Avoiding Misdiagnosis of Parkinson's Disease With the Use of Wearable Sensors and Artificial Intelligence

Parkinson's Disease (PD) is a neurodegenerative disease associated with the extrapyramidal motor system disorder currently being the second most common neurodegenerative disorder. The first clinical symptoms can manifest themselves long before the retirement age and inevitably lead to reducing...

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Bibliographic Details
Published in:IEEE sensors journal 2021-02, Vol.21 (3), p.3738-3747
Main Authors: Talitckii, Aleksandr, Kovalenko, Ekaterina, Anikina, Anna, Zimniakova, Olga, Semenov, Maksim, Bril, Ekaterina, Shcherbak, Aleksei, Dylov, Dmitry V., Somov, Andrey
Format: Article
Language:English
Subjects:
Artificial intelligence
Bradykinesia
Classification
Data analysis
Diagnostic systems
Disease
Diseases
Feature extraction
intelligent sensing
Internet of Things
Machine learning
Parkinson's disease
Retirement
Sensors
Signs and symptoms
Task analysis
Tremors
Wearable sensors
Wearable technology
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Summary:Parkinson's Disease (PD) is a neurodegenerative disease associated with the extrapyramidal motor system disorder currently being the second most common neurodegenerative disorder. The first clinical symptoms can manifest themselves long before the retirement age and inevitably lead to reducing the possibility of continuing work. However, PD is sometimes misdiagnosed. In this article, we discuss the typical misdiagnosed cases and propose a second opinion system based on wearable sensors and artificial intelligence. For this reason, we designed a number of common tasks and recorded the movement data using wearable sensors worn by individuals with PD and other extrapyramidal disorders. PD patients are differentiated against other patients with similar diseases and not against healthy subjects. This allows one to measure the true specificity of wearable technologies with regard to detecting PD. Data analysis and classification of the types of tremor using machine learning methods (feature extraction, dimensionality reduction, classification) helps significantly improve the accuracy of PD diagnosis. Our results show that, when considering bradykinesia and tremor together, the accuracy of distinguishing PD from similar diseases increases (f1 score 0.88). This closed-loop configuration makes it possible to tune exercises to maximize the diagnostic value of the entire routine. We report approbation on 56 patients.
ISSN:1530-437X
1558-1748
DOI:10.1109/JSEN.2020.3027564