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A convolutional-recurrent neural network approach to resting-state EEG classification in Parkinsons disease

By Soojin Lee, Ramy Hussein, Rabab Ward, Jane Z Wang, Martin J McKeown

Posted 16 Jun 2021
medRxiv DOI: 10.1101/2021.06.10.21258677

Background: Parkinsons disease (PD) is expected to become more common, particularly with an aging population. Diagnosis and monitoring of the disease typically rely on the laborious examination of physical symptoms by medical experts, which is necessarily limited and may not detect the prodromal stages of the disease. New Method: We propose a lightweight (20K parameters) deep learning model, to discriminate between resting-state EEG recorded from people with PD and healthy controls. The proposed CRNN model consists of convolutional neural networks (CNN) and a recurrent neural network (RNN) with gated recurrent units (GRUs). The 1D CNN layers are designed to extract spatiotemporal features across EEG channels, which are subsequently supplied to the GRUs to discover temporal features pertinent to the classification. Results: The CRNN model achieved 99.2% accuracy, 98.9% precision, and 99.4% recall in classifying PD from healthy controls (HC). Interrogating the model, we further demonstrate that the model is sensitive to dopaminergic medication effects and predominantly uses phase information of the EEG signals. Comparison with Existing Methods: The CRNN model achieves superior performance compared to baseline machine learning methods and other recently proposed deep learning models. Conclusion: The approach proposed in this study adequately extracts the spatial and temporal features in multi-channel EEG signals that enable the accurate differentiation between PD and HC. It has excellent potential for use as an oscillatory biomarker for assisting in the diagnosis and monitoring of people with PD. Future studies to further improve and validate the model performance in clinical practice are warranted.

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