Abstract: Electrocardiogram is an important approach to diagnose cardiovascular disease. The paper put forward a new classifier which combined two classifiers， Bayes classifier and Support Vector Machine (SVM) classifier, and diagnosed five types of cardiovascular diseases making use of this new approach. The experiments show that the accuracy of the combined classifier is higher than that of Bayes classifier and SVM classifier respectively when training and testing the data in MIT-BIH arrythmia database.

Key words: Electrocardiogram (ECG), Bayes classification, Support Vector Machine (SVM), combined classifier, feature

摘要： 心电图是诊断心血管疾病的重要依据。提出将两个分类器（贝叶斯分类器和支持向量机分类器）进行组合，对五种心电图疾病建立分类模型，并利用麻省理工学院（MIT-BIH）的心电图数据库中的数据进行训练和测试，实验结果表明，经过组合过的分类器的分类正确率比单个贝叶斯分类器和单个支持向量机分类器的正确率要高。

关键词: 心电图, 贝叶斯分类, 支持向量机, 组合分类器, 特征