Tecnológico Nacional de México/Instituto Tecnológico de Durango, C.P. 34080, Durango, DGO, Mexico.
Universidad Politécnica de Durango, C.P. 34300, Durango, DGO, Mexico.
J Healthc Eng. 2019 Jul 9;2019:1614963. doi: 10.1155/2019/1614963. eCollection 2019.
Clinical evaluation of the patellar reflex is one of the most frequent diagnostic methods used by physicians and medical specialists. However, this test is usually elicited and diagnosed manually. In this work, we develop a device specifically designed to induce the patellar reflex and measure the angle and angular velocity of the leg during the course of the reflex test. We have recorded the response of 106 volunteers with the aim of finding a recognizable pattern in the responses that can allow us to classify each reflex according to the scale of the National Institute of Neurological Disorders and Stroke (NINDS). In order to elicit the patellar reflex, a hammer is attached to a specially designed pendulum, with a controlled impact force. All volunteer test subjects sit at a specific height, performing the Jendrassik maneuver during the test, and the medical staff evaluates the response in accordance with the NINDS scale. The data acquisition system is integrated by using a tapping sensor, an inertial measurement unit, a control unit, and a graphical user interface (GUI). The GUI displays the sensor behavior in real time. The sample rate is 5 kHz, and the control unit is configured for a continuous sample mode. The measured signals are processed and filtered to reduce high-frequency noise and digitally stored. After analyzing the signals, several domain-specific features are proposed to allow us to differentiate between various NINDS groups using machine learning classifiers. The results show that it is possible to automatically classify the patellar reflex into a NINDS scale using the proposed biomechanical measurements and features.
临床评估髌反射是医生和医学专家最常使用的诊断方法之一。然而，这项测试通常是手动进行的。在这项工作中，我们开发了一种专门设计的设备，用于诱发髌反射，并在反射测试过程中测量腿部的角度和角速度。我们已经记录了 106 名志愿者的反应，旨在找到反应中的可识别模式，以便我们能够根据美国国立卫生研究院神经紊乱和中风研究所（NINDS）的标准对每个反射进行分类。为了诱发髌反射，将一个锤子连接到一个专门设计的摆锤上，锤子具有受控的冲击力。所有志愿者测试对象坐在特定的高度，在测试过程中执行詹德雷斯基操作，医务人员根据 NINDS 标准评估反应。数据采集系统由敲击传感器、惯性测量单元、控制单元和图形用户界面（GUI）集成而成。GUI 实时显示传感器行为。采样率为 5kHz，控制单元配置为连续采样模式。测量信号经过处理和滤波，以减少高频噪声并进行数字存储。在对信号进行分析后，提出了几个特定于领域的特征，以便使用机器学习分类器对不同的 NINDS 组进行区分。结果表明，使用提出的生物力学测量和特征，有可能自动将髌反射分类为 NINDS 标准。
