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سی و دومین کنفرانس بین المللی مهندسی برق
Forged Channel: A Breakthrough Approach for Accurate Parkinson's Disease Classification using Leave-One-Subject-Out Cross-Validation
نویسندگان :
SeyedAmirReza Hamidi
1
Kamal Mohamed-Pour
2
Mohammad Yousefi
3
1- دانشگاه صنعتی خواجه نصیرالدین طوسی
2- دانشگاه صنعتی خواجه نصیرالدین طوسی
3- دانشگاه صنعتی خواجه نصیرالدین طوسی
کلمات کلیدی :
Parkinson's Disease (PD)،Deep Learning،Convolutional Neural network،SPWVD Transformation،Electroencephalogram (EEG)،Classification
چکیده :
This paper introduces a novel technique called "Forged Channel," which aims to comprehensively represent EEG signals in order to achieve accurate classification of Parkinson's disease. The forged channel method prepares EEG signals in a manner that allows a deep learning model to effectively perceive all EEG channels within a single input. By employing this approach alongside a convolutional neural network, an impressive accuracy of 90.32% was achieved using leave-one-subject-out cross-validation. This performance closely reflects real-world conditions, highlighting the superiority of our method compared to similar approaches.
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بیشتر
ثمین همایش، سامانه مدیریت کنفرانس ها و جشنواره ها - نگارش 44.7.2
