吉林大学学报（信息科学版）2023，Vol.41Issue(6)：1135-1142,8.
基于深度学习的心电信号分析检测系统
ECG Analysis and Detection System Based on Deep Learning
摘要
Abstract
The traditional methods of manually identifying electrocardiogram signals have problems such as high workload and recognition errors.The existing electrocardiogram monitoring equipment still faces drawbacks such as limited recognition types of electrocardiogram signals,low diagnostic accuracy,and excessive reliance on network services.In order to improve the performance of electrocardiogram monitoring systems,ECG(Electrocardiogram Signals)analysis and detection system is designed based on deep learning technology.SENet-LSTM(Squeeze-and-Excitation Networks-Long Short Term Memory)network model is built to realize automatic diagnosis of seven categories of ECG signals.The model is deployed on an intelligent hardware platform which uses ADS1292R as the ECG acquisition module,STM32F103 as the data processing module,and Raspberry PI as the central processing module.The system uses the integrated high-performance microcomputer Raspberry PI for calculation and analysis,and provides users with offline AI(Artificial Intelligence)services.The preciseness of the model can reach 98.44％,and the accuracy can reach 90.00％,realizing the real-time monitoring and accurate classification of ECG,and providing accurate disease diagnosis for patients.关键词
心电信号/数字信号处理/深度学习/智能化硬件平台Key words
electrocardiogram signals/digital signal processing/deep learning/intelligent hardware platform分类
信息技术与安全科学引用本文复制引用
刘樱琪,宋杨,李梓木,罗维,黄新睿,王昊丰..基于深度学习的心电信号分析检测系统[J].吉林大学学报（信息科学版）,2023,41(6):1135-1142,8.基金项目
吉林大学大学生创新训练基金资助项目(202210183168) （202210183168）
