计算机工程2012，Vol.38Issue(9)：174-176,3.DOI:10.3969/j.issn.1000-3428.2012.09.052
基于非负矩阵分解和支持向量机的心电图分类
ECG Classification Based on Nonnegative Matrix Factorization and Support Vector Machine
摘要
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%.关键词
非负矩阵分解/支持向量机/心电图/特征向量/降维Key words
Nonnegative Matrix Factorization(NMF)/ Support Vector Machine(SVM)/ Electrocardiograph ECG)/ eigenvector/ dimension reduction分类
信息技术与安全科学引用本文复制引用
赵传敏,马小虎..基于非负矩阵分解和支持向量机的心电图分类[J].计算机工程,2012,38(9):174-176,3.基金项目
苏州市科技计划基金资助项目(SG201005) （SG201005）
