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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2023
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2305.02234 |
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| _version_ | 1866910412604178432 |
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| author | Hamidi, A. Mohamed-Pour, k. Yousefi, M. |
| author_facet | Hamidi, A. Mohamed-Pour, k. Yousefi, M. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2305_02234 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Forged Channel: A Breakthrough Approach for Accurate Parkinson's Disease Classification using Leave-One-Subject-Out Cross-Validation Hamidi, A. Mohamed-Pour, k. Yousefi, M. Signal Processing 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. |
| title | Forged Channel: A Breakthrough Approach for Accurate Parkinson's Disease Classification using Leave-One-Subject-Out Cross-Validation |
| topic | Signal Processing |
| url | https://arxiv.org/abs/2305.02234 |