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صفحه اصلی
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سی و چهارمین کنفرانس بین المللی مهندسی برق
Analysis Therapeutic Effect of repetitive Transcranial Magnetic Stimulation on Recovery of Patients with Multiple Sclerosis based on Human EEG Signals via Machine Learning
نویسندگان :
Ali Abedi
1
Mehran Jahed
2
1- دانشگاه صنعتی امیرکبیر(پلی تکنیک تهران)
2- دانشگاه صنعتی شریف
کلمات کلیدی :
repetitive Transcranial Magnetic Stimulation (rTMS)،Multiple Sclerosis (MS)،Support Vector Machine (SVM)،Machine Learning،Electroencephalography (EEG)
چکیده :
repetitive Transcranial Magnetic Stimulation (rTMS) is a noninvasive treatment for Multiple Sclerosis (MS), but results are inconsistent, and protocols are seldom personalized. The current paper proposes an EEG-based machine learning scheme to investigate the effects of rTMS by classifying Real Stimulation (RS) from Sham Stimulation (SS). Resting EEG was recorded pre/post 10-week rTMS in 37 MS patients (RS=19, SS=18). Features were extracted across time, frequency, time–frequency, and connectivity domains, and LASSO selected the informative features. A SVM with leave-one-out cross-validation achieved 92.57% accuracy, 90.0% specificity, 95.0% sensitivity, and AUC = 0.96, while PLV, spectral entropy, and band power featured as the most discriminative ones.
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بیشتر
ثمین همایش، سامانه مدیریت کنفرانس ها و جشنواره ها - نگارش 44.7.2
