心电节拍自动分类算法的研究

世界卫生组织调查发现在全球范围内心血管、心脏疾病是导致死亡概率最高的疾病，心电图(ECG)是临床上广泛应用的预防、监护和诊断心血管及心脏疾病的重要工具之一。心电自动分析诊断技术可以大大减少心电医师的工作量，提高心电图的诊断效率，其中心电节拍(ECG Beat)分类是心电自动分析诊断技术的主要研究方向，是自动分析心律失常的一种重要分析手段，特别是在动态心电图或者长期心电记录领域发挥着重要的作用。本文提出一种心电节拍分类算法，该算法在聚类分析的基础上，结合线性分类器加权判断和心电医师对各聚类的抽样判断，获得心电节拍的最终分类。以MIT-BIH-AR1]心律失常数据库作为原始数据，采用AAMI的ANSI/AAMI EC57:1998/(R)20032]标准规定的心电节拍分类种类及准确率的衡量方法，对该算法的检验，发现采用聚类分析和线性分类器加权判断的方法，分类的准确率达到86.60%；结合心电医师的抽样判断后，算法最终的准确率高达98.16%。

Research on Automatic ECG Heartbeat Classification

The World Health Organization found that cardiovascular and heart disease causes the highest probability of death in the world. Electrocardiogram (ECG) is an important tool widely used in clinical prevention and diagnosis of cardiovascular and heart disease. Automatic analysis of ECG diagnostic technique can greatly reduce the workload of the cardiologists and improve the efficiency of diagnosis. The classification of ECG Heartbeat is the mainly research direction of ECG automatic analysis as automatic ECG heartbeat classification can improve the diagnostic quality of arrhythmia, especially in the area of dynamic electrocardiogram or the long-term ECG recording. This paper presents an ECG beat classification algorithm, The algorithm uses clusting analysis, mixed with linear classifiers, weighted judgment and physician-assisted classification. Using MIT-BIH-AR arrhythmia database as the raw data and ANSI / AAMI EC57: 1998 / (R) 2003 of AAMI as the standard of classification, the experiment results show that only using clusting analysis, mixed with linear classifiers and weighted judgment, the accuracy rate is 86.60%. After introducing cardiologists-assisted classification, the final accuracy rate is 98.16%.