Background: Subtle abnormal motor signs are indications of serious neurological diseases. Although neurological deficits require fast initiation of treatment in a restricted time, it is difficult for nonspecialists to detect and objectively assess the symptoms. In the clinical environment, diagnoses and decisions are based on clinical grading methods, including the National Institutes of Health Stroke Scale (NIHSS) score or the Medical Research Council (MRC) score, which have been used to measure motor weakness. Objective grading in various environments is necessitated for consistent agreement among patients, caregivers, paramedics, and medical staff to facilitate rapid diagnoses and dispatches to appropriate medical centers. Objective: In this study, we aimed to develop an autonomous grading system for stroke patients. We investigated the feasibility of our new system to assess motor weakness and grade NIHSS and MRC scores of 4 limbs, similar to the clinical examinations performed by medical staff. Methods: We implemented an automatic grading system composed of a measuring unit with wearable sensors and a grading unit with optimized machine learning. Inertial sensors were attached to measure subtle weaknesses caused by paralysis of upper and lower limbs. We collected 60 instances of data with kinematic features of motor disorders from neurological examination and demographic information of stroke patients with NIHSS 0 or 1 and MRC 7, 8, or 9 grades in a stroke unit. Training data with 240 instances were generated using a synthetic minority oversampling technique to complement the imbalanced number of data between classes and low number of training data. We trained 2 representative machine learning algorithms, an ensemble and a support vector machine (SVM), to implement auto-NIHSS and auto-MRC grading. The optimized algorithms performed a 5-fold cross-validation and were searched by Bayes optimization in 30 trials. The trained model was tested with the 60 original hold-out instances for performance evaluation in accuracy, sensitivity, specificity, and area under the receiver operating characteristics curve (AUC). Results: The proposed system can grade NIHSS scores with an accuracy of 83.3% and an AUC of 0.912 using an optimized ensemble algorithm, and it can grade with an accuracy of 80.0% and an AUC of 0.860 using an optimized SVM algorithm. The auto-MRC grading achieved an accuracy of 76.7% and a mean AUC of 0.870 in SVM classification and an accuracy of 78.3% and a mean AUC of 0.877 in ensemble classification. Conclusions: The automatic grading system quantifies proximal weakness in real time and assesses symptoms through automatic grading. The pilot outcomes demonstrated the feasibility of remote monitoring of motor weakness caused by stroke. The system can facilitate consistent grading with instant assessment and expedite dispatches to appropriate hospitals and treatment initiation by sharing auto-MRC and auto-NIHSS scores between prehospital and hospital responses as an objective observation.

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中文翻译：

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使用优化的机器学习和四肢运动学对卒中症状进行自动评分以进行快速评估：临床验证研究。

背景：微妙的异常运动征兆表明存在严重的神经系统疾病。尽管神经功能缺损需要在有限的时间内快速开始治疗，但非专业人员很难发现并客观地评估症状。在临床环境中，诊断和决策基于临床分级方法，包括用于测量运动无力的美国国立卫生研究院卒中量表（NIHSS）评分或医学研究理事会（MRC）评分。为了在患者，护理人员，护理人员和医务人员之间达成一致的协议，有必要在各种环境中进行客观评分，以促进快速诊断并派遣到适当的医疗中心。目的：在这项研究中，我们旨在为中风患者开发一种自动分级系统。我们调查了新系统评估运动无力以及4条肢体NIHSS和MRC评分的可行性，类似于医务人员进行的临床检查。方法：我们实施了一个自动分级系统，该系统由带有可穿戴传感器的测量单元和具有最佳机器学习功能的分级单元组成。安装了惯性传感器以测量由上肢和下肢麻痹引起的细微弱点。我们从神经系统检查和脑卒中以NIHSS 0或1以及MRC 7、8或9级的卒中患者的神经学检查和人口统计学信息收集了60个具有运动障碍运动学特征的数据实例。使用合成的少数群体过采样技术生成了240个实例的训练数据，以补充类之间的数据数量不均衡和训练数据数量少的问题。我们训练了2种代表性的机器学习算法，即集成算法和支持向量机（SVM），以实现自动NIHSS和自动MRC分级。优化的算法进行了5倍交叉验证，并在30个试验中通过贝叶斯优化进行了搜索。使用60个原始保留实例对训练后的模型进行了测试，以评估准确性，灵敏度，特异性和接收器工作特征曲线（AUC）下的面积的性能。结果：所提出的系统可以使用优化的集成算法对NIHSS评分进行评分，准确度为83.3％，AUC为0.912，使用优化的SVM算法可以对评分为80.0％的准确性，AUC为0.860。在SVM分类中，自动MRC分级的准确度为76.7％，平均AUC为0.870，准确度为78.3％，平均AUC为0。合奏分类中的877。结论：自动分级系统实时量化近端肌无力，并通过自动分级评估症状。试验结果证明了远程监测中风引起的运动无力的可行性。该系统可通过在院前和医院反应之间共享自动MRC和自动NIHSS分数作为客观观察，从而促进即时评估的一致评分，并加快派往适当医院的步伐和治疗开始。

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