.png){kind=icon}
返回
A New ECG Denoising Framework Using Generative Adversarial Network
.png){kind=icon}
PRE
AI
DOI：10.1109/TCBB.2020.2976981.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
This paper presents a novel Electrocardiogram (ECG) denoising approach based on the generative adversarial network (GAN). Noise is often associated with the ECG signal recording process. Denoising is central to most of the ECG signal processing tasks. The current ECG denoising techniques are based on the time domain signal decomposition methods. These methods use some kind of thresholding and filtering approaches. In our proposed technique, convolutional neural network (CNN) based GAN model is effectively trained for ECG noise filtering. In contrast to existing techniques, we performed end-to-end GAN model training using the clean and noisy ECG signals. MIT-BIH Arrhythmia database is used for all the qualitative and quantitative analyses. The improved ECG denoising performance open the door for further exploration of GAN based ECG denoising approach.
Keyword:
Electrocardiography
Noise reduction
Gallium nitride
Generative adversarial networks
Training
Noise measurement
Training data
Electrocardiogram
denoising
generative adversarial networks
convolutional neural networks
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
I
IF:
3.4
论文数:
3.3K
被引数:
6.4K
机构
引用论文
An adaptive filtering approach for electrocardiogram (ECG) signal noise reduction using neural networks.png)
NEUROCOMPUTING
NEUROCOMPUTINGIF6.5
PREAI
分享收藏
Is SA shared or distributed in team work? An exploratory study in an intelligence analysis task
PREAI
分享收藏Denoising of ECG signals based on noise reduction algorithms in EMD and wavelet domains基于EMD和小波域降噪算法的心电信号去噪
PREAI
分享收藏
查看更多内容