Deep Learning Based Processing of EEG Signals for Detection and Recognition of Parkinson Disease

The aim of this study is to provide early detection of Parkinson's disease by processing EEG signals through two dimensional colored image transforms. Parkinson's disease is a neurological disease that usually occurs in old ages and occurs with a decrease in dopamine levels in the brain. There is no known treatment for Parkinson's disease. Early detection and early treatment in Parkinson's disease is very important to slow the progression of the disease. EEG data were obtained from the UC San Diego Resting State EEG Database from Patients with Parkinson's disease. EEG signals were converted to GASF images by going through various preprocessing steps. AlexNet deep learning model was used to train and test the obtained 2D colored image data. AlexNet is a Convolutional Neural Network model consisting of 8 layers. In the literature review, 16 channels used in various studies were selected. Amoung these Fp1, F7 and F3 channels are the ones with highest reported succes results. The same channels are also considered with in the scope of address in this thesis work. GASF images of selected Fp1, F7 and F3 channels were used to train the AlexNet CNN model over 100 epochs. The developed model achieved promising performance with 97.72% accuracy, 97.76% sensitivity and 97.68% specificity. In addition, the AlexNet CNN model was trained and tested over 100 epochs with 4-fold Cross Validation. As a result of this study, the developed model achieved the highest results with 97.73% accuracy, 97.94% sensitivity and 97.53% specificity.