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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

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Source of Support: None, Conflict of Interest: None

DOI: 10.4103/jmss.JMSS_50_17

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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.

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