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Clinical Spasticity Assessment Assisted by Machine Learning Methods and Rule-Based Decision

: The Modified Ashworth Scale (MAS) is commonly used to assess spasticity in clinics. The qualitative description of MAS has resulted in ambiguity during spasticity assessment. This work supports spasticity assessment by providing measurement data acquired from wireless wearable sensors, i.e., goni...

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Main Authors:	Jingye Yee, Jingye Yee, Cheng Yee Low, Cheng Yee Low, Mohamad Hashim, Natiara, Che Zakaria, Noor Ayuni, Johar, Khairunnisa, Othman, Nurul Atiqah, Hock Hung Chieng, Hock Hung Chieng, Hanapiah, Fazah Akhtar
Format:	Article
Language:	English
Published:	Mdpi 2023
Subjects:	T Technology (General)
Online Access:	http://eprints.uthm.edu.my/9363/1/J15866_3962a9a5eedc50a7d3cf3ed014298823.pdf http://eprints.uthm.edu.my/9363/ https://doi.org/10.3390/diagnostics13040739
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spelling	my.uthm.eprints.93632023-07-30T07:09:20Z http://eprints.uthm.edu.my/9363/ Clinical Spasticity Assessment Assisted by Machine Learning Methods and Rule-Based Decision Jingye Yee, Jingye Yee Cheng Yee Low, Cheng Yee Low Mohamad Hashim, Natiara Che Zakaria, Noor Ayuni Johar, Khairunnisa Othman, Nurul Atiqah Hock Hung Chieng, Hock Hung Chieng Hanapiah, Fazah Akhtar T Technology (General) : The Modified Ashworth Scale (MAS) is commonly used to assess spasticity in clinics. The qualitative description of MAS has resulted in ambiguity during spasticity assessment. This work supports spasticity assessment by providing measurement data acquired from wireless wearable sensors, i.e., goniometers, myometers, and surface electromyography sensors. Based on in-depth discussions with consultant rehabilitation physicians, eight (8) kinematic, six (6) kinetic, and four (4) physiological features were extracted from the collected clinical data from fifty (50) subjects. These features were used to train and evaluate the conventional machine learning classifiers, including but not limited to Support Vector Machine (SVM) and Random Forest (RF). Subsequently, a spasticity classification approach combining the decision-making logic of the consultant rehabilitation physicians, SVM, and RF was developed. The empirical results on the unknown test set show that the proposed Logical–SVM–RF classifier outperforms each individual classifier, reporting an accuracy of 91% compared to 56–81% achieved by SVM and RF. A data-driven diagnosis decision contributing to interrater reliability is enabled via the availability of quantitative clinical data and a MAS prediction. Mdpi 2023 Article PeerReviewed text en http://eprints.uthm.edu.my/9363/1/J15866_3962a9a5eedc50a7d3cf3ed014298823.pdf Jingye Yee, Jingye Yee and Cheng Yee Low, Cheng Yee Low and Mohamad Hashim, Natiara and Che Zakaria, Noor Ayuni and Johar, Khairunnisa and Othman, Nurul Atiqah and Hock Hung Chieng, Hock Hung Chieng and Hanapiah, Fazah Akhtar (2023) Clinical Spasticity Assessment Assisted by Machine Learning Methods and Rule-Based Decision. Diagnostic, 13 (739). pp. 1-31. https://doi.org/10.3390/diagnostics13040739
institution	Universiti Tun Hussein Onn Malaysia
building	UTHM Library
collection	Institutional Repository
continent	Asia
country	Malaysia
content_provider	Universiti Tun Hussein Onn Malaysia
content_source	UTHM Institutional Repository
url_provider	http://eprints.uthm.edu.my/
language	English
topic	T Technology (General)
spellingShingle	T Technology (General) Jingye Yee, Jingye Yee Cheng Yee Low, Cheng Yee Low Mohamad Hashim, Natiara Che Zakaria, Noor Ayuni Johar, Khairunnisa Othman, Nurul Atiqah Hock Hung Chieng, Hock Hung Chieng Hanapiah, Fazah Akhtar Clinical Spasticity Assessment Assisted by Machine Learning Methods and Rule-Based Decision
description	: The Modified Ashworth Scale (MAS) is commonly used to assess spasticity in clinics. The qualitative description of MAS has resulted in ambiguity during spasticity assessment. This work supports spasticity assessment by providing measurement data acquired from wireless wearable sensors, i.e., goniometers, myometers, and surface electromyography sensors. Based on in-depth discussions with consultant rehabilitation physicians, eight (8) kinematic, six (6) kinetic, and four (4) physiological features were extracted from the collected clinical data from fifty (50) subjects. These features were used to train and evaluate the conventional machine learning classifiers, including but not limited to Support Vector Machine (SVM) and Random Forest (RF). Subsequently, a spasticity classification approach combining the decision-making logic of the consultant rehabilitation physicians, SVM, and RF was developed. The empirical results on the unknown test set show that the proposed Logical–SVM–RF classifier outperforms each individual classifier, reporting an accuracy of 91% compared to 56–81% achieved by SVM and RF. A data-driven diagnosis decision contributing to interrater reliability is enabled via the availability of quantitative clinical data and a MAS prediction.
format	Article
author	Jingye Yee, Jingye Yee Cheng Yee Low, Cheng Yee Low Mohamad Hashim, Natiara Che Zakaria, Noor Ayuni Johar, Khairunnisa Othman, Nurul Atiqah Hock Hung Chieng, Hock Hung Chieng Hanapiah, Fazah Akhtar
author_facet	Jingye Yee, Jingye Yee Cheng Yee Low, Cheng Yee Low Mohamad Hashim, Natiara Che Zakaria, Noor Ayuni Johar, Khairunnisa Othman, Nurul Atiqah Hock Hung Chieng, Hock Hung Chieng Hanapiah, Fazah Akhtar
author_sort	Jingye Yee, Jingye Yee
title	Clinical Spasticity Assessment Assisted by Machine Learning Methods and Rule-Based Decision
title_short	Clinical Spasticity Assessment Assisted by Machine Learning Methods and Rule-Based Decision
title_full	Clinical Spasticity Assessment Assisted by Machine Learning Methods and Rule-Based Decision
title_fullStr	Clinical Spasticity Assessment Assisted by Machine Learning Methods and Rule-Based Decision
title_full_unstemmed	Clinical Spasticity Assessment Assisted by Machine Learning Methods and Rule-Based Decision
title_sort	clinical spasticity assessment assisted by machine learning methods and rule-based decision
publisher	Mdpi
publishDate	2023
url	http://eprints.uthm.edu.my/9363/1/J15866_3962a9a5eedc50a7d3cf3ed014298823.pdf http://eprints.uthm.edu.my/9363/ https://doi.org/10.3390/diagnostics13040739
_version_	1773545889098366976
score	13.211869

Clinical Spasticity Assessment Assisted by Machine Learning Methods and Rule-Based Decision

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