| 基于AR模型和神经网络的膝骨性关节炎诊断 |
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| 引用本文： | 廖志伟,蒋锦萍,李玉榕,杜民.基于AR模型和神经网络的膝骨性关节炎诊断[J].东北重型机械学院学报,2010(2):169-172,188. |
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| 作者姓名： | 廖志伟 蒋锦萍 李玉榕 杜民 |
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| 作者单位： | [1]福州大学电气工程与自动化学院,福建福州350108 [2]福建省医疗器械和医药技术重点实验室,福建福州350002 |
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| 基金项目： | 福建省自然科学基金项目（2009J01280）;福建省教育厅资助项目（JD08018）;福建省中西医结合老年性疾病重点实验室开放课题及陈可冀中西医结合发展基金资助项目（2008J1004-53CKJ2008090） |
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| 摘 要： | 本文旨在采用表面肌电信号无创性方法诊断和评判膝骨性关节炎，以在早期能够预防和治疗膝骨性关节炎，改善生活质量。在研究中，采集了对照组和膝骨性关节炎患者水平行走时下肢的股外侧肌，股内侧肌，股二头肌和半腱肌的表面肌电信号。利用表面肌电信号建立自回归（AR）模型，提取AR模型参数为特征向量训练BP神经网络，并通过神经网络诊断膝骨性关节炎。实验表明，基于BP神经网络分类器可以得到较好的结果，正确率可达到88％以上。
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| 关 键 词： | AR模型 特征向量 BP神经网络 膝骨性关节炎 |
Diagnosis of knee osteoarthritis based on AR model and neural network |
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| Authors: | LIAO Zhi-wei JIANG Jin-ping LI Yu-rong DU Min |
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| Affiliation: | 1. College of Electrical Engineering and Automation, Fuzhou University, Fuzhou, Fujian 350108, China; 2. Fujian Key Laboratory of Medical Instrumentation and Pharmaceutical Technology, Fuzhou, Fujian 350002, China) |
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| Abstract: | It is aimed to diagnose and evaluate knee osteoarthritis with noninvasive method by surface electromyography （sEMG） signal, which can prevent and treatment knee osteoarthritis in early stages and improve the quality of life. In this study, the sEMG signals were collected, which from the vastus lateralis, vastus medialis, biceps femoris, and semitendinosus of lower extremity during level working among control subjects and knee osteoarthritis patients. An autoregressive （AR） model is built with sEMG. The AR model parameters are extracted as the characteristic vectors, which is used to train the BP neural network. Then the knee osteoarthritis is diagnosed through the BP neural network. It is showed from the experiments that a satisfactory result is achieved from classifiers based on BP neural network, with the accuracy rate more than 88%. |
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| Keywords: | AR model characteristic vectors BP neural network knee osteoarthritis |
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