title: An automated methodology for levodopa-induced dyskinesia: assessment based on gyroscope and accelerometer signals.
abstract: OBJECTIVE
  In this study, a methodology is presented for an automated levodopa-induced dyskinesia (LID) assessment in patients suffering from Parkinson's disease (PD) under real-life conditions.
  
  
  METHODS
  The methodology is based on the analysis of signals recorded from several accelerometers and gyroscopes, which are placed on the subjects' body while they were performing a series of standardised motor tasks as well as voluntary movements. Sixteen subjects were enrolled in the study. The recordings were analysed in order to extract several features and, based on these features, a classification technique was used for LID assessment, i.e. detection of LID symptoms and classification of their severity.
  
  
  RESULTS
  The results were compared with the clinical annotation of the signals, provided by two expert neurologists. The analysis was performed related to the number and topology of sensors used; several different experimental settings were evaluated while a 10-fold stratified cross validation technique was employed in all cases. Moreover, several different classification techniques were examined. The ability of the methodology to be generalised was also evaluated using leave-one-patient-out cross validation. The sensitivity and positive predictive values (average for all LID severities) were 80.35% and 76.84%, respectively.
  
  
  CONCLUSIONS
  The proposed methodology can be applied in real-life conditions since it can perform LID assessment in recordings which include various PD symptoms (such as tremor, dyskinesia and freezing of gait) of several motor tasks and random voluntary movements.
authors.affiliation: Unit of Medical Technology and Intelligent Information Systems, Dept. of Materials Science and Engineering, University of Ioannina, GR 45110 Ioannina, Greece.
authors.forename: Markos G
authors.initials: MG
authors.lastname: Tsipouras
authors.forename: Alexandros T
authors.initials: AT
authors.lastname: Tzallas
authors.forename: George
authors.initials: G
authors.lastname: Rigas
authors.forename: Sofia
authors.initials: S
authors.lastname: Tsouli
authors.forename: Dimitrios I
authors.initials: DI
authors.lastname: Fotiadis
authors.forename: Spiros
authors.initials: S
authors.lastname: Konitsiotis
mesh_terms: D000368:Aged; D001331:Automation; D015374:Biosensing Techniques / Q000379:methods*; D004409:Dyskinesia, Drug-Induced / Q000175:diagnosis* / Q000209:etiology / Q000503:physiopathology; D006801:Humans; D007980:Levodopa / Q000009:adverse effects* / Q000494:pharmacology; D008875:Middle Aged; D018670:Monitoring, Ambulatory / Q000379:methods*; D010300:Parkinson Disease / Q000150:complications / Q000188:drug therapy; D011237:Predictive Value of Tests; D012720:Severity of Illness Index
chemical_list: D007980:Levodopa