Artificial Intelligence in Medicine – February 2016
Dopaminergic-induced dyskinesia assessment based on a single belt-worn accelerometer
Carlos Pérez-López, Albert Samà, Daniel Rodríguez-Martína, Juan Manuel Moreno-Aróstegui, Joan Cabestany, Angels Bayes, Berta Mestre, Sheila Alcaine, Paola Quispe, Gearóid Ó. Laighin, Dean Sweeney, Leo R. Quinlan, Timothy J. Counihan, Patrick Browne, Roberta Annicchiarico, Alberto Costaf, Hadas Lewy, Alejandro Rodríguez-Molinero, “Dopaminergic-induced dyskinesia assessment based on a single belt-worn accelerometer”, Artificial Intelligence in Medicine, Vol. 67, pp. 47-56, 2016. DOI: 10.1016/j.artmed.2016.01.001
After several years of treatment, patients with Parkinson’s disease (PD) tend to have, as a side effect of the medication, dyskinesias. Close monitoring may benefit patients by enabling doctors to tailor a personalised medication regimen. Moreover, dyskinesia monitoring can help neurologists make more informed decisions in patient’s care.
To design and validate an algorithm able to be embedded into a system that PD patients could wear during their activities of daily living with the purpose of registering the occurrence of dyskinesia in real conditions.
Materials and methods
Data from an accelerometer positioned in the waist are collected at the patient’s home and are annotated by experienced clinicians. Data collection is divided into two parts: a main database gathered from 92 patients used to partially train and to evaluate the algorithms based on a leave-one-out approach and, on the other hand, a second database from 10 patients which have been used to also train a part of the detection algorithm.
Results show that, depending on the severity and location of dyskinesia, specificities and sensitivities higher than 90% are achieved using a leave-one-out methodology. Although mild dyskinesias presented on the limbs are detected with 95% specificity and 39% sensitivity, the most important types of dyskinesia (any strong dyskinesia and trunk mild dyskinesia) are assessed with 95% specificity and 93% sensitivity.
The presented algorithmic method and wearable device have been successfully validated in monitoring the occurrence of strong dyskinesias and mild trunk dyskinesias during activities of daily living.