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

Autor: Jingye Yee, Cheng Yee Low, Natiara Mohamad Hashim, Noor Ayuni Che Zakaria, Khairunnisa Johar, Nurul Atiqah Othman, Hock Hung Chieng, Fazah Akhtar Hanapiah
Jazyk: angličtina
Rok vydání: 2023
Předmět:
Zdroj: Diagnostics, Vol 13, Iss 4, p 739 (2023)
Druh dokumentu: article
ISSN: 2075-4418
51744961
DOI: 10.3390/diagnostics13040739
Popis: 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.
Databáze: Directory of Open Access Journals
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