Diagnosis of Parkinson’s disease using EEG and fMRI

Abstract

Parkinson’s disease (PD) is a brain disorder that leads to shaking, stiffness, and difficulty with walking, balance, and coordination. Parkinson’s symptoms usually begin gradually and get worse over time. We have developed a framework to find solution for early diagnose of PD by investigating the topological properties of functional brain networks within fMRI and EEG Signals. After the construction partial correlation matrices of 160 regions from Dosenbach brain from fMRI image, six features were extracted. As well as extracted five features from EEG signals and these 11 inputs were given as input to adaboost classifier. This system has produced 93.45% accuracy and the outcome is significantly higher accuracy when compared to earlier works.