Dataset Information

A convolutional-recurrent neural network approach to resting-state EEG classification in Parkinson's disease.

ABSTRACT:

Background

Parkinson's 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 (~20 K parameters) deep learning model to classify resting-state EEG recorded from people with PD and healthy controls (HC). 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 HC. Interrogating the model, we further demonstrate that the model is sensitive to dopaminergic medication effects and predominantly uses phase information in 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 model.

Conclusion

The approach proposed in this study adequately extracts spatial and temporal features in multi-channel EEG signals that enable accurate differentiation between PD and HC. The CRNN model 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's performance in clinical practice are warranted.

SUBMITTER: Lee S

PROVIDER: S-EPMC8528094 | biostudies-literature | 2021 Sep

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

A convolutional-recurrent neural network approach to resting-state EEG classification in Parkinson's disease.

Lee Soojin S Hussein Ramy R Ward Rabab R Jane Wang Z Z McKeown Martin J MJ

Journal of neuroscience methods 20210706

<h4>Background</h4>Parkinson's 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.<h4>New method</h4>We propose a lightweight (~20 K parameters) deep learning model to classify resting-state EEG recorded from people with PD and healthy controls (HC). The ...[more]

PMID: 34237382

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Dataset Information

A convolutional-recurrent neural network approach to resting-state EEG classification in Parkinson's disease.

Background

New method

Results

Comparison with existing methods

Conclusion

Publications

A convolutional-recurrent neural network approach to resting-state EEG classification in Parkinson's disease.

Similar Datasets

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