Parkinson’s Disease tremor classification – A comparison between Support Vector Machines and neural networks

Deep Brain Stimulation has been used in the study of and for treating Parkinson’s Disease (PD) tremor symptoms since the 1980s. In the research reported here we have carried out a comparative analysis to classify tremor onset based on intraoperative microelectrode recordings of a PD patient’s brain Local Field Potential (LFP) signals. In particular, we compared the performance of a Support Vector Machine (SVM) with two well known artificial neural network classifiers, namely a Multiple Layer Perceptron (MLP) and a Radial Basis Function Network (RBN). The results show that in this study, using specifically PD data, the SVM provided an overall better classification rate achieving an accuracy of 81% recognition.

► We classify a Parkinson’s Disease (PD) patient STN-LFP signal between tremor and non-tremor status. ► Tremor-frequency power spectrum density feature is selected for feature input. ► We use EMG as evaluation method to analysis the classifier results. ► Performance comparison between classifiers on a real PD patient dataset. ► Support Vector Machine have highest accuracy rate comparing with two other ANNs namely MLP and RBN.