基于GAN-CNN的心律失常识别  

Arrhythmia Recognition Based on GAN-CNN

作　　者：陈鹏[1] 刘子龙[1] CHEN Peng;LIU Zilong(School of Optical-Electrical and Computer Engineering,University of Shanghai for Science and Technology,Shanghai 200093,China)

机构地区：[1]上海理工大学光电信息与计算机工程学院,上海200093

出　　处：《电子科技》2022年第3期45-50,共6页Electronic Science and Technology

基　　金：国家自然科学基金(61573246)。

摘　　要：心电图分析是医生诊断心律失常的重要依据。对心律失常的准确判断有助于患者及时了解身体状况并发现潜在疾病。然而,心电图分析不仅费时费力,而且还依赖于临床经验,因此心电图分析的效率一直受到医生数量和工作效率的限制。深度学习技术的发展为计算机辅助诊断系统的开发提供了基础。文中将一维心电信号转换为二维灰度图像,并采用一种GAN-CNN网络解决心电数据不平衡的问题,可同时实现7类心律失常类型和正常心搏的识别。实验使用MIT-BIH心律失常数据库进行验证,平均准确率达到了99.32%,敏感性和特异性分别为99.69%和98.91%。ECG analysis is an important basis for doctors to diagnose arrhythmia.The judgment of arrhythmia helps patients understand their physical conditions in time and find potential diseases.However,ECG analysis is not only time-consuming and labor-intensive,but also relies on clinical experience.Therefore,the efficiency of ECG analysis has always been limited by the number of doctors and work efficiency.The development of deep learning technology provides a foundation for the development of computer-aided diagnosis systems.In this study,a one-dimensional ECG signal is converted into a two-dimensional gray image,and a GAN-CNN network is used to solve the problem of ECG data imbalance,which can simultaneously realize the recognition of 7 types of arrhythmia and normal heartbeat.The experiment is verified by the MIT-BIH arrhythmia database.The average accuracy rate reaches 99.32%,and the sensitivity and specificity are 99.69%and 98.91%,respectively.

关 键 词：心电图 心律失常 深度学习 辅助诊断 心电信号 GAN-CNN 二维图像 心电数据不平衡 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程] TN99[自动化与计算机技术—控制科学与工程]