基于深度学习的心律失常分类系统设计  

Design of Arrhythmia Classification System based on Deep Learning

作　　者：吕杭 李杨[1] 张鞠成 王志康[2] 蒋明峰[1] LÜHang;LI Yang;ZHANG Jucheng;WANG Zhikang;JIANG Mingfeng(School of Computer Science and Technology,Zhejiang Sci-Tech University,Hangzhou 310018,China;The Second Affiliated Hospital,Zhejiang University School of Medicine,Hangzhou 310009,China)

机构地区：[1]浙江理工大学计算机科学与技术学院,浙江杭州310018 [2]浙江大学医学院附属第二医院,浙江杭州310009

出　　处：《软件工程》2023年第2期46-49,45,共5页Software Engineering

基　　金：浙江省科技厅重点研发项目(2020C03060);国家自然科学基金(61672466);浙江省自然科学基金-数理医学学会联合基金重点项目(LSZ19F010001).

摘　　要：针对可穿戴设备的长时间心电记录、实时分类及对心电数据的远程监测分析问题,开发了一个对接医疗级心电采集终端,并实现实时监测、实时分析,并通过深度学习模型自动对心律失常分类的通用系统。该系统中部署的深度学习模型是基于残差网络构建的,深度学习模型的训练和测试使用2017年心脏病学挑战赛(CinC2017)提供的数据集。训练和测试结果显示,模型具有较好的性能。系统通过反向代理服务器(Nginx)部署在阿里云服务器上,能够稳定运行;心电采集终端贴在患者身上,通过用户App端和医生后端实时反馈系统自动监测分析的数据,并且有较好的分类效果。该系统可用于有心血管疾病风险的人群,起到早发现、早预防的作用。Aiming at the problems of wearable ECG devices,such as long-term ECG(electrocardiogram)recording,real-time classification,and remote monitoring and analysis of ECG data,this paper proposes to develop a general system of automatic classification of arrhythmia bases on deep learning model.This system docks medical-grade ECG acquisition terminal,realizing real-time monitoring and analysis.The deep learning model deployed in the system is constructed based on residual network.Its training and testing are performed by using the data set provided by 2017 PhysioNet/Computing in Cardiology Challenge(CinC2017),and the training and testing results show that the model achieves good performance.The system is deployed on the Ali Cloud server through Nginx(A reverse proxy server)and has been able to run stably.The ECG acquisition terminal,which is attached to the patient,automatically monitors and analyzes the data through real-time feedback system of the user App terminal and the doctor back-end,and it shows a good classification effect.The proposed system can be used for anyone with a risk of cardiovascular disease,playing the role of early detection and early prevention.

关 键 词：深度学习 心律失常 DJANGO 云服务器 

分 类 号：TP315[自动化与计算机技术—计算机软件与理论]