Department of Information Technology, Usak University, Usak, 64100, Türkiye.
Department of Electrical and Electronics Engineering, Kutahya Dumlupinar University, Kutahya, 43000, Türkiye.
Comput Biol Med. 2024 Aug;178:108728. doi: 10.1016/j.compbiomed.2024.108728. Epub 2024 Jun 8.
Multiple sclerosis (MS) is a neurodegenerative autoimmune disease affecting the central nervous system, leading to various neurological symptoms. Early detection is paramount to prevent enduring damage during MS episodes. Although magnetic resonance imaging (MRI) is a common diagnostic tool, this study aims to explore the feasibility of using electroencephalography (EEG) signals for MS detection, considering their accessibility and ease of application compared to MRI.
The study involved the analysis of EEG signals during rest from 17 MS patients and 27 healthy volunteers to investigate MS-healthy patterns. Power spectral density features (PSD) were extracted from the 32-channel EEG signals. The study employed Linear Discriminant Analysis (LDA), Support Vector Machine (SVM), Classification and Regression Trees (CART), and k-Nearest Neighbor (kNN) classifiers to identify channels with the highest accuracy. Notably, the study achieved 100% accuracy in MS detection using the "Fp1" and "Pz" channels with the LDA classifier. A statistical analysis, utilizing the independent sample t-test, was conducted to explore whether PSD features of these channels differed significantly between healthy individuals and those with MS.
The results of the study demonstrate that effective detection of MS can be achieved using PSD features from only two channels of the EEG signal. Specifically, the "Fp1" and "Pz" channels exhibited 100% accuracy in MS detection with the LDA classifier. The statistical analysis further explored and confirmed the significant differences in PSD features between healthy individuals and MS patients.
The study concludes that the proposed method, utilizing PSD features from specific EEG channels, offers a straightforward and efficient diagnostic approach for the effective detection of MS. The findings suggest the potential utility of EEG signals as a non-invasive and accessible alternative for MS detection, highlighting the importance of further research in this direction.
多发性硬化症（MS）是一种影响中枢神经系统的神经退行性自身免疫性疾病，导致各种神经症状。早期发现对于防止 MS 发作期间的持续损伤至关重要。尽管磁共振成像（MRI）是一种常见的诊断工具，但本研究旨在探索使用脑电图（EEG）信号进行 MS 检测的可行性，因为与 MRI 相比，EEG 信号具有更高的可用性和应用便利性。
该研究分析了 17 名 MS 患者和 27 名健康志愿者在休息时的 EEG 信号，以研究 MS-健康模式。从 32 通道 EEG 信号中提取了功率谱密度特征（PSD）。研究采用线性判别分析（LDA）、支持向量机（SVM）、分类回归树（CART）和 k-最近邻（kNN）分类器来识别具有最高准确性的通道。值得注意的是，使用 LDA 分类器，通过“Fp1”和“Pz”通道可实现 MS 检测的 100%准确率。通过独立样本 t 检验进行了统计分析，以探讨这些通道的 PSD 特征在健康个体和 MS 患者之间是否存在显著差异。
研究结果表明，仅使用 EEG 信号的两个通道的 PSD 特征即可实现 MS 的有效检测。具体来说，LDA 分类器在“Fp1”和“Pz”通道上实现了 MS 检测的 100%准确率。统计分析进一步探讨并证实了健康个体和 MS 患者之间 PSD 特征的显著差异。
该研究得出结论，利用特定 EEG 通道的 PSD 特征的提出方法为 MS 的有效检测提供了一种简单而有效的诊断方法。研究结果表明，EEG 信号作为一种非侵入性和易于获取的 MS 检测替代方法具有潜力，突出了进一步研究的重要性。
