Design of abnormal electrocardiograph monitoring model based on stacking classifier

doi:10.11772/j.issn.1001-9081.2020060760

Journal of Computer Applications ›› 2021, Vol. 41 ›› Issue (3): 887-890.DOI: 10.11772/j.issn.1001-9081.2020060760

Special Issue: 前沿与综合应用

• Frontier and comprehensive applications • Previous Articles Next Articles

Design of abnormal electrocardiograph monitoring model based on stacking classifier

QIN Jing¹, ZUO Changqing¹, WANG Zumin¹, JI Changqing², WANG Baofeng³

1. College of Information Engineering, Dalian University, Dalian Liaoning 116622 China;
2. College of Physical Science and Technology, Dalian University, Dalian Liaoning 116622, China;
3. School of Network Engineering, Zhoukou Normal University, Zhoukou Henan 466001, China

Received:2020-06-08 Revised:2020-10-19 Online:2021-03-10 Published:2020-12-22
Supported by:
This work is partially supported by the National Natural Science Foundation of China (61702071), the Key Research and Development Program of Liaoning Province (2017104014), the Natural Science Foundation of Liaoning Province (20180551247).

基于堆叠分类器的心电异常监测模型设计

秦静¹, 左长青¹, 汪祖民¹, 季长清², 王宝凤³

1. 大连大学信息工程学院, 辽宁大连 116622;
2. 大连大学物理科学与技术学院, 辽宁大连 116622;
3. 周口师范学院网络工程学院, 河南周口 466001

通讯作者: 汪祖民
作者简介:秦静(1981-),女,甘肃张掖人,讲师,博士,CCF会员,主要研究方向:信号处理、大数据分析;左长青(1993-),男,山东威海人,硕士研究生,主要研究方向:计算机图像处理;汪祖民(1975-),男,河南信阳人,教授,博士,CCF会员,主要研究方向:智慧城市、物联网;季长清(1980-),男,辽宁庄河人,副教授,博士,CCF会员,主要研究方向:空间数据处理;王宝凤(1980-),女,山东烟台人,讲师,博士,CCF会员,主要研究方向:传感器网络、室内定位、智慧医疗。
基金资助:
国家自然科学基金资助项目（61702071）；辽宁省重点研发计划项目（2017104014）；辽宁省自然科学基金资助项目（20180551247）。

Abstract

Abstract: 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.

Key words: ElectroCardioGraph (ECG) monitoring, model fusion, Synthetic Minority Over-sampling TEchnique (SMOTE), integrated learning, machine learning

摘要： 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。

关键词: 心电监测, 模型融合, 合成少数过采样技术, 集成学习, 机器学习

CLC Number:

TP181

QIN Jing, ZUO Changqing, WANG Zumin, JI Changqing, WANG Baofeng. Design of abnormal electrocardiograph monitoring model based on stacking classifier[J]. Journal of Computer Applications, 2021, 41(3): 887-890.

秦静, 左长青, 汪祖民, 季长清, 王宝凤. 基于堆叠分类器的心电异常监测模型设计[J]. 计算机应用, 2021, 41(3): 887-890.

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Design of abnormal electrocardiograph monitoring model based on stacking classifier

基于堆叠分类器的心电异常监测模型设计

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