ECG Classification Based on Nonnegative Matrix Factorization and Support Vector Machine
Chuanmin Zhao, Xiaohu Ma · Jisuanji gongcheng · 2012
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%.