Frequency information enhanced half instance normalization network for denoising electrocardiograms

IF 4.9 2区 医学 Q1 ENGINEERING, BIOMEDICAL
Ning Gao , Yurong Li , Nan Zheng , Wuxiang Shi , Dan Cai , Xiaoying Huang , Hong Chen
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引用次数: 0

Abstract

Background

Electrocardiogram (ECG) is crucial in diagnosing and preventing heart diseases. However, its efficacy is compromised by the interference of the external environment, leading to potential misdiagnoses. Thus, it is crucial to remove the noise in ECGs. Recently, deep-learning based ECGs denoising approaches have achieved impressive performance, however, they only considered the time-domain information of ECGs.

Methods

In this work, we propose a Frequency Information Enhanced Half Instance Normalization Network (FIEHINet) which integrates knowledge of both time and frequency domains into a deep-learning model for ECG signal denoising. Two branches are employed to extract time and frequency features for noise eliminating, respectively. Then the ECG signals are reconstructed based on the fused features. Furthermore, masked signal training is introduced to improve the generalization ability.

Results

In order to evaluate the proposed method, ECGs used are chosen from five different databases. The proposed method for ECG signal denoising achieved Sum of Squared Distances scores of 3.95 ± 7.04, 2.04 ± 3.20, and 0.998 ± 1.579 for three kinds of noise intensities. Meanwhile, the classification experimental results of the processed dataset with the proposed method are 3.8 % higher in F1 score than the original dataset.

Conclusion

A model for removing mixed noises is successfully developed and tested.

Significance

This study presents an ECG denoising technique based on Half Instance Normalization, time–frequency information, and masked signal training, which can improve ECG interpretation and potentially reduce misdiagnoses in clinical practice.
基于频率信息的心电图去噪半实例归一化网络
背景:心电图(ECG)在诊断和预防心脏病方面至关重要。然而,其疗效受到外界环境的干扰,导致潜在的误诊。因此,消除心电图中的噪声是至关重要的。近年来,基于深度学习的脑电图去噪方法取得了令人印象深刻的效果,但这些方法只考虑脑电图的时域信息。方法提出了一种频率信息增强半实例归一化网络(FIEHINet),该网络将时域和频域知识集成到心电信号去噪的深度学习模型中。采用两个分支分别提取时间和频率特征进行消噪。然后根据融合特征重构心电信号。此外,为了提高泛化能力,还引入了屏蔽信号训练。结果为了评价所提出的方法,我们从5个不同的数据库中选择了所使用的心电图。所提出的心电信号去噪方法在三种噪声强度下的距离平方和得分分别为3.95±7.04、2.04±3.20和0.998±1.579。同时,采用该方法处理的数据集的分类实验结果比原始数据集的F1分数提高了3.8%。结论成功地建立了一种去除混合噪声的模型并进行了测试。意义本研究提出了一种基于半实例归一化、时频信息和掩蔽信号训练的心电去噪技术,可以提高心电信号的判读能力,减少临床上的误诊。
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来源期刊
Biomedical Signal Processing and Control
Biomedical Signal Processing and Control 工程技术-工程:生物医学
CiteScore
9.80
自引率
13.70%
发文量
822
审稿时长
4 months
期刊介绍: Biomedical Signal Processing and Control aims to provide a cross-disciplinary international forum for the interchange of information on research in the measurement and analysis of signals and images in clinical medicine and the biological sciences. Emphasis is placed on contributions dealing with the practical, applications-led research on the use of methods and devices in clinical diagnosis, patient monitoring and management. Biomedical Signal Processing and Control reflects the main areas in which these methods are being used and developed at the interface of both engineering and clinical science. The scope of the journal is defined to include relevant review papers, technical notes, short communications and letters. Tutorial papers and special issues will also be published.
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