In:
Scientific Reports, Springer Science and Business Media LLC, Vol. 10, No. 1 ( 2020-04-03)
Abstract:
Patients with advanced Parkinson’s disease regularly experience unstable motor states. Objective and reliable monitoring of these fluctuations is an unmet need. We used deep learning to classify motion data from a single wrist-worn IMU sensor recording in unscripted environments. For validation purposes, patients were accompanied by a movement disorder expert, and their motor state was passively evaluated every minute. We acquired a dataset of 8,661 minutes of IMU data from 30 patients, with annotations about the motor state (OFF,ON, DYSKINETIC) based on MDS-UPDRS global bradykinesia item and the AIMS upper limb dyskinesia item. Using a 1-minute window size as an input for a convolutional neural network trained on data from a subset of patients, we achieved a three-class balanced accuracy of 0.654 on data from previously unseen subjects. This corresponds to detecting the OFF, ON, or DYSKINETIC motor state at a sensitivity/specificity of 0.64/0.89, 0.67/0.67 and 0.64/0.89, respectively. On average, the model outputs were highly correlated with the annotation on a per subject scale (r = 0.83/0.84; p 〈 0.0001), and sustained so for the highly resolved time windows of 1 minute (r = 0.64/0.70; p 〈 0.0001). Thus, we demonstrate the feasibility of long-term motor-state detection in a free-living setting with deep learning using motion data from a single IMU.
Type of Medium:
Online Resource
ISSN:
2045-2322
DOI:
10.1038/s41598-020-61789-3
Language:
English
Publisher:
Springer Science and Business Media LLC
Publication Date:
2020
detail.hit.zdb_id:
2615211-3
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