摘要： 提出一种心电信号分类方法，利用非负矩阵分解进行数据降维，运用支持向量机进行心电信号分类，以保留更多的原始数据信息，从而更有效地提取高维心电数据特征，提高分类准确度。通过对MIT-BIH数据库中4类常见心电信号进行分类实验，证明该方法的整体准确率达到99%。

关键词: 非负矩阵分解, 支持向量机, 心电图, 特征向量, 降维

Abstract: In order to achieve better Electrocardiograph(ECG) characteristics from high-dimensional data and realize accurate automatic ECG classification, a novel method for ECG multi-classification is proposed. This method uses Nonnegative Matrix Factorization(NMF) for data dimension reduction and conducts multi-classification by Support Vector Machine(SVM). In implementing the conversion of high dimension to low dimension, NMF retains the original information and supplies better eigenvectors, so it improves the classification accuracy. By testing four kinds of ECG from the MIT-BIH arrhythmia database, the total accuracy is up to 99%.

Key words: Nonnegative Matrix Factorization(NMF), Support Vector Machine(SVM), Electrocardiograph(ECG), eigenvector, dimension reduction

中图分类号: 

• TP391