Forged Channel: A Breakthrough Approach for Accurate Parkinson's Disease Classification using Leave-One-Subject-Out Cross-Validation

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 effe...

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Bibliographic Details
Main Authors: Hamidi, A., Mohamed-Pour, K., Yousefi, M.
Format: Conference Proceeding
Language:English
Subjects:
Accuracy
Brain modeling
Classification
Convolutional Neural Network
Convolutional neural networks
Deep learning
Electrical engineering
Electroencephalogram (EEG)
Electroencephalography
Parkinson's disease
Parkinson's Disease (PD)
SPWVD Transformation
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Summary: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.
ISSN:2642-9527
DOI:10.1109/ICEE63041.2024.10667765