临床上检测异位性心搏，对心血管疾病的早发现、早诊断、早治疗有重大意义。异位性心搏的自动识别能有效减轻人工识别的负担。采用 37只小鼠各 10 min的心电图(ECG)信号，由 3位专家完成异位性心搏的标定及数据库的建立。在此基础上，使用 7种机器学习方法，组合脉冲排除滤波器和模板匹配算法的值，对异位性心搏点进行自动识别。实验结果表明， 7种机器学习的方法都能得到较好的预测性能(所有曲线下面积 (AUC)大于0.899)，其中集成学习 AdaBoost的预测能力最好(AUC=0.940，准确度、特异性均为 0.888)。

Clinical examination of ectopic beats is very important for early detection, diagnosis and treatment of cardiovascular diseases. Automatic recognition of ectopic beats can effectively reduce the burden of manual recognition. In this paper, the 10 minutes Electrocardiogram(ECG) signals of 37 mice were used for analysis. All ectopic beats were calibrated by 3 experts to establish the database. Using 7 machine learning methods, the ectopic beats were automatically identified by combining the values of Impulse Rejection Filter(IRF) and the template matching algorithm. The experimental results show that 7 machine learning methods can achieve good predictive performances(all Area Under Curve(AUC)>0.899), where the ensemble learning method－AdaBoost has the best predictive performance(AUC=0.940, sensitivity=specificity=0.888).