摘要
目的:室性早搏(PVC)是最常见的心律失常,能够实时、准确检测出它具有重要的临床意义。为了能够及早地发现室性早搏(PVC)、提高医生对室性早搏(PVC)诊断的准确性,本文研究了基于小波变换和BP神经网络的室性早搏(PVC)识别方法。方法:首先选取ECG信号的特征参数,用小波变换检测其特征点,提取特征参数,构造特征向量,然后再用训练好的BP神经网络对室性早搏(PVC)进行识别,从而实现ECG信号的正常心律和室性早搏的自动检测识别。结果:本文构造了一个7-10-1结构的三层BP神经网络,用MIT-BIH标准心电数据库中的心电信号数据对构造的神经网络进行训练、检测识别,识别率达到预期的效果。结论:分析MIT-BIH心电数据库六组试验数据的实验结果,证明了基于小波变换和BP神经网络的室性早搏(PVC)识别的方法具有较高的PVC自动检测识别率,对医生的诊断有良好的辅助作用,具有较高的应用价值。
Objective： The premature ventricular contraction （PVC） is one of the most familiar diseases of the arrhythmia. Its real-time and accurate examination has important clinical significance. To find a premature ventricular contractions （PVC） early and improve the diagnostic fccuracy of premature ventricular contractions （PVC）,a method called automatic identification of：PVC based on wavelet transform and BP Neural Network is proposed in this paper. Methods： After selecting the characteristic parameters of EC_.G signal using wavelet transform, detection of the feature points, extracting characteristic parameters, constructing feature vector and recognition of PVC by BP Neural Network. The automatic identification of normal ECG and PVC was realized. Results： A 7-10-1 three-layer BP Neural Network is constructed in this paper, the neural network was proved having better automatic identification ability （average 92%） after training and testing the neural network using the ECG datum of MIT-BIH database. Conclusions： Analyzing experimental results of six groups of test data in MIT-BIH ECG database,the method could assist doctors in making better diagnosis on ECG potentially.
出处
《中国医学物理学杂志》
CSCD
2010年第2期1762-1765,共4页
Chinese Journal of Medical Physics
