تقرير
Forged Channel: A Breakthrough Approach for Accurate Parkinson's Disease Classification using Leave-One-Subject-Out Cross-Validation
| العنوان: | Forged Channel: A Breakthrough Approach for Accurate Parkinson's Disease Classification using Leave-One-Subject-Out Cross-Validation |
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| المؤلفون: | Hamidi, A., Mohamed-Pour, k., Yousefi, M. |
| سنة النشر: | 2023 |
| مصطلحات موضوعية: | Electrical Engineering and Systems Science - Signal Processing |
| الوصف: | 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. Comment: 5 Pages, 2 Figure, 3 Table |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2305.02234 |
| رقم الأكسشن: | edsarx.2305.02234 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |