Cerebro-Cardiovascular Disease Research Center, Yonsei University College of Medicine, Seoul, Republic of Korea.
Department of Radiology, Yonsei University College of Medicine, Seoul, Republic of Korea.
J Med Internet Res. 2020 Sep 16;22(9):e20641. doi: 10.2196/20641.
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.
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.
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).
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.
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.
细微的运动异常迹象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迅速开始治疗，但非专业人员很难发现并客观评估这些症状。在临床环境中，诊断和决策基于临床分级方法，包括美国国立卫生研究院卒中量表（NIHSS）评分或医学研究委员会（MRC）评分，这些方法用于测量运动无力。为了使患者、护理人员、护理人员和医务人员之间达成一致，客观地对各种环境进行分级是必要的，以促进快速诊断并将患者送往合适的医疗中心。
本研究旨在开发一种用于脑卒中患者的自主分级系统。我们研究了新系统评估运动无力并对 4 肢的 NIHSS 和 MRC 评分进行分级的可行性，类似于医务人员进行的临床检查。
我们实现了一个自动分级系统，该系统由一个带有可穿戴传感器的测量单元和一个具有优化机器学习的分级单元组成。惯性传感器用于测量由上下肢瘫痪引起的细微无力。我们在卒中病房中收集了 60 例神经检查中运动障碍的运动学特征和 NIHSS 0 或 1 级和 MRC 7、8 或 9 级的卒中患者的人口统计学信息。使用合成少数过采样技术生成了具有 240 个实例的训练数据，以补充类之间不平衡的数据数量和低训练数据数量。我们训练了 2 个代表性的机器学习算法，即集成和支持向量机（SVM），以实现自动 NIHSS 和自动 MRC 分级。优化后的算法进行了 5 折交叉验证，并在 30 次试验中通过贝叶斯优化进行搜索。使用 60 个原始保留实例对训练模型进行测试，以评估准确性、敏感性、特异性和接收者操作特征曲线（ROC）下的面积（AUC）。
使用优化的集成算法，该系统可以对 NIHSS 评分进行分级，准确率为 83.3%，AUC 为 0.912；使用优化的 SVM 算法，该系统可以分级，准确率为 80.0%，AUC 为 0.860。自动 MRC 分级使用 SVM 分类的准确率为 76.7%，平均 AUC 为 0.870，使用集成分类的准确率为 78.3%，平均 AUC 为 0.877。
自动分级系统实时量化近端无力，并通过自动分级评估症状。初步结果表明，远程监测卒中引起的运动无力是可行的。该系统可以通过在院前和医院响应之间共享自动 MRC 和自动 NIHSS 评分，作为客观观察，实现即时评估和加速将患者送往合适医院和开始治疗的一致性分级。
