Pattern Classification of Hand Movement Tremor in MS Patients with DBS ON and OFF
Background: Hand tremor is one of the consequences of MS disease degrading quality of patient’s life. Recently DBS is used as a prominent treatment to reduce this effect. Evaluation of this approach has significant importance because of the prevalence rate of disease. Objective: The purpose of this...
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| Format: | Article |
| Language: | English |
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Shiraz University of Medical Sciences
2022-02-01
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| Series: | Journal of Biomedical Physics and Engineering |
| Subjects: | |
| Online Access: | https://jbpe.sums.ac.ir/article_45716_9613294d9061755ed5605b1bc505e597.pdf |
| _version_ | 1811189858291417088 |
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| author | Fatemeh Valipour Ali Esteki |
| author_facet | Fatemeh Valipour Ali Esteki |
| author_sort | Fatemeh Valipour |
| collection | DOAJ |
| description | Background: Hand tremor is one of the consequences of MS disease degrading quality of patient’s life. Recently DBS is used as a prominent treatment to reduce this effect. Evaluation of this approach has significant importance because of the prevalence rate of disease. Objective: The purpose of this study was the nonlinear analysis of tremor signal in order to evaluate the quantitative effect of DBS on reducing MS tremor and differentiating between them using pattern recognition algorithms.Material and Methods: In this analytical study, nine features were extracted from the tremor signal. Through statistical analysis, the significance level of each feature was examined. Finally, tremor signals were categorized by SVM, weighted KNN and NN classifiers. The performance of methods was compared with an ROC graph. Results: The results have demonstrated that dominant frequency, maximum amplitude and energy of the first IMF, deviation of the direct path, sample entropy and fuzzy entropy have the potential to create a significant difference between the tremor signals. The classification accuracy rate of tremor signals in three groups for Weighted KNN, NN and SVM with Gaussian and Quadratic kernels resulted in 95.1%, 93.2%, 91.3% and 88.3%, respectively. Conclusion: Generally, nonlinear and nonstationary analyses have a high potential for a quantitative and objective measure of MS tremor. Weighted KNN has shown the best performance of classification with the accuracy of more than 95%. It has been indicated that DBS has a positive influence on reducing the MS tremor. Therefore, DBS can be used in the objective improvement of tremor in MS patients. |
| first_indexed | 2024-04-11T14:41:55Z |
| format | Article |
| id | doaj.art-a46b9e28da5644d1baaa39cd3dc541c5 |
| institution | Directory Open Access Journal |
| issn | 2251-7200 |
| language | English |
| last_indexed | 2024-04-11T14:41:55Z |
| publishDate | 2022-02-01 |
| publisher | Shiraz University of Medical Sciences |
| record_format | Article |
| series | Journal of Biomedical Physics and Engineering |
| spelling | doaj.art-a46b9e28da5644d1baaa39cd3dc541c52022-12-22T04:17:52ZengShiraz University of Medical SciencesJournal of Biomedical Physics and Engineering2251-72002022-02-01121213010.31661/jbpe.v0i0.102845716Pattern Classification of Hand Movement Tremor in MS Patients with DBS ON and OFFFatemeh Valipour0Ali Esteki1MSc, Department of Biomedical Engineering and Medical Physics, Faculty of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, IranPhD, Department of Biomedical Engineering and Medical Physics, Faculty of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, IranBackground: Hand tremor is one of the consequences of MS disease degrading quality of patient’s life. Recently DBS is used as a prominent treatment to reduce this effect. Evaluation of this approach has significant importance because of the prevalence rate of disease. Objective: The purpose of this study was the nonlinear analysis of tremor signal in order to evaluate the quantitative effect of DBS on reducing MS tremor and differentiating between them using pattern recognition algorithms.Material and Methods: In this analytical study, nine features were extracted from the tremor signal. Through statistical analysis, the significance level of each feature was examined. Finally, tremor signals were categorized by SVM, weighted KNN and NN classifiers. The performance of methods was compared with an ROC graph. Results: The results have demonstrated that dominant frequency, maximum amplitude and energy of the first IMF, deviation of the direct path, sample entropy and fuzzy entropy have the potential to create a significant difference between the tremor signals. The classification accuracy rate of tremor signals in three groups for Weighted KNN, NN and SVM with Gaussian and Quadratic kernels resulted in 95.1%, 93.2%, 91.3% and 88.3%, respectively. Conclusion: Generally, nonlinear and nonstationary analyses have a high potential for a quantitative and objective measure of MS tremor. Weighted KNN has shown the best performance of classification with the accuracy of more than 95%. It has been indicated that DBS has a positive influence on reducing the MS tremor. Therefore, DBS can be used in the objective improvement of tremor in MS patients.https://jbpe.sums.ac.ir/article_45716_9613294d9061755ed5605b1bc505e597.pdfmultiple sclerosis’s diseasedeep brain stimulationms tremorempirical mode decompositionnonlinear analysissupport vector machineneural networks, computerk-nearest neighbor |
| spellingShingle | Fatemeh Valipour Ali Esteki Pattern Classification of Hand Movement Tremor in MS Patients with DBS ON and OFF Journal of Biomedical Physics and Engineering multiple sclerosis’s disease deep brain stimulation ms tremor empirical mode decomposition nonlinear analysis support vector machine neural networks, computer k-nearest neighbor |
| title | Pattern Classification of Hand Movement Tremor in MS Patients with DBS ON and OFF |
| title_full | Pattern Classification of Hand Movement Tremor in MS Patients with DBS ON and OFF |
| title_fullStr | Pattern Classification of Hand Movement Tremor in MS Patients with DBS ON and OFF |
| title_full_unstemmed | Pattern Classification of Hand Movement Tremor in MS Patients with DBS ON and OFF |
| title_short | Pattern Classification of Hand Movement Tremor in MS Patients with DBS ON and OFF |
| title_sort | pattern classification of hand movement tremor in ms patients with dbs on and off |
| topic | multiple sclerosis’s disease deep brain stimulation ms tremor empirical mode decomposition nonlinear analysis support vector machine neural networks, computer k-nearest neighbor |
| url | https://jbpe.sums.ac.ir/article_45716_9613294d9061755ed5605b1bc505e597.pdf |
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