Enhancing multiple sclerosis diagnosis: A comparative study of electroencephalogram signal processing and entropy methods

Abstract

As one of the most common neurodegenerative diseases, Multiple sclerosis (MS) is a chronic immune-driven disorder that affects the central nervous system (CNS). Due to the variety of symptoms, accurately diagnosing MS demands rigorous attention to differential diagnosis, as various disorders can closely mimic its clinical and paraclinical features. Although MR imaging techniques are gold standards in diagnosing MS, the feasibility of advanced Electroencephalogram (EEG) signal processing methods is discussed in this study to detect patients with MS disorder. EEG signals from 50 individuals were evaluated through entropy-based methods. Sixteen distinct entropy methods were employed to extract features, which were used to train several machine-learning algorithms for classifying MS patients. Furthermore, each entropy method was individually evaluated to identify the most effective approach for MS diagnosis. A regional analysis of the EEG channels was conducted to determine the most informative regions for classification. The results indicated that the proposed method outperformed previous studies and achieved highly effective results in the classification of MS patients.