PDCNNet: An Automatic Framework for the Detection of Parkinson’s Disease Using EEG Signals

Abstract

Parkinson's disease (PD) is a neurodegenerative ailment which causes changes in the neuronal, behavioral, and physiological structures. During the early stages of PD, these changes are very subtle and hence accurate diagnosis is challenging. Pathological and neurological experts assess the PD patients by examining their drawing, writing, walking, tremor, facial expressions, and speech. The manual analysis performed by specialists is time-consuming and prone to errors. Electroencephalogram (EEG) signals represent changes in the brain activities, but it is difficult to manually analyze these signals due to their non-linear, non-stationary, and complex nature. Traditional machine learning methods require several manual steps, such as decomposition, extraction of features, and classification. To overcome these limitations, automated PD detection using smoothed pseudo-Wigner Ville distribution (SPWVD) coupled with convolutional neural networks (CNN) called Parkinson's disease CNN (PDCNNet) is proposed. First the EEG signals are subjected to SPWVD to obtain time-frequency representation (TFR). Then these two-dimensional plots are fed to an CNN. The proposed model is developed using two public databases. We have obtained an accuracy of 100% and 99.97% for dataset 1 and 2, respectively in detecting PD automatically using our proposed PDCNNet model. Our developed prototype has outperformed all existing state-of-the-art techniques and ready to be validated with more diverse datasets.