Continuously Identifying Dyskinesia Periods in Parkinson's Disease with Body-Worn Sensors
Parkinson’s disease (PD) is a progressive neurodegenerative disorder that causes both motor and nonmotor deficits, which can be clinically observed and regulated with treatment. Motor symptoms include akinesia, bradykinesia, impaired balance, and tremor. Deficits typically fluctuate in severity on an hourly or daily basis. Time periods characterized by severe deficits are referred to as “off” periods, while periods of relatively normal function are considered “on” periods. Clinicians treat the symptoms of PD through the combined administration of Carbidopa/Levodopa medication, dopamine agonist medication, and voltage-controlled deep brain stimulation (DBS) devices. Overmedication or overstimulation can cause dyskinesia, an involuntary, often rhythmic or choreic, exaggeration of movements. As medication doses are increased with disease progression, dyskinesias become more prevalent. Clinicians aim to reduce both off and dyskinesia periods by prescribing the correct balance of medication and/or DBS treatments. Clinicians investigate the prevalence of dyskinesia, in part, by asking patients to retrospectively self-report the frequency of their dyskinesia periods over the several months prior to their clinical visit, a method subject to recall bias. Our goal in this study was to develop an objective system that identifies dyskinesia periods occurring over a 1-2 hour observational period from body-worn accelerometer data using signal analysis, feature extraction, and machine learning algorithms. We are using the results from this study to design a continuous system that automatically generates a time history of off and dyskinesia periods, which clinicians could use to optimize their prescription of PD treatments.