Hybrid Convolutional Recurrent Neural Networks Outperform CNN and RNN in Task-state EEG Detection for Parkinson's Disease

Abstract

In hospitals, brain-related disorders such as Parkinson's disease (PD) could be diagnosed by analyzing electroencephalograms (EEG). However, conventional EEG-based diagnosis for PD relies on handcrafted feature extraction, which is laborious and time-consuming. With the emergence of deep learning, automated analysis of EEG signals can be realized by exploring the inherent information in data, and outputting the results of classification from the hidden layer. In the present study, four deep learning algorithm architectures, including two convention deep learning models (convolutional neural network, CNN; and recurrent neural network, RNN) and two hybrid convolutional recurrent neural networks (2D-CNN-RNN and 3D-CNN-RNN), were designed to detect PD based on task-state EEG signals. Our results showed that the hybrid models outperformed conventional ones (fivefold average accuracy: 3D-CNN-RNN 82.89%, 2D-CNN-RNN 81.13%, CNN 80.89%, and RNN 76.00%) as they combine the strong modeling power of CNN in temporal feature extraction, and the advantage of RNN in processing sequential information. This study represents the an attempt to use hybrid convolutional recurrent neural networks in classifying PD and normal take-state EEG signals, which carries important implications to the clinical practice.