基于小波变换和支持向量机的心电信号ST段分类

ST Segment Classification of ECG Signals Based on Wavelet Transform and Support Vector Machine

为完成ECG(Electrocardiogram)信号特征点提取,并对ST段分类,提出了一种基于离散小波变换和支持向量机的ST分类算法。首先对信号进行预处理,完成噪声消除,QRS波群检测和提取特征值;然后计算ST段平均值、曲线面积和标准差,并结合使用SVM(Support Vector Machine)对ST段进行分类。Matlab仿真结果表明,小波去噪效果明显,ST段未出现失真现象,特征点提取完整。经MIT-BIT数据库验证,分类结果显示交叉验证准确率平均值为80.70%,训练准确率平均值为91.83%,测试准确率平均值为74.28%。

To complete the ECG signal feature points extraction and the classification of ST segment, we putforward an algorithm based on the discrete wavelet transform, combined with the f derivative and the SVM（Support Vector Machine）. The algorithm can accomplish the signal preprocessing, noise elimination, QRScomplex detection and extraction of characteristic value, calculating the average ST segment, curve area and thestandard deviation, and the simple classification of ST segment by using the SVM combined with the three sets ofdata. The matlab simulation results show that the wavelet denoising is effective and has no distortion, andcompletely extract ST segment feature points. The data are downloaded from the MIT-BIT database, theclassification results show that cross-validation average accuracy is 80.70%, the average accuracy of training is91.83% , the average testing accuracy was 74.28%.