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

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 si...

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Bibliographic Details
Published in:IEEE sensors journal 2021-05, Vol.21 (10), p.11916-11925
Main Authors: Kovalenko, Ekaterina, Talitckii, Aleksandr, Anikina, Anna, Shcherbak, Aleksei, Zimniakova, Olga, Semenov, Maksim, Bril, Ekaterina, Dylov, Dmitry V., Somov, Andrey
Format: Article
Language:English
Subjects:
Classification
Diseases
Essential tremor
Feasibility studies
feature engineering
Feature extraction
Health care facilities
Indexing
Kinematics
Machine learning
Parkinson's disease
Physicians
Sensors
Signs and symptoms
Task analysis
Tremor (Muscular contraction)
Tremors
Video data
video processing
Wearable sensors
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Summary: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.
ISSN:1530-437X
1558-1748
DOI:10.1109/JSEN.2020.3035240