摘要
心电信号(Electrocardiogram,ECG)作为识别人体心脏异常的重要指标,其最常见的一个处理问题是消除不必要的噪声。这些噪声会使干净信号失真,从而影响对人体心脏的诊断与分析。综述了5种不同的心电信号降噪技术框架以及在该框架下的最新研究成果,最后汇总了近5年优秀降噪模型,并通过信噪比等性能评价标准进行比较。对比显示,不管基于单一噪声或是复合噪声,深度学习模型在降噪方面均显现出良好性能。最后,讨论了当前降噪模型存在的不足,并对下一步研究进行了展望。
One of the most common signal processing problems with the electrocardiogram(ECG),an important indicator for identifying cardiac abnormalities in humans,is the elimination of unwanted noise.These noises can distort the clean signal,which can affect the diagnosis and analysis of the human heart.This paper reviews five different frameworks of ECG signal denoising techniques and the latest research results within these frameworks,and finally summarizes the best noise reduction models in last five years and compares them by performance evaluation criteria such as signal-to-noise ratio.The comparison shows that the deep learning models show good performance in ECG denoising,whether based on single noise or composite noise.Finally,the problems with the current denoising model are discussed and an outlook on the next step of the research is given.
作者
侯彦荣
刘瑞霞
舒明雷
陈长芳
单珂
HOU Yanrong;LIU Ruixia;SHU Minglei;CHEN Changfang;SHAN Ke(School of Mathematics and Statistics,Qilu University of Technology(Shandong Academy of Sciences),Jinan 250353,China;Shandong Artificial Intelligence Institute,Qilu University of Technology(Shandong Academy of Sciences),Jinan 250014,China)
出处
《计算机科学》
CSCD
北大核心
2023年第S01期238-248,共11页
Computer Science
基金
国家重点研发计划(2018YFB1404500)。
关键词
心电信号
深度学习
降噪
信噪比
Electrocardiogram(ECG)
Deep learning
Denoising
Signal-to-noise ratio
