基于多项式参数拟合和支持向量机的心肌梗死识别算法

Myocardial infarction recognition algorithm based on polynomial fitting and support vector machine

心电图的复杂性和个人差异导致心肌梗死诊断标准的难以遵循,本文提出了一种基于多项式参数拟合和支持向量机的心肌梗死识别算法。原始信号经过滤波、去噪等预处理之后,使用多项式拟合的方法得到曲线拟合效果最佳的拟合系数,并把预处理中的参数作为特征值,支持向量机作为分类器,实现心电信号的自动分类。该方法实现了心肌梗死信号和正常心电信号的分类,最终识别率为82.017 9%。该方法可行性高、识别率高,具有可扩展性。

Based on the fact that the diagnostic criteria of myocardial infarction is hard to be followed because of the complexity and individual difference of electrocardiogram（ECG）, we proposed a novel recognition method based on polynomial parameter fitting and support vector machine（SVM）. The original signal is preprocessed by filtering and denoising. The optimal fitting coefficients obtained by the polynomial fitting are used as the eigenvalues, and the SVM is taken as the classifier in order to automatically classify the ECG signals. With a good feasibility, high recognition rate and extensibility, the proposed method distinguishes the myocardial infarction signals from normal ECG signals, with a final recognition rate of 82.017 9%.