title: Automated Levodopa-induced dyskinesia assessment.
abstract: An automated methodology for Levodopa-induced dyskinesia (LID) assessment is presented in this paper. The methodology is based on the analysis of the signals recorded from accelerometers and gyroscopes, which are placed on certain positions on the subject's body. The obtained signals are analyzed and several features are extracted. Based on these features a classification technique is used for LID detection and classification of its severity. The method has been evaluated using a group of 10 subjects. Results are presented related to each individual sensor as well as for various sensor combinations. The obtained results indicate high classification ability (93.73% classification accuracy).
authors.affiliation: Unit of Medical Technology and Intelligent Information Systems, Dept. of Materials Science and Engineering, University of Ioannina, GR45110, Greece. markos@cs.uoi.gr
authors.forename: Markos G
authors.initials: MG
authors.lastname: Tsipouras
authors.forename: Alexandros T
authors.initials: AT
authors.lastname: Tzallas
authors.forename: Georgios
authors.initials: G
authors.lastname: Rigas
authors.forename: Panagiota
authors.initials: P
authors.lastname: Bougia
authors.forename: Dimitrios I
authors.initials: DI
authors.lastname: Fotiadis
authors.forename: Spyridon
authors.initials: S
authors.lastname: Konitsiotis
mesh_terms: D000054:Acceleration; D000465:Algorithms; D000978:Antiparkinson Agents / Q000494:pharmacology; D001331:Automation; D015374:Biosensing Techniques; D004409:Dyskinesia, Drug-Induced / Q000503:physiopathology*; D004867:Equipment Design; D006801:Humans; D007980:Levodopa / Q000494:pharmacology*; D015233:Models, Statistical; D018670:Monitoring, Ambulatory / Q000295:instrumentation* / Q000379:methods; D010300:Parkinson Disease / Q000175:diagnosis; D011381:Programming Languages; D015203:Reproducibility of Results; D012815:Signal Processing, Computer-Assisted
chemical_list: D000978:Antiparkinson Agents; D007980:Levodopa