title: Detection of Levodopa Induced Dyskinesia in Parkinson's Disease patients based on activity classification.
abstract: In this paper, we present an activity classification-based algorithm for the automatic detection of Levodopa Induced Dyskinesia in Parkinson's Disease (PD) patients. Two PD patients experiencing motor fluctuations related to chronic Levodopa therapy performed a protocol of simple daily life activities on at least two different occasions. A Random Forest classifier was able to classify the performed activities by the patients with an overall accuracy of 86%. Based on the detected activity, a K Nearest Neighbor classifier detected the presence of dyskinesia with accuracy ranging from 75% to 88%.
authors.forename: Nahed
authors.initials: N
authors.lastname: Jalloul
authors.forename: Fabienne
authors.initials: F
authors.lastname: Porée
authors.forename: Geoffrey
authors.initials: G
authors.lastname: Viardot
authors.forename: Philippe
authors.initials: P
authors.lastname: L'Hostis
authors.forename: Guy
authors.initials: G
authors.lastname: Carrault
mesh_terms: D061725:Accelerometry / Q000295:instrumentation; D000203:Activities of Daily Living / Q000145:classification*; D000368:Aged; D000465:Algorithms; D000978:Antiparkinson Agents / Q000009:adverse effects*; D003936:Diagnosis, Computer-Assisted / Q000379:methods; D004409:Dyskinesia, Drug-Induced / Q000175:diagnosis*; D005260:Female; D006801:Humans; D007980:Levodopa / Q000009:adverse effects*; D008297:Male; D008875:Middle Aged; D010300:Parkinson Disease / Q000188:drug therapy*
chemical_list: D000978:Antiparkinson Agents; D007980:Levodopa