Detection of early stages of Parkinson's disease by analyzing fMRI data and machine learning approaches

Abstract

Parkinson's Disease is a progressive nervous system brain disorder which affects motor neuron loss control and movement coordination. Parkinson's symptoms are shown gradually and get worse over time. Its signs and symptoms can be different for everyone. There may be minor early signs and they may go unnoticed. Therefore, early detection of Parkinson's disease might significantly improve life style by giving proper treatment. Moreover, doctors may suggest regulating certain regions of your brain and improve the symptoms. In recent years, the use of Functional Imaging in neurodegenerative diseases has increased, with applications in basic pathophysiology research, support in determination, or evaluation of new medications. In our research we used fMRI data of eight early PD patients. Resting-state fMRI images were collected for analyzing the data and feature extraction. Time series data were generated for each subject based on voxel intensity. In addition, STFT was used to measure the time frequency function. Furthermore, SVM classifier was used for the classification and prediction of the early stage of PD. Using our proposed method, we have achieved 100% sensitivity, specificity, and accuracy considering seven subjects, however, one subject was exceptional whereas we have achieved 99.76% accuracy, 100% specificity and 99.53% sensitivity. Finally, this process is a well-structured model for predicting the early stages of PD. It may help to the doctors for diagnosis of the disease at its early stages and the patients should receive better treatment.