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A heterogeneous sensing suite for multisymptom quantification of Parkinson’s disease
File | Description | Size | Format | |
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Vaidyanathan_TNSRE_10pp_2020.pdf | Accepted version | 638.16 kB | Adobe PDF | View/Open |
Title: | A heterogeneous sensing suite for multisymptom quantification of Parkinson’s disease |
Authors: | Huo, W Angeles, P Tai, YF Pavese, N Wilson, S Hu, MT Vaidyanathan, R |
Item Type: | Journal Article |
Abstract: | Parkinson’s disease (PD) is the second most common neurodegenerative disease affecting millions worldwide. Bespoke subject-specific treatment (medication or deep brain stimulation (DBS)) is critical for management, yet depends on precise assessment cardinal PD symptoms - bradykinesia, rigidity and tremor. Clinician diagnosis is the basis of treatment, yet it allows only a cross-sectional assessment of symptoms which can vary on an hourly basis and is liable to inter- and intra-rater subjectivity across human examiners. Automated symptomatic assessment has attracted significant interest to optimise treatment regimens between clinician visits, however, no wearable has the capacity to simultaneously assess all three cardinal symptoms. Challenges in the measurement of rigidity, mapping muscle activity outof-clinic and sensor fusion have inhibited translation. In this study, we address all through a novel wearable sensor system and learning algorithms. The sensor system is composed of a force-sensor, two inertial measurement units (IMUs) and four custom mechanomyography (MMG) sensors. The system was tested in its capacity to predict Unified Parkinson’s Disease Rating Scale (UPDRS) scores based on quantitative assessment of bradykinesia, rigidity and tremor in PD patients. 23 PD patients were tested with the sensor system in parallel with exams conducted by treating clinicians and 10 healthy subjects were recruited as a comparison control group. Results prove the system accurately predicts UPDRS scores for all symptoms (85.4% match on average with physician assessment) and discriminates between healthy subjects and PD patients (96.6% on average). MMG features can also be used for remote monitoring of severity and fluctuations in PD symptoms out-of-clinic. This closedloop feedback system enables individually tailored and regularly updated treatment, facilitating better outcomes for a very large patient population. |
Issue Date: | 1-Jun-2020 |
Date of Acceptance: | 10-Feb-2020 |
URI: | http://hdl.handle.net/10044/1/77054 |
DOI: | 10.1109/TNSRE.2020.2978197 |
ISSN: | 1534-4320 |
Publisher: | Institute of Electrical and Electronics Engineers |
Start Page: | 1397 |
End Page: | 1406 |
Journal / Book Title: | IEEE Transactions on Neural Systems and Rehabilitation Engineering |
Volume: | 28 |
Issue: | 6 |
Copyright Statement: | This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/. |
Sponsor/Funder: | Engineering & Physical Science Research Council (E Engineering & Physical Science Research Council (EPSRC) Office Of Naval Research Global |
Funder's Grant Number: | EP/R511547/1 EP/K503381/1 N62909-14-1-N221 |
Keywords: | Science & Technology Technology Life Sciences & Biomedicine Engineering, Biomedical Rehabilitation Engineering Parkinson's disease symptoms wearable sensor system machine learning MMG telemedicine muscle stiffness DEEP-BRAIN-STIMULATION OBJECTIVE ASSESSMENT MOTOR SYMPTOMS TREMOR RIGIDITY SYSTEM CLASSIFICATION BRADYKINESIA 0903 Biomedical Engineering 0906 Electrical and Electronic Engineering Biomedical Engineering |
Publication Status: | Published |
Open Access location: | https://ieeexplore.ieee.org/document/9064818 |
Online Publication Date: | 2020-04-13 |
Appears in Collections: | Mechanical Engineering Faculty of Medicine Department of Brain Sciences Faculty of Engineering |