Pattern Identification of Subthalamic Local Field Potentials in Parkinson’s Disease

Abstract

The local field potentials (LFP) in Parkinson’s disease (PD), which contain abundant information related to disease and symptoms, are important for clinical treatment. The amplitudes of oscillations in LFP and the balance between them were found to be involved in brain functional state of PD patients. The LFP recorded from subthalamic nucleus before and after medication treatment were selected in this study. The power spectral ratio between frequencies and the percentage of energy corresponding to the wavelet packet nodes in the overall signal energy based on wavelet packet analysis related to symptoms were extracted as features. The brain states related to medication treatment in patients were classified using machine learning. The Naive Bayesian classifier and support vector machine (SVM) classifier were used to classify the states of off and on medication conditions. The results showed that the Naive Bayesian classifier was better than SVM classifier with higher accuracy. The specificity of Naive Bayesian classifier reached to 82.4%. The method proposed in this paper can accurately identify the brain functional state of PD patients.