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首页> 外文期刊>Computer Methods and Programs in Biomedicine: An International Journal Devoted to the Development, Implementation and Exchange of Computing Methodology and Software Systems in Biomedical Research and Medical Practice >Development and testing of a prototype reflex measurement system employing artificial neural networks.
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Development and testing of a prototype reflex measurement system employing artificial neural networks.

机译：使用人工神经网络的原型反射测量系统的开发和测试。

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摘要

This paper presents the development, testing and performance evaluation of a patellar tendon reflex measurement system to provide a quantitative reflex evaluation for use by medical practitioners and in a telemedicine or E-medicine environment. A prototype was developed that makes use of XSens MTx orientation sensors, force-sensitive resistors and an electromyogram to measure the reflex response. Suitable parameters from the sensors were identified for analysis, and clinical testing was performed on 20 subjects to collect data to evaluate the system's performance. Subjective reflex evaluations were conducted by three medical doctors according to a standard reflex grading scale using video recordings of the tests. Multi-layer feed-forward (MLFF) artificial neural networks (ANNs) were used to analyze the collected data with the aim of pattern identification and reflex grading prediction. It was found that the MLFF network delivered the corresponding reflex grading with an accuracy of 85%, which was of the same order as the rate of differences between the subjective reflex evaluations performed by the doctors (80%). The use of ANNs to analyze a reflex measurement offers a repeatable and concise representation of the reflex that is familiar to doctors and can be developed for use in a general clinical setting or for telemedicine purposes.
机译：本文介绍了tell骨腱反射测量系统的开发，测试和性能评估，以提供定量反射评估，供医学从业人员在远程医疗或电子医疗环境中使用。开发了一个原型，该原型使用XSens MTx方向传感器，力敏感电阻器和肌电图来测量反射响应。确定了传感器的合适参数以进行分析，并对20位受试者进行了临床测试以收集数据以评估系统的性能。由三位医生根据标准的反射分级量表，使用测试视频记录进行主观反射评估。多层前馈（MLFF）人工神经网络（ANN）用于分析收集的数据，目的在于进行模式识别和反射灰度预测。发现MLFF网络以85％的准确度提供了相应的反射等级，与医生进行的主观反射评估之间的差异率（80％）处于同一数量级。使用人工神经网络来分析反射测量结果，是医生所熟悉的反射的可重复且简洁的表示，可以开发用于一般临床环境或远程医疗目的。

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