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中文摘要：
我们基于心电图特征提取与神经网络分类，提出了一种模拟现实的心电图分类方法，首先应用改进的心电图射线拟合理论，得到心电图多导联的特征；并根据心电图疾病分析的原理与实际诊断的特点，得到一个能对多导联心电图数据疾病类型进行分类的神经网络，为心电图自动分析提出了一个新的思路。经MIT-BIH心电数据库部分波形试验证明，该方法具有较高准确率，对学习过的波形分类正确率为100％，未学习过的78．2％。
英文摘要：
An electrocardiogram （ECG） classify system based on the features of the ECG and neural network classification, which is the simulation of the real world situation, was present. First, a modified approach of the linear approximation distance thresholding （LADT） algorithm was studied and the features of the ECG were obtained. Then a neural network which can classify the multi-lead ECG data was trained with these features along the theory of the ECG diagnosis and the situation of ECG diagnosis in practice. Thus take a new idea for the ECG automatic analysis. The algorithm was tested using several ECG signals of MIT-BIH, and the performance was good. The correct rate of the trained wave is 100%, untrained is 78.2%.
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