基于堆叠分类器的心电异常监测模型设计

针对传统的人工监测心脏疾病的方法对资深医生的依赖性强，需要一定的先验知识，且其监测疾病的速度和准确性有待提高等问题，提出了一种基于堆叠分类器的心电（ECG）监测算法来用于心脏异常的判定。首先，将多种机器学习算法的优势相结合，通过叠加分类器的方式集成起来，从而弥补了单个机器学习算法学习的局限性；其次，使用合成少数过采样技术（SMOTE）对原有的数据集进行了数据扩充，使得各种疾病的数量持平从而增强数据的平衡性。通过在MIT-BIH数据集上与其他机器学习算法的结果进行比较评估，实验结果表明所提算法能够提高ECG异常监测的准确性。

Design of abnormal electrocardiograph monitoring model based on stacking classifier

The traditional methods of manual heart disease monitoring are highly dependent on senior doctors with prior knowledge, and their speeds and accuracies of monitoring disease need to be improved. In order to solve these problems, a ElectroCardioGraph (ECG) monitoring algorithm based on stack classifier was proposed for the determination of cardiac anomalies. Firstly, the advantages of various machine learning algorithms were combined, and these algorithms were integrated by the way of stack classifier to make up for the limitation of learning by single machine learning algorithm. Then, Synthetic Minority Over-sampling TEchnique (SMOTE) was used to perform data augmentation to the original dataset and balance the number of samples of various diseases, so as to improve the data balance. The proposed algorithm was compared with other machine learning algorithms on MIT-BIH dataset. Experimental results show that the proposed algorithm can improve the accuracy and speed of abnormal ECG monitoring.