Distinguishing Between Parkinson’s Disease and Essential Tremor Through Video Analytics Using Machine Learning: A Pilot Study: a Pilot Study

Ekaterina Kovalenko, Aleksandr Talitckii, Anna Anikina, Aleksei Shcherbak, Olga Zimniakova, Maxim Semenov, Ekaterina Bril, Dmitry V. Dylov, Andrey Somov

Research output: Contribution to journalArticlepeer-review

6 Citations (Scopus)

Abstract

Parkinson's Disease (PD) is currently the fastest growing neurodegenerative disease. It decreases the quality of life for patients, especially when not diagnosed properly and timely. Accurate diagnostic of PD is complicated by the fact that there exist several neurodegenerative diseases with similar motor symptoms, e.g. essential tremor. In this work, we report on a second opinion system based on the video analysis and classification of subjects using machine learning methods including feature extraction, dimensionality reduction and classification. Our approach serves for avoiding a typical misdiagnosis of PD by essential tremor. Consequently, we designed 15 common tasks and recorded the movement video. Video data was collected from 89 subjects at a medical center and labeled by doctors. We first demonstrate classification between the healthy subjects and subjects with PD suspected case followed by the classification between the subjects with true PD and the subjects with essential tremor. We achieved f1 score 0.90 for the first classification and f1 score 0.84 for the second classification. The proposed unobtrusive approach demonstrated its feasibility through a pilot study. It opens up wide vista for differentiating PD patients against other patients and not against a cohort of healthy subjects.

Original languageEnglish
Article number9246541
Pages (from-to)11916-11925
Number of pages10
JournalIEEE Sensors Journal
Volume21
Issue number10
DOIs
Publication statusPublished - 15 May 2021

Keywords

  • Essential tremor
  • Feature engineering
  • Machine learning
  • Parkinson's disease
  • Video processing

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