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

SUBMITTER: Lee S 

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

REPOSITORIES: biostudies-literature