Detection of multiple sclerosis from photic stimulation EEG signals

Background: Multiple Sclerosis (MS) is characterized as a chronic, autoimmune and inflammatory disease of the central nervous system. Early diagnosis of MS is of great importance for the treatment and course of the disease. In addition to the many methods, cost-effective and non-invasive electroencephalogram signals may contribute to the pre-diagnosis of MS. Objectives: The aim of this paper is to classify male subjects who have MS and who are healthy control using photic stimulation electroencephalogram signals. Methods: Firstly the continuous wavelet transformation (CWT) method was applied to electroencephalogram signals under photic stimulation with 5Hz, 10Hz, 15Hz, 20Hz, and 25Hz frequencies. The sum, maximum, minimum and standard deviation values of absolute CWT coefficients, corresponding to 1-4 Hz and 4-13 Hz frequency ranges, were extracted in each stimulation frequency region. The ratios of these values obtained from the frequency ranges 1-4Hz and 4-13Hz was decided as features. Finally, various machine learning classifiers were evaluated to test the effectivity of determined features. Results: Consequently, the overall accuracy, sensitivity, specificity and positive predictive value of the proposed algorithm were 80 %, 72.7 %, 88.9 %, and 88.9 %, respectively by using the Ensemble Subspace k-NN classifier algorithm. Conclusions: The results showed how photic stimulation electroencephalogram signals can contribute to the prediagnosis of MS.

Automated summary

This study aimed to classify male subjects with MS and healthy controls using photic stimulation electroencephalogram signals. Through analysis and evaluation, machine learning classifiers showed good performance based on specific EEG wave features.