Abstract:
BACKGROUND: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. OBJECTIVE: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: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. CONCLUSION: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.
journal_name
Artif Intell Medjournal_title
Artificial intelligence in medicineauthors
Pérez-López C,Samà A,Rodríguez-Martín D,Moreno-Aróstegui JM,Cabestany J,Bayes A,Mestre B,Alcaine S,Quispe P,Laighin GÓ,Sweeney D,Quinlan LR,Counihan TJ,Browne P,Annicchiarico R,Costa A,Lewy H,Rodríguez-Molinero Adoi
10.1016/j.artmed.2016.01.001subject
Has Abstractpub_date
2016-02-01 00:00:00pages
47-56eissn
0933-3657issn
1873-2860pii
S0933-3657(16)00003-8journal_volume
67pub_type
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