An Automated Method for Levodopa-Induced Dyskinesia Detection and Severity Classification

In this paper we propose an automated method for Levodopa-induced dyskinesia (LID) detection and classifi- cation of its severity. The method is based on the analysis of the signals recorded from accelerometers which are placed on certain positions on the patient's body. The signals are ana- lyzed using a moving window and several features are ex- tracted. Based on these features a decision tree is used to detect if LID symptoms occur and classify them related to their se- verity. The method has been evaluated using a group of pa- tients and the obtained results indicate high classification ability (95% classification accuracy). Furthermore, extensive evaluation has been done in order to determine the optimal positioning of the sensors and the selection of the classification algorithm.

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