Supervised classification of bradykinesia for Parkinson's disease diagnosis from smartphone videos

David C. Wong, Samuel D. Relton, Hui Fang, Rami Qhawaji, Christopher D. Graham, Jane Alty, Stefan Williams

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Citations (Scopus)

Abstract

Slowness of movement, known as bradykinesia, is an important early symptom of Parkinson's disease. This symptom is currently assessed subjectively by clinical experts. However, expert assessment has been shown to be subject to inter-rater variability. We propose a low-cost, contactless system using smarthphone videos to automatically determine the presence of bradykinesia. Using 70 videos recorded in a pilot study, we predicted the presence of bradykinesia with an estimated test accuracy of 0.79 and the presence of Parkinson's disease with estimated test accuracy 0.63. Even on a small set of pilot data this accuracy is comparable to that recorded by blinded human experts.

Original languageEnglish
Title of host publicationProceedings - 2019 IEEE 32nd International Symposium on Computer-Based Medical Systems, CBMS 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages32-37
Number of pages6
Volume2019-June
ISBN (Electronic)9781728122861
DOIs
Publication statusPublished - 01 Jun 2019
Event32nd IEEE International Symposium on Computer-Based Medical Systems, CBMS 2019 - Cordoba, Spain
Duration: 05 Jun 201907 Jun 2019

Conference

Conference32nd IEEE International Symposium on Computer-Based Medical Systems, CBMS 2019
CountrySpain
CityCordoba
Period05/06/201907/06/2019

Keywords

  • Bradykinesia
  • Classification
  • Computer Vision
  • Diagnosis
  • Parkinson's
  • Support Vector Machine
  • Video

ASJC Scopus subject areas

  • Radiology Nuclear Medicine and imaging
  • Computer Science Applications

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