Browse by author
Lookup NU author(s): Dr James FisherORCiD, Dr Nils Hammerla, Dr Thomas Ploetz, Dr Peter Andras, Professor Lynn RochesterORCiD, Professor Richard Walker
Full text for this publication is not currently held within this repository. Alternative links are provided below where available.
© 2016 Elsevier Ltd Introduction Current PD assessment methods have inherent limitations. There is need for an objective method to assist clinical decisions and to facilitate evaluation of treatments. Accelerometers, and analysis using artificial neural networks (ANN), have shown potential as a method of motor symptom evaluation. This work describes the development of a novel PD disease state detection system informed by algorithms based on data collected in an unsupervised, home environment. We evaluated whether this approach can reproduce patient-completed symptom diaries and clinical assessment of disease state. Methods 34 participants with PD wore bilateral wrist-worn accelerometers for 4 h in a research facility (phase 1) and for 7 days at home whilst completing symptom diaries (phase 2). An ANN to predict disease state was developed based on home-derived accelerometer data. Using a leave-one-out approach, ANN performance was evaluated against patient-completed symptom diaries and against clinician rating of disease state. Results In the clinical setting, specificity for dyskinesia detection was extremely high (0.99); high specificity was also demonstrated for home-derived data (0.93), but with low sensitivity (0.38). In both settings, sensitivity for on/off detection was sub-optimal. ANN-derived values of the proportions of time in each disease state showed strong, significant correlations with patient-completed symptom diaries. Conclusion Accurate, real-time evaluation of symptoms in an unsupervised, home environment, with this sensor system, is not yet achievable. In terms of the amounts of time spent in each disease state, ANN-derived results were comparable to those of symptom diaries, suggesting this method may provide a valuable outcome measure for medication trials.
Author(s): Fisher JM, Hammerla NY, Ploetz T, Andras P, Rochester L, Walker RW
Publication type: Article
Publication status: Published
Journal: Parkinsonism and Related Disorders
Year: 2016
Volume: 33
Pages: 44-50
Print publication date: 01/12/2016
Online publication date: 09/09/2016
Acceptance date: 06/09/2016
ISSN (print): 1353-8020
ISSN (electronic): 1873-5126
Publisher: Elsevier Ltd
URL: http://doi.org/10.1016/j.parkreldis.2016.09.009
DOI: 10.1016/j.parkreldis.2016.09.009
Altmetrics provided by Altmetric
