Detection of Multiple Sclerosis Disease by EEG Coherence Analysis

Karaca, Busra Kubra; Akşahin, Mehmet Feyzi; Öcal, Ruhşen

doi:10.1109/tiptekno.2019.8894929

Cited by 3 publications

(2 citation statements)

References 13 publications

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“…It has been demonstrated that when compared to a single classifier model, a higher accuracy rate can be obtained. 30 However, depending on data, the method's classification accuracy differs. As a result, analyses will be conducted using ANN method, which provides the best results in this study.…”

Section: Computational Techniquesmentioning

confidence: 99%

Investigation of the Effect of Ear Measurements on Sex Estimation in Forensic Sciences Using Machine Learning Techniques: Descriptive Research

ÖZMEN AKYOL,

SEZGİN

2023

Turkiye Klinikleri J Foren Sci Leg Med

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Section: Computational Techniquesmentioning

confidence: 99%

Investigation of the Effect of Ear Measurements on Sex Estimation in Forensic Sciences Using Machine Learning Techniques: Descriptive Research

ÖZMEN AKYOL,

SEZGİN

2023

Turkiye Klinikleri J Foren Sci Leg Med

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“…In the study of Karaca et al [ 38 ], bipolar channel coherence analysis of EEG signal obtained from MS patients and healthy individuals was performed. Therefore, feature extraction was conducted from specific frequency bands.…”

Section: Related Workmentioning

confidence: 99%

A Hybrid Approach for MS Diagnosis Through Nonlinear EEG Descriptors and Metaheuristic Optimized Classification Learning

Mohseni

Moghaddasi

2022

Computational Intelligence and Neuroscience

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Multiple sclerosis (MS), a disease of the central nervous system, affects the white matter of the brain. Neurologists interpret magnetic resonance images that are often complicated, time-consuming, and contradictory. Using EEG signals, this disease can be analyzed and diagnosed more accurately. However, it is imperative that MS be diagnosed by specialists using assistive technology. Until now, a few methods have been proposed in this field that are sometimes associated with different challenges in analysis. This paper presents a hybrid approach to MS diagnosis in order to decrease classification error rates. Using the proposed method, EEG descriptors in both the time and frequency domains are analyzed. After the feature extraction stage, a modified version of the ant colony optimization method (m-ACO) was used to select the appropriate subset of features. Then, the support vector machine is used to determine whether the disease exists. A metaheuristic algorithm adjusts the support vector machine’s parameters to withstand overfitting challenges. Despite a limited number of input channels, significant classification accuracy has been achieved using wavelet analysis techniques, dividing all five subbands of EEG signals, signal windowing, and extracting efficient features from the data. Additionally, alpha, beta, and gamma bands of the signal are important, and the accuracy, sensitivity, and specificity levels are higher than 98.5%. Compared to similar MS diagnostic methods, the proposed method achieved significantly higher diagnostic accuracy. Application and implementation of this method can be effective in treating neurological diseases, including multiple sclerosis.

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Advancements in Multiple Sclerosis Disease Classification Through Machine Learning

Pandya,

Degadwala,

Vyas

et al. 2023

2023 Third International Conference on Ubiquitous Computing and Intelligent Information Systems (ICUIS)

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Detection of Multiple Sclerosis Disease by EEG Coherence Analysis

Cited by 3 publications

References 13 publications

Investigation of the Effect of Ear Measurements on Sex Estimation in Forensic Sciences Using Machine Learning Techniques: Descriptive Research

Investigation of the Effect of Ear Measurements on Sex Estimation in Forensic Sciences Using Machine Learning Techniques: Descriptive Research

A Hybrid Approach for MS Diagnosis Through Nonlinear EEG Descriptors and Metaheuristic Optimized Classification Learning

Advancements in Multiple Sclerosis Disease Classification Through Machine Learning

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