Detection of multiple sclerosis from photic stimulation EEG signals
| Autor: | Busra Kubra Karaca, Mehmet Feyzi Aksahin, Ruhsen Öcal |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
medicine.diagnostic_test
Photic Stimulation business.industry Multiple sclerosis 0206 medical engineering Biomedical Engineering Health Informatics Stimulation Pattern recognition 02 engineering and technology Electroencephalography medicine.disease 020601 biomedical engineering Standard deviation 03 medical and health sciences 0302 clinical medicine Continuous wavelet Signal Processing Healthy control medicine Sensitivity (control systems) Artificial intelligence business 030217 neurology & neurosurgery |
| Popis: | 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 pre-diagnosis of MS. |
| Databáze: | OpenAIRE |
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