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深腱反射反应的电生理学和运动学分析,角速度的重要性。

Electrophysiological and kinesiological analysis of deep tendon reflex responses, importance of angular velocity.

机构信息

Department of Biophysics, Faculty of Medicine, Akdeniz University, Dumlupınar Blv. Campus, Antalya, 07070, Turkey.

Department of Electrical-Electronics and Communication Engineering, Public University of Navarra, Pamplona, Navarra, Spain.

出版信息

Med Biol Eng Comput. 2022 Oct;60(10):2917-2929. doi: 10.1007/s11517-022-02638-5. Epub 2022 Aug 12.

Abstract

Deep tendon reflexes are one of the main parameters of the neurological examination in many diseases. Reflex responses increase in upper motor neuron diseases due to a lack of suprasegmental control such as spasticity and rigidity. This information provided by the reflex response makes it an indispensable element of neurological examination. However, an important limitation is that this assessment is subjective. In this study, EMG and kinesiology measurements were recorded together during the assessment of the patellar T reflex in healthy control, spasticity, and Parkinson's disease groups. Nine kinesiologic and three electrophysiologic features were extracted. We validated the proposed method with three healthy participants by ten repeated measurements on 6 different days and we observed that angular velocity is the most stable parameter. Clustering of different groups determined with K-clustering and artificial neural network used for classification with kinesiological and EMG inputs. Our findings show that reflex grade can be determined with high accuracy (Acc = 98.6) in a large population for both pathological and healthy groups and angular velocity is sufficient for reflex grading. Therefore, we think that our study will contribute to the literature by providing an approach with high reliability and reproducibility in the quantitative assessment of reflexes.

摘要

深腱反射是许多疾病神经检查的主要参数之一。由于缺乏诸如痉挛和僵硬等上位运动神经元控制,反射反应在上运动神经元疾病中增加。反射反应提供的信息使其成为神经检查不可或缺的要素。但是,一个重要的局限性是这种评估是主观的。在这项研究中,在健康对照组、痉挛组和帕金森病组中评估髌腱 T 反射时,同时记录肌电图和运动学测量值。提取了九个运动学和三个电生理学特征。我们通过在 6 天的 10 次重复测量中对 3 个健康参与者进行验证,观察到角速度是最稳定的参数。使用 K 聚类和用于分类的人工神经网络对不同组进行聚类,使用运动学和肌电图输入。我们的研究结果表明,反射等级可以在大人群中(包括病理和健康人群)以高精度(Acc=98.6)确定,角速度足以用于反射分级。因此,我们认为我们的研究通过提供一种在反射的定量评估中具有高可靠性和可重复性的方法,将为文献做出贡献。

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