桡动脉多普勒信号诊断类风湿关节炎的分析

A. Özkan, S. Kara, A. Sallı, S. Günes
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引用次数: 0

摘要

本研究记录了40例健康受试者和40例类风湿关节炎患者右手桡动脉接收到的多普勒信号。利用基于子空间的MUSIC方法(谱分析方法中的一种)获得了这些信号的一些特征,并用人工神经网络分类方法对病变病例进行了区分。MUSIC方法采用5、10、15、20、25作为模型度。测试程序是在人工神经网络训练后进行的。测试结果表明,5个模型度的分类准确率为88.75%,10和25个模型度的分类准确率为93.75%,15个模型度的分类准确率为100%,20个模型度的分类准确率为92.5%,所有模型的平均分类准确率为93.75%。建议的方法有可能帮助研究这一主题的专家早期诊断RA疾病。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Analysis of Doppler signals of radial artery for diagnosis of rheumatoid arthritis
In this study, Doppler signals received from the radial artery of the right hands of the 40 healthy subjects and 40 patients with rheumatoid arthritis were recorded. Some features of these signals were obtained using Subspace-based MUSIC method which is one of the spectral analysis methods, and the diseased cases were distinguished with Artificial Neural Networks classification method. In MUSIC method, 5,10,15,20 and 25 were used as model degrees. Test procedure was carried out after training with Artificial Neural Networks. Classification accuracy after the test results was 88.75% for 5 model degrees, 93.75% for 10 and 25 model degrees, 100% for 15 model degrees and 92.5% for 20 model degrees and all models had an average degree of 93.75% classification accuracy was obtained. The proposed approach has potential to help with the early diagnosis of RA disease for the specialists who study this subject.
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