军事医学2016，Vol.40Issue(10)：829-832,838,5.DOI:10.7644/j.issn.1674-9960.2016.10.013
用于室颤节律辨识的多参数融合BP神经网络设计
Design of BP neural network based on multi-parametes for VF detection
摘要
Abstract
Objective To develop a BP neural network to differentiate between ventricular fibrillation( VF) and non-VF rhythms.Methods Eighteen metrics were extracted from the ECG signals.Each of these metrics respectively characterized each aspect of the signals, such as morphology, gaussianity, spectra, variability, and complexity.These metrics were regarded as the input vector of the BP neural network.After training, a classifier used for VF and non-VF rhythm classification was obtained.Results and Conclusion The constructed BP neural network was tested with the databases of VFDB and CUDB, and the accuracy was 98.61%and 95.37%, respectively.关键词
心电图/室颤/BP神经网络/多参数融合辨识Key words
electrocardiogram/ventricular fibrillation/BP neural network/multi-parameter fusion identification分类
医药卫生引用本文复制引用
余明,陈锋,张广,顾彪,李良喆,王春晨,王丹,吴太虎..用于室颤节律辨识的多参数融合BP神经网络设计[J].军事医学,2016,40(10):829-832,838,5.基金项目
国家自然科学基金资助项目 （）
