生物医学工程学杂志

生物医学工程学杂志

基于近邻保持嵌入算法的心律失常心拍分类

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心律失常是一种极其常见的心电活动异常症状,基于心电图(ECG)的心拍分类对心律失常的临床诊断具有十分重要的意义。本文提出一种基于流形学习的特征提取方法——近邻保持嵌入(NPE)算法,实现心律失常心拍的自动分类。分类系统利用NPE算法获取高维心电节拍信号的低维流形结构特征,然后将特征向量输入支持向量机(SVM)分类器进行心拍的分类诊断。实验基于 MIT-BIH 心律失常数据库提供的 ECG 数据,对 14 种类型的心律失常心拍进行分类,总体分类准确率高达 98.51%。实验结果表明,所提方法是一种有效的心律失常心拍分类方法。

Arrhythmia is a kind of common cardiac electrical activity abnormalities. Heartbeats classification based on electrocardiogram (ECG) is of great significance for clinical diagnosis of arrhythmia. This paper proposes a feature extraction method based on manifold learning, neighborhood preserving embedding (NPE) algorithm, to achieve the automatic classification of arrhythmia heartbeats. With classification system, we obtained low dimensional manifold structure features of high dimensional ECG signals by NPE algorithm, then we inputted the feature vectors into support vector machine (SVM) classifier for heartbeats diagnosis. Based on MIT-BIH arrhythmia database, we clustered 14 classes of arrhythmia heartbeats in the experiment, which yielded a high overall classification accuracy of 98.51%. Experimental result showed that the proposed method was an effective classification method for arrhythmia heartbeats.

关键词: 心律失常; 近邻保持嵌入; 心电图; 支持向量机

Key words: arrhythmia; neighborhood preserving embedding; electrocardiogram; support vector machine

引用本文: 高兴姣, 李智, 陈珊珊, 李健. 基于近邻保持嵌入算法的心律失常心拍分类. 生物医学工程学杂志, 2017, 34(1): 1-6. doi: 10.7507/1001-5515.201605045 复制

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