Using a deep recurrent neural network with EEG signal to detect Parkinson's disease.

Background: Parkinson's disease (PD) gradually degrades the functionality of the brain. Because of its relevance to the abnormality of the brain, electroencephalogram (EEG) signal is used for the early detection of this disease. This paper introduces a novel computer-aided diagnosis method to detect PD, which is an efficient deep learning method based on a pooling-based deep recurrent neural network (PDRNN). Therefore, the purpose of this study is to detect Parkinson's disease based on deep recurrent neural network of EEG signal.
Methods: The EEG signals of 20 patients with Parkinson's disease and 20 healthy people in Henan Provincial People's Hospital (People's Hospital of Zhengzhou University) were examined, and a PDRNN learning method was applied on the dataset for managing the demand of the traditional feature presentation step.
Results: The suggested DPRNN network gives the precision, sensitivity and specificity of 88.31%, 84.84% and 91.81%, respectively. Nevertheless, 11.28% of the healthy cases are wrongly categorized in Parkinson class. Also, 11.49% percent of Parkinson cases are classified wrongly in the healthy class.
Conclusions: The experimental model has high efficiency and can be used as a reliable tool for clinical PD detection. In future research, more cases should be used to test and develop the proposed model.
Competing Interests: Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at http://dx.doi.org/10.21037/atm-20-5100). The authors have no conflicts of interest to declare.
(2020 Annals of Translational Medicine. All rights reserved.)