• Users Online: 290
  • Print this page
  • Email this page
ORIGINAL ARTICLE
Year : 2018  |  Volume : 8  |  Issue : 2  |  Page : 65-72

A classification system for assessment and home monitoring of tremor in patients with Parkinson's disease


1 Department of Electrical and Computer Engineering, Texas Tech University, Lubbock, Texas, USA; Department of Electrical and Computer Engineering, University of Tabriz, Iran
2 Department of Neurology, Rasool Akram Hospital, Iran University of Medical Sciences, Tehran, Iran
3 Department of Information Engineering, Marche Polytechnic University, Ancona, Italy
4 Department of Biological Sciences, Texas Tech University, Lubbock, Texas, USA

Correspondence Address:
Omid Bazgir
Department of Electrical and Computer Engineering, Texas Tech University, Lubbock, Texas

Login to access the Email id

Source of Support: None, Conflict of Interest: None


DOI: 10.4103/jmss.JMSS_50_17

Rights and Permissions

Background: Tremor is one of the most common symptoms of Parkinson's disease (PD), which is widely being used in the diagnosis procedure. Accurate estimation of PD tremor based on Unified PD Rating Scale (UPDRS) provides aid for physicians in prescription and home monitoring. This article presents a robust design of a classification system to estimate PD patient's hand tremors and the results of the proposed system as compared to the UPDRS. Methods: A smartphone accelerometer sensor is used for accurate and noninvasive data acquisition. We applied short-time Fourier transform to time series data of 52 PD patients. Features were extracted based on the severity of PD patients' hand tremor. The wrapper method was employed to determine the most discriminative subset of the extracted features. Four different classifiers were implemented for achieving best possible accuracy in the estimation of PD hand tremor based on UPDRS. Of the four tested classifiers, the Naive Bayesian approach proved to be the most accurate one. Results: The classification result for the assessment of PD tremor achieved close to 100% accuracy by selecting an optimum combination of extracted features of the acceleration signal acquired. For home health-care monitoring, the proposed algorithm was also implemented on a cost-effective embedded system equipped with a microcontroller, and the implemented classification algorithm achieved 93.8% average accuracy. Conclusions: The accuracy result of both implemented systems on MATLAB and microcontroller is acceptable in comparison with the previous works.


[FULL TEXT] [PDF]*
Print this article     Email this article
 Next article
 Previous article
 Table of Contents

 Similar in PUBMED
   Search Pubmed for
   Search in Google Scholar for
 Related articles
 Citation Manager
 Access Statistics
 Reader Comments
 Email Alert *
 Add to My List *
 * Requires registration (Free)
 

 Article Access Statistics
    Viewed1107    
    Printed49    
    Emailed0    
    PDF Downloaded72    
    Comments [Add]    
    Cited by others 19    

Recommend this journal