A Convolutional Recurrent Neural Network Model for Classification of Parkinson’s Disease from Resting State Multi-channel EEG Signals-Reference-Cited by-同舟云学术

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A Convolutional Recurrent Neural Network Model for Classification of Parkinson’s Disease from Resting State Multi-channel EEG Signals

Published:2023 Issue: Volume: Page:136-146
ISSN:1865-0929
Container-title:Advances in Computational Collective Intelligence
language:
Short-container-title:

Author:

Salah Fatma^ORCID,Guesmi Dhouha^ORCID,Ayed Yassine Ben^ORCID

Publisher

Springer Nature Switzerland

Link

https://link.springer.com/content/pdf/10.1007/978-3-031-41774-0_11

Reference16 articles.

1. Azevedo, F.A.C., et al.: Equal numbers of neuronal and nonneuronal cells make the human brain an isometrically scaled-up primate brain. J. Comp. Neurol. 513(5), 532–541 (2009). https://doi.org/10.1002/cne.21974

2. Jankovic, J.: Parkinson’s disease: clinical features and diagnosis. J. Neurol. Neurosurg. Psychiatr. 79(4), 368–376 (2008)

3. Parkinson’s Disease Foundation, https://www.parkinson.org/Understanding-Parkinsons/Statistics, last accessed 11 October 2022

4. Salvatore, C., et al.: Machine learning on brain MRI data for differential diagnosis of Parkinson’s disease and Progressive Supranuclear Palsy. J. Neurosci. Methods 222, 230–237 (2014). https://doi.org/10.1016/j.jneumeth.2013.11.016

5. Pizarro, R.A., et al.: Automated quality assessment of structural magnetic resonance brain images based on a supervised machine learning algorithm. Front. Neuroinform. 10, 52 (2016). https://doi.org/10.3389/fninf.2016.00052

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1. Deep Learning for Parkinson's Disease Detection: An Analytical Study;2024 14th International Conference on Cloud Computing, Data Science & Engineering (Confluence);2024-01-18

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