On automated assessment of Levodopa-induced dyskinesia in Parkinson´s disease

A method for the analysis of accelerometer and gyroscope signals in order to automatically assess the Levodopa-induced dyskinesia (LID) in patients with Parkinson´s disease is presented in this paper. Several accelerometers and gyroscopes are placed on certain positions on the subject´s body and the obtained signals are analyzed in order to extract several features that depict the energy distribution over the frequency spectrum and the non-linear properties of the signal. These features are fed into an artificial neural network which is used for LID detection and severity classification. The method has been evaluated using a group of 29 subjects. Results are presented related to the body locations that the accelerometers and the gyroscopes are placed. The obtained results indicate high classification ability (84.3% average classification accuracy).