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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Vydáno v:Proceedings (IEEE International Conference on Emerging Technologies and Factory Automation) s. 1066 - 1071
Hlavní autoři: Valla, Elli, Laur, Henry, Nomm, Sven, Medijainen, Kadri, Taba, Pille, Toomela, Aaro
Médium: Konferenční příspěvek
Jazyk:angličtina
Vydáno: IEEE 15.12.2023
Témata:
Deep learning
handwriting dataset
Kinematics
Luria's alternating series
Machine learning algorithms
Parkinson's disease
Sensitivity
Spirals
Writing
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ISSN:1946-0759
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Shrnutí: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,
ISSN:1946-0759
DOI:10.1109/ICMLA58977.2023.00158