title: Automatic assessment of medication states of patients with Parkinson's disease using wearable sensors.
abstract: Motor fluctuations are a major focus of clinical managements in patients with mid-stage and advance Parkinson's disease (PD). In this paper, we develop a new patient-specific algorithm that can classify those fluctuations during a variety of activities. We extract a set of temporal and spectral features from the ambulatory signals and then introduce a semi-supervised classification algorithm based on K-means and self-organizing tree map clustering methods. Two different types of cluster labeling are introduced: hard and fuzzy labeling. The developed algorithm is evaluated on a dataset from triaxial gyroscope sensors for 12 PD patients. The average result of using K-means and fuzzy labeling on the trunk and the more affected leg sensors' readings was 75.96%, 70.57%, and 86.93% for accuracy, sensitivity, and specificity, respectively. The accuracy for individual patients varied from 99.95% to 42.53%, which was correlated with dyskinesia severity and the improvement of the PD symptoms with medication.
authors.forename: Murtadha D
authors.initials: MD
authors.lastname: Hssayeni
authors.forename: Michelle A
authors.initials: MA
authors.lastname: Burack
authors.forename: Behnaz
authors.initials: B
authors.lastname: Ghoraani
mesh_terms: D000465:Algorithms; D003020:Clothing; D016903:Drug Monitoring; D020820:Dyskinesias / Q000145:classification / Q000503:physiopathology; D017143:Fuzzy Logic; D006801:Humans; D018670:Monitoring, Ambulatory / Q000295:instrumentation* / Q000379:methods*; D010300:Parkinson Disease / Q000145:classification / Q000175:diagnosis / Q000188:drug therapy / Q000503:physiopathology; D012815:Signal Processing, Computer-Assisted