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Fuzzy inference model based on triaxial signals for pronation and supination assessment in Parkinson's disease patients.
Artificial Intelligence in Medicine ( IF 6.1 ) Pub Date : 2020-05-06 , DOI: 10.1016/j.artmed.2020.101873
Alejandro Garza-Rodríguez 1 , Luis Pastor Sánchez-Fernández 1 , Luis Alejandro Sánchez-Pérez 2 , José Juan Carbajal Hernández 1
Affiliation  

Nowadays, the Unified Parkinson Disease Rating Scale supported by the Movement Disorder Society (MDS-UPDRS), is a standardized and widely accepted instrument to rate Parkinson’s disease (PD). This work presents a thorough analysis of item 3.6 of the MDS-UPDRS scale which corresponds to the pronation and supination hand movements. The motivation for this work lies in the objective quantification of motor affectations not covered by the MDS-UPDRS scale such as unsteady oscillations and velocity decrements during the motor exploration. Overall, 12 different bio-mechanical features were quantified based on measurements performed by inertial measurement units (IMUs). After a feature selection process, the selected bio-mechanical features were used as inputs for a fuzzy inference model that predicts the stage of development of the disease in each patient. In addition to this model’s output, the scores of three different expert examiners and the output of a fuzzy inference model which covers affectations strictly attached the MDS-UPDRS guidelines, were also considered to obtain an integrated computational model. The proposed integrated model was incorporated using the Analytic Hierarchy Process (AHP), which gives the novelty of a combined score that helps expert examiners to give a broader assessment of the disease that covers both affectations mentioned in the MDS-UPDRS guidelines and affectations not covered by it in an objective manner.



中文翻译:

基于三轴信号的模糊推理模型用于帕金森病患者的旋前和旋后评估。

如今,运动障碍协会 (MDS-UPDRS) 支持的统一帕金森病评定量表是一种标准化且被广泛接受的帕金森病 (PD) 评定工具。这项工作对 MDS-UPDRS 量表的第 3.6 项进行了全面分析,该项目对应于旋前和旋后手部运动。这项工作的动机在于对 MDS-UPDRS 量表未涵盖的运动影响进行客观量化,例如运动探索过程中的不稳定振荡​​和速度衰减。总的来说,基于惯性测量单元 (IMU) 执行的测量,量化了 12 种不同的生物力学特征。经过特征选择过程后,选定的生物力学特征被用作模糊推理模型的输入,该模型预测每个患者的疾病发展阶段。除了该模型的输出之外,还考虑了三个不同专家审查员的分数和模糊推理模型的输出,该模型涵盖了严格遵守 MDS-UPDRS 指南的人为因素,以获得集成计算模型。提议的集成模型是使用层次分析过程 (AHP) 合并的,它提供了一个综合评分的新颖性,有助于专家审查员对疾病进行更广泛的评估,包括 MDS-UPDRS 指南中提到的影响和未涵盖的影响以客观的方式通过它。三个不同专家审查员的分数和模糊推理模型的输出,该模型涵盖了严格依附于 MDS-UPDRS 指南的人为因素,也被视为获得集成计算模型。提议的集成模型是使用层次分析过程 (AHP) 合并的,它提供了一个综合评分的新颖性,有助于专家审查员对疾病进行更广泛的评估,包括 MDS-UPDRS 指南中提到的影响和未涵盖的影响以客观的方式通过它。三个不同专家审查员的分数和模糊推理模型的输出,该模型涵盖了严格依附于 MDS-UPDRS 指南的人为因素,也被视为获得集成计算模型。提议的集成模型是使用层次分析过程 (AHP) 合并的,它提供了一个综合评分的新颖性,有助于专家审查员对疾病进行更广泛的评估,包括 MDS-UPDRS 指南中提到的影响和未涵盖的影响以客观的方式通过它。

更新日期:2020-05-06
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