0% Complete
صفحه اصلی
/
سی و سومین کنفرانس بین المللی مهندسی برق
Recurrence Quantification and Machine Learning: A Novel Approach for Parkinson’s Disease Diagnosis from EEG Signals
نویسندگان :
Asghar Zarei
1
Alireza Talesh Jafadideh
2
1- دانشگاه صنعتی سهند
2- دانشگاه تهران
کلمات کلیدی :
Parkinson's disease (PD)،Recurrence Quantification Analysis (RQA)،Electroencephalogram (EEG)،Machine Learning (ML)
چکیده :
Parkinson's disease (PD), the second most common neurodegenerative disorder globally, primarily involves deficiency of central nervous system dopamine. Hence, diagnosis of PD presents serious challenges and is usually a prolonged process without a standardized protocol. As a result, various studies have been conducted to find reliable biomarkers for PD. One such approach is through a characterization of EEG signal features. EEG records neuronal activity from electrodes placed on the skull, and with the advent of AI, EEG signal features have been incorporated into machine learning (ML) algorithms for assistance in automatically diagnosing neurological diseases. This suggests that EEG signals can be regarded as important biomarkers that may help discriminate PD patients from controls. In this study, we explore the potential of Recurrence Quantification Analysis (RQA) features calculated from EEG signals as biomarkers for PD. Based on publicly accessible data received from The Patient Repository for EEG Data + Computational Tools (PRED + CT), we analyzed EEG recordings of PD patients who were repeatedly submitted to auditory stimulation. We employed Support Vector Machine (SVM), K-Nearest Neighborhood (KNN), and Random Forest algorithms for the classification procedure and utilized a 10-fold cross-validation method. The proposed model achieved an average accuracy of 95.72 % separating PD patients from healthy controls using the SVM classifier. This indicates that RQA features from the EEG signals could serve as promising biomarkers for PD.
لیست مقالات
لیست مقالات بایگانی شده
Brain Tumor Segmentation Using U-net and U-net++ Networks
Seyyed Ali Mortazavi-Zadeh - Alireza Amini - Hamid Soltanian-Zadeh
A Novel Method for Partial Discharge Localization in Power Distribution Cables Using Phase Resolved Patterns
Arman Vasigh zadeh ansari - Mehdi Vakilian
طراحی و شبیه سازی شتاب سنج خازنی MEMS برای استفاده در سمعک های تمام کاشت
میلاد کریمی پور - مهدیه مهران
Application of Metaheurestic Optimization Algorithms for Feature Selection in Text Classification
Elham Nazari - Nafise Haghshenas - Alireza Basiri - Mohammad Reza Ahmadzadeh
Perfect Tracking of a Non-minimum Phase MIMO System
Saeedreza Tofighi - Farshad Merrikh-Bayat
ارائه روشی جهت بهبود عملکرد شبکههای بیسیم حسگر ناهمگون مبتنی بر برداشت انرژی
محمد فرشته حکمت - علیرضا کشاورز حداد
Bilabial Consonants Recognition in CV Persian Syllable Based on Computer Vision
Melika Khajeh - Azam Bastanfard - Dariush Amirkhani
Improving the Accuracy of the Annotation Algorithm in Pattern-Based Tennis Game Video
Azam Bastanfard - Dariush Amirkhani
A CMOS Low-Noise and Low-Power Transimpedance Amplifier
Mehrdad Amirkhan Dehkordi - Seyed Mehdi Mirsanei - Soorena Zohoori
User Identification Based on Hand Geometrical Biometrics Using Media-Pipe
Sara Ghanbari - Zahra Parvin Ashtyani - Mehdi Tale Masouleh
ثمین همایش، سامانه مدیریت کنفرانس ها و جشنواره ها - نگارش 40.4.2