A Multimodal Approach to the Quantification of Kinetic Tremor in Parkinson’s Disease
Parkinson’s disease results in motor impairment that deteriorates patients’ quality of life. One of the symptoms negatively interfering with daily activities is kinetic tremor which should be measured to monitor the outcome of therapy. A new instrumented method of quantification...
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| Format: | Article |
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MDPI AG
2019-12-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/20/1/184 |
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| author | Mateusz Szumilas Krzysztof Lewenstein Elżbieta Ślubowska Stanisław Szlufik Dariusz Koziorowski |
| author_facet | Mateusz Szumilas Krzysztof Lewenstein Elżbieta Ślubowska Stanisław Szlufik Dariusz Koziorowski |
| author_sort | Mateusz Szumilas |
| collection | DOAJ |
| description | Parkinson’s disease results in motor impairment that deteriorates patients’ quality of life. One of the symptoms negatively interfering with daily activities is kinetic tremor which should be measured to monitor the outcome of therapy. A new instrumented method of quantification of the kinetic tremor is proposed, based on the analysis of circles drawn on a digitizing tablet by a patient. The aim of this approach is to obtain a tremor scoring equivalent to that performed by trained clinicians. Models are trained with the least absolute shrinkage and selection operator (LASSO) method to predict the tremor scores on the basis of the parameters computed from the patients’ drawings. Signal parametrization is derived from both expert knowledge and the response of an artificial neural network to the raw data, thus the approach was named multimodal. The fitted models are eventually combined into model ensembles that provide aggregated scores of the kinetic tremor captured in the drawings. The method was verified with a set of clinical data acquired from 64 Parkinson’s disease patients. Automated and objective quantification of the kinetic tremor with the presented approach yielded promising results, as the Pearson’s correlations between the visual ratings of tremor and the model predictions ranged from 0.839 to 0.890 in the best-performing models. |
| first_indexed | 2024-12-10T06:57:22Z |
| format | Article |
| id | doaj.art-291b9c89c2064df8b0bd324836dcf133 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-12-10T06:57:22Z |
| publishDate | 2019-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-291b9c89c2064df8b0bd324836dcf1332022-12-22T01:58:25ZengMDPI AGSensors1424-82202019-12-0120118410.3390/s20010184s20010184A Multimodal Approach to the Quantification of Kinetic Tremor in Parkinson’s DiseaseMateusz Szumilas0Krzysztof Lewenstein1Elżbieta Ślubowska2Stanisław Szlufik3Dariusz Koziorowski4Institute of Metrology and Biomedical Engineering, Faculty of Mechatronics, Warsaw University of Technology, A. Boboli 8 St., 02-525 Warsaw, PolandInstitute of Metrology and Biomedical Engineering, Faculty of Mechatronics, Warsaw University of Technology, A. Boboli 8 St., 02-525 Warsaw, PolandInstitute of Metrology and Biomedical Engineering, Faculty of Mechatronics, Warsaw University of Technology, A. Boboli 8 St., 02-525 Warsaw, PolandDepartment of Neurology, Faculty of Health Science, Medical University of Warsaw, Żwirki i Wigury 61 St., 02-091 Warsaw, PolandDepartment of Neurology, Faculty of Health Science, Medical University of Warsaw, Żwirki i Wigury 61 St., 02-091 Warsaw, PolandParkinson’s disease results in motor impairment that deteriorates patients’ quality of life. One of the symptoms negatively interfering with daily activities is kinetic tremor which should be measured to monitor the outcome of therapy. A new instrumented method of quantification of the kinetic tremor is proposed, based on the analysis of circles drawn on a digitizing tablet by a patient. The aim of this approach is to obtain a tremor scoring equivalent to that performed by trained clinicians. Models are trained with the least absolute shrinkage and selection operator (LASSO) method to predict the tremor scores on the basis of the parameters computed from the patients’ drawings. Signal parametrization is derived from both expert knowledge and the response of an artificial neural network to the raw data, thus the approach was named multimodal. The fitted models are eventually combined into model ensembles that provide aggregated scores of the kinetic tremor captured in the drawings. The method was verified with a set of clinical data acquired from 64 Parkinson’s disease patients. Automated and objective quantification of the kinetic tremor with the presented approach yielded promising results, as the Pearson’s correlations between the visual ratings of tremor and the model predictions ranged from 0.839 to 0.890 in the best-performing models.https://www.mdpi.com/1424-8220/20/1/184parkinson’s diseasekinetic tremordigitizing tabletecho state networkmachine learning |
| spellingShingle | Mateusz Szumilas Krzysztof Lewenstein Elżbieta Ślubowska Stanisław Szlufik Dariusz Koziorowski A Multimodal Approach to the Quantification of Kinetic Tremor in Parkinson’s Disease Sensors parkinson’s disease kinetic tremor digitizing tablet echo state network machine learning |
| title | A Multimodal Approach to the Quantification of Kinetic Tremor in Parkinson’s Disease |
| title_full | A Multimodal Approach to the Quantification of Kinetic Tremor in Parkinson’s Disease |
| title_fullStr | A Multimodal Approach to the Quantification of Kinetic Tremor in Parkinson’s Disease |
| title_full_unstemmed | A Multimodal Approach to the Quantification of Kinetic Tremor in Parkinson’s Disease |
| title_short | A Multimodal Approach to the Quantification of Kinetic Tremor in Parkinson’s Disease |
| title_sort | multimodal approach to the quantification of kinetic tremor in parkinson s disease |
| topic | parkinson’s disease kinetic tremor digitizing tablet echo state network machine learning |
| url | https://www.mdpi.com/1424-8220/20/1/184 |
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