کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
387670 | 660906 | 2012 | 8 صفحه PDF | دانلود رایگان |
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.
Journal: Expert Systems with Applications - Volume 39, Issue 12, 15 September 2012, Pages 10764–10771