一种改进U-Net网络的心电图分类算法研究
Study on ECG classification algorithm based on improved U-Net network
王建荣 1尉向前 2辛彬彬 2高睿丰 2李国翚3
作者信息
- 1. 天津大学智能与计算学部,天津 300350;山西大学 自动化与软件学院,太原 030006
- 2. 山西大学 自动化与软件学院,太原 030006
- 3. 天津开发区奥金高新技术有限公司产品研发部,天津 300457
- 折叠
摘要
基于CPSC-2018十二导联数据,提出了一种U-Net网络和注意力机制结合的心电图分类算法.首先,针对数据集数据长度长短不一的问题,对数据进行等长处理和归一化处理.然后,利用U-Net网络中跳层连接和编码解码方式,对预处理后较长的数据进行处理.在U-Net网络解码的最后一层加入注意力机制对抗噪声,提升模型的有效信息关注度和准确性.最后,利用CPSC-2018数据集进行验证.实验结果表明:所提模型能够取得较好的分类效果,识别房颤(AF)和右束支传导阻滞(RBBB)心律失常的精准率、召回率、F1值都可以达到90％以上,平均F1值可以达到82.5％.
Abstract
Cardiovascular disease,with the highest mortality rate across the globe,kills over ten million people every year.Thanks to the continuous development of artificial intelligence,patients'heart conditions can now be quickly and accurately diagnosed with the assistance of automatic electrocardiogram anomaly classification technology.This paper proposes an electrocardiogram classification algorithm based on CPSC-2018 twelve lead data,which combines U-Net network and attention mechanism.First,the data are processed for equal length and normalization to address their varied lengths.Then,the preprocessed data with longer lengths are reprocessed by the skip layer connection and encoding and decoding methods in the U-Net network.An attention mechanism is added to the last layer of U-Net network decoding to combat noise and improve the effective information attention and accuracy of the model.Finally,CPSC-2018 dataset is employed to verify the model.Our experimental results show the proposed model delivers fairly satisfying classification performance,recording its accuracy,recall,and F1 values of over 90％in identifying atrial fibrillation(AF)and right bundle branch block(RBBB)arrhythmias,and an average F1 value of 82.5％.
关键词
心律失常/心电图/U-Net网络/注意力机制Key words
arrhythmia/electrocardiogram/U-Net network/attention mechanism引用本文复制引用
基金项目
国家重点研发计划(2018YFC2000701)
中国博士后科学基金项目(2021M692400)
山西省基础研究计划项目(202203021221017)
出版年
2024
国家科技期刊平台
NSTL
万方数据