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

Abstract

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.

Multiple Sclerosis Diagnosis Using Machine Learning and Deep Learning: Challenges and Opportunities

“…Mohseni and Moghaddasi [ 67 ] introduced a hybrid approach for MS diagnosis with an aim to decrease the classification error rate. In the study, they focused on analyzing Electroencephalogram (EEG) descriptors in both the time and frequency domains.…”

Examining electroencephalogram signatures of people with multiple sclerosis using a nonlinear dynamics approach: a systematic review and bibliographic analysis

“…Seven articles used feature extraction, feature selection, and feature classification in their studies ( Ahmadi and Pechenizkiy, 2016 ; Torabi et al, 2017 ; Kotan et al, 2019 ; Raeisi et al, 2020 ; Karaca et al, 2021 ; Karacan et al, 2022 ; Mohseni and Moghaddasi, 2022 ).…”

“…Thus, unlike ApEn, SampEn is relatively consistent irrespective of dataset size ( Richman and Moorman, 2000 ). Mohseni and Moghaddasi (2022) used sample entropy in the feature-extraction step of their proposed method to create an MS diagnostic tool ( Mohseni and Moghaddasi, 2022 ). See reference Li et al (2018) for more information on entropy, its measures, and variants.…”

“…Using this study's methodology, one can assess the degree of synchronization between brain networks, which is exactly the highlevel brain function they believed to be impaired (Carrubba et al, 2010). Mohseni and Moghaddasi (2022) also employed nonlinear feature extraction and classification. Through segmenting random signals in EEG signals, they proposed a model for diagnosing MS.…”

“…Through segmenting random signals in EEG signals, they proposed a model for diagnosing MS. While the alpha, beta, and gamma sub-bands have an impact on signal analysis, the robustness of the technique with random selections of 15-to-30-second segments of the EEG signal and the detecting power of the algorithm makes it appropriate for generalization (Mohseni and Moghaddasi, 2022). Jouzizadeh et al (2021) applied coherence analysis to understand the functional connectivity of MS.…”