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A Detection Method for Parkinson's Hand Tremor Based on Machine Learning

  • Jiao Meng
  • , Qingran Niu
  • , Xin Huo
  • , Hui Zhao
  • , Liming Zhang
  • , Xun Wang
  • , Yang Wang
  • Harbin Institute of Technology
  • The First Affiliated Hospital of Harbin Medical University

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Parkinson's disease (PD) is a common neurodegenerative disease, which threatens people's health seriously. Advanced artificial intelligence methods such as machine learning provide a new way for the diagnosis of PD, so it has become a hot topic of concern. This paper proposes a method to expand the hand tremor data set by Generative Adversarial Networks (GAN), extracts the characteristics of tremor data by Discrete Wavelet Transform-Singular Value Decomposition (DWT-SVD), and finally utilizes multiple classification by Support Vector Machine (SVM) to realize the detection of Parkinson's disease.

Original languageEnglish
Title of host publicationProceeding - 2021 China Automation Congress, CAC 2021
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages4105-4109
Number of pages5
ISBN (Electronic)9781665426473
DOIs
StatePublished - 2021
Event2021 China Automation Congress, CAC 2021 - Beijing, China
Duration: 22 Oct 202124 Oct 2021

Publication series

NameProceeding - 2021 China Automation Congress, CAC 2021

Conference

Conference2021 China Automation Congress, CAC 2021
Country/TerritoryChina
CityBeijing
Period22/10/2124/10/21

Keywords

  • Feature Extraction
  • Generative Adversarial Networks
  • Machine Learning
  • Parkinson's Diagnosis

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