Towards Longitudinal Data Analytics in Parkinson’s Disease

Nikos Fragopanagos; Stefan Kueppers; Panagiotis Kassavetis; Marco Luchini; George Roussos

eHealth 360°. International Summit on eHealth, Budapest, Hungary, June 14-16, 2016, Revised Selected Papers

Research Article

Towards Longitudinal Data Analytics in Parkinson’s Disease

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@INPROCEEDINGS{10.1007/978-3-319-49655-9_9,
    author={Nikos Fragopanagos and Stefan Kueppers and Panagiotis Kassavetis and Marco Luchini and George Roussos},
    title={Towards Longitudinal Data Analytics in Parkinson’s Disease},
    proceedings={eHealth 360°. International Summit on eHealth, Budapest, Hungary, June 14-16, 2016, Revised Selected Papers},
    proceedings_a={EHEALTH360},
    year={2017},
    month={1},
    keywords={},
    doi={10.1007/978-3-319-49655-9_9}
}

Nikos Fragopanagos
Stefan Kueppers
Panagiotis Kassavetis
Marco Luchini
George Roussos
Year: 2017
Towards Longitudinal Data Analytics in Parkinson’s Disease
EHEALTH360
Springer
DOI: 10.1007/978-3-319-49655-9_9

Nikos Fragopanagos¹, Stefan Kueppers^,*, Panagiotis Kassavetis², Marco Luchini³, George Roussos⁴

1: Retechnica Ltd.
2: Boston University
3: Benchmark Performance Ltd.
4: Birkbeck College, University of London

*Contact email: stefan@dcs.bbk.ac.uk

Abstract

The CloudUPDRS app has been developed as a Class I medical device to assess the severity of motor symptoms for Parkinson’s Disease using a fully automated data capture and signal analysis process based on the standard Unified Parkinson’s Disease Rating Scale. In this paper we report on the design and development of the signal processing and longitudinal data analytics microservices developed to carry out these assessments and to forecast the long-term development of the disease. We also report on early findings from the application of these techniques in the wild with a cohort of early adopters.

Published: 2017-01-05
Appears in: SpringerLink

: http://dx.doi.org/10.1007/978-3-319-49655-9_9