基于集成学习的束支传导阻滞识别方法

为提升基于心电图的左、右束支传导阻滞自动化诊断性能，提出了一种以多导联心电图卷积神经网络模型作为基学习器的集成学习诊断方法.首先从临床12导联同步静态心电图中提取出有效诊断导联数据并分割为若干个多导联单心搏数据切片.采用自助采样法抽取多个数据子集，并对每个子集以不同方式进行数据扰动后输入基学习器，得到相应的预测模型.然后以贝叶斯方法作为集成学习的结合策略融合多个模型进行预测.最后采用投票法结合1例心电图中的多个心搏分类结果给出诊断.实验结果表明，该方法具有较高的灵敏度和特异度，具有临床应用价值.

Ensemble Learning Based Recognition Method for Bundle Branch Block

In order to improve the automatic diagnosis performance of left and right bundle branch block based on electrocardiogram(ECG)， an ensemble learning method was proposed，while a combination of multi-lead electrocardiogram and convolution neural network model served as the basic learner. Firstly， effective diagnostic lead data is extracted from the clinical 12-lead synchronous static electrocardiogram and divided into slices of multi-lead single heart beat data. Secondly， the bootstrapping method is used to extract multiple data subsets. Each subset would be perturbed and input to the base learner. Afterwards， the corresponding prediction models are obtained. Then， the Bayesian method is used as the combined strategy of ensemble learning to fuse multiple prediction models. Finally， the diagnosis is provided by voting combined with the classification results of multiple beats in an ECG. The experimental results show that the method has high sensitivity and specificity， which has clinical application value.