心电信号能够反映人体心脏健康状态，对心电图（Electrocardiogram，ECG）的自动分析方法对于及时地发现和预防心血管疾病具有重要的意义。本文提出了一种融合手工特征和深度特征的集成超限学习机心跳分类方法。一方面，手工提取的特征明确地表征了心电信号的特定特性，即RR间期特征反映了心跳信号的时域特性，小波系数特征反应了心跳信号的时频特性。另一方面，本文设计了一个一维卷积神经网络来对心跳信号特征进行自动提取。这些特征被整合到一起，然后传入超限学习机（Extreme Leaning Machine，ELM）分类模型中。由于ELM初始参数的随机给定可能导致其性能不稳定，本文提出一种基于Bagging策略的多个ELM的集成方法，使最后的分类结果更加稳定且模型泛化能力更强。本文提出的方法在MIT-BIH心律失常公开数据集上进行验证，执行16类心跳分类。在单导联上的分类准确率达到了99.05%。实验结果也说明了融合后的特征得到的准确率要高于两者分别得到的准确率。
Electrocardiosignal can reflect human cardiac health status, and automatic analysis approach of electrocardiogram (ECG) is of great significance for the timely detection and prevention of cardiovascular diseases. An extreme learning machine (ELM) ensemble approach for heartbeat classification that fuses handcrafted features and deep features is proposed. On one hand, the manually extracted features clearly characterize the heartbeat signal: RR interval features reflect the time-domain characteristic while the wavelet coefficient features reflect the time-frequency characteristic. On the other hand, a 1D convolutional neural network (1D CNN) is designed to automatically extract deep features for heartbeat signal. These features are fused and sent to ELM for classification. Due to the unstable nature caused by the random assignment of ELM hidden layer parameters, bagging ensemble strategy is proposed to integrate multiple ELMs and hence achieves stable classification performance and good generalization ability. The proposed approach is validated on the MIT-BIH arrhythmia public dataset, where classification task on 16 heartbeat types is performed. The classification accuracy on single lead reaches 99.05%. The experimental results also show that the performance of the approach with fused features is better than those with deep feature and handcrafted features, respectively.