , Sibel Canbaz Kabay

https://doi.org/10.59313/jsr-a.1253334

Abstract

Multiple sclerosis (MS) is an autoimmune, neurodegenerative, chronic disease that affects the central nervous system and manifests itself with attacks. Although there is no definite cure for the disease, it is possible to control these attacks. Follow-up of the disease has great importance in terms of disability. An Extended Disability Status Scale (EDSS) is used to show how much the disease affects. This score is determined by specialized clinicians. In this study, the EDSS score, previously determined by neurologists, was attempted to be estimated using the EEG signals. 32-channel EEG signals were recorded while 17 MS patients with EDSS 1.0, 1.5, and 2.0 were performing a working memory task. Using the band power of these 6-minute EEG signals, EDSS estimation was performed with the Decision Tree Regressor, resulting in a Mean Absolute Error (MAE) of 0.088. With the Leave One Out Cross-Validation, 17 trees were extracted and 12 were found to be identical. As a result, the band power features of F7 and CP2 EEG channels were found to be successful in predicting 3-level EDSS scores with a decision tree regressor with 0.0 MAE. Additionally, the relationship between the scores obtained in the working memory task and the EDSS scores of MS patients was statistically calculated with One-way ANOVA. There was no significant difference between the EDSS score and the task scores (p>.05).

Keywords

Multiple sclerosis (MS), EEG, EDSS, Working Memory, Decision Tree Regressor

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