Deep Learning Based Segmentation of Luria's Alternating Series Test to Support Diagnostics of Parkinson's Disease
This research paper focuses on the analysis of various segments of Luria's alternating series drawing test as a diagnostic support for Parkinson's disease. Digitization of drawing tests has allowed capturing pen movement parameters imperceptible to the naked eye, enabling precise neurologi...
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| Vydané v: | Proceedings (IEEE International Conference on Emerging Technologies and Factory Automation) s. 1066 - 1071 |
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| Hlavní autori: | , , , , , |
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15.12.2023
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| ISSN: | 1946-0759 |
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| Abstract | This research paper focuses on the analysis of various segments of Luria's alternating series drawing test as a diagnostic support for Parkinson's disease. Digitization of drawing tests has allowed capturing pen movement parameters imperceptible to the naked eye, enabling precise neurological disorder diagnosis. However, this analysis of parameters presents a disparity between the pre-digital era's human-assisted assessment and the machine learning algorithm employed today. While human practitioners primarily emphasized overall performance and subject errors, the machine learning method relies on kinematic and pressure features to characterize pen tip movements. The paper aims to bridge this gap by utilizing the deep learning object detection algorithm to identify test elements and classical machine learning techniques to analyze kinematic and pressure parameters associated with drawing these elements. The main research contribution centers around two key aspects: 1) evaluating the individual informativeness of test elements at different stages, i.e., beginning, middle, and final parts of the test, and 2) establishing an efficient automatic segmentation framework aimed at enhancing decision support systems in the context of Parkinson's disease diagnosis, |
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| AbstractList | This research paper focuses on the analysis of various segments of Luria's alternating series drawing test as a diagnostic support for Parkinson's disease. Digitization of drawing tests has allowed capturing pen movement parameters imperceptible to the naked eye, enabling precise neurological disorder diagnosis. However, this analysis of parameters presents a disparity between the pre-digital era's human-assisted assessment and the machine learning algorithm employed today. While human practitioners primarily emphasized overall performance and subject errors, the machine learning method relies on kinematic and pressure features to characterize pen tip movements. The paper aims to bridge this gap by utilizing the deep learning object detection algorithm to identify test elements and classical machine learning techniques to analyze kinematic and pressure parameters associated with drawing these elements. The main research contribution centers around two key aspects: 1) evaluating the individual informativeness of test elements at different stages, i.e., beginning, middle, and final parts of the test, and 2) establishing an efficient automatic segmentation framework aimed at enhancing decision support systems in the context of Parkinson's disease diagnosis, |
| Author | Nomm, Sven Laur, Henry Taba, Pille Valla, Elli Medijainen, Kadri Toomela, Aaro |
| Author_xml | – sequence: 1 givenname: Elli surname: Valla fullname: Valla, Elli email: elli.valla@taltech.ee organization: Tallinn University of Technology,Department of Software Science,Tallinn,Estonia – sequence: 2 givenname: Henry surname: Laur fullname: Laur, Henry email: helaur@taltech.ee organization: Tallinn University of Technology,Department of Software Science,Tallinn,Estonia – sequence: 3 givenname: Sven surname: Nomm fullname: Nomm, Sven email: sven.nomm@taltech.ee organization: Tallinn University of Technology,Department of Software Science,Tallinn,Estonia – sequence: 4 givenname: Kadri surname: Medijainen fullname: Medijainen, Kadri email: kadri.medijainen@ut.ee organization: Institute of Sport Sciences and Physiotherapy University of Tartu,Tartu,Estonia – sequence: 5 givenname: Pille surname: Taba fullname: Taba, Pille email: pille.taba@kliinikum.ee organization: University of Tartu,Department of Neurology,Tartu,Estonia – sequence: 6 givenname: Aaro surname: Toomela fullname: Toomela, Aaro email: aaro.toomela@tlu.ee organization: School of Natural Sciences and Health Tallinn University,Tallinn,Estonia |
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| Snippet | This research paper focuses on the analysis of various segments of Luria's alternating series drawing test as a diagnostic support for Parkinson's disease.... |
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| SubjectTerms | Deep learning handwriting dataset Kinematics Luria's alternating series Machine learning algorithms Parkinson's disease Sensitivity Spirals Writing yolo |
| Title | Deep Learning Based Segmentation of Luria's Alternating Series Test to Support Diagnostics of Parkinson's Disease |
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