MrSeNet: Electrocardiogram signal denoising based on multi-resolution residual attention network

IF 1.3 4区 医学 Q3 CARDIAC & CARDIOVASCULAR SYSTEMS
Zhen Wang , Hanshuang Xie , Yamin Liu , Huaiyu Zhu , Hongpo Zhang , Zongmin Wang , Yun Pan
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Abstract

Electrocardiography (ECG) is a widely used, non-invasive, and cost-effective diagnostic method that plays a crucial role in the early detection and management of cardiac conditions. However, the ECG signal is easily disrupted by various noise signals in the real world, leading to a decrease in signal quality and potentially compromising accurate clinical interpretation. With the goal of reducing noise in ECG signals, this research proposes an end-to-end multi-resolution deep learning network with attention mechanism, namely the MrSeNet to perform effective denoising of ECG data. Our MrSeNet fuses features at different scales for effective denoising with the squeeze-and-excitation module to enhance the features of the ECG signal channel. CPSC2018 database and the MIT-BIH database were used to verify the validity of the model by adding different intensity noises based on NSTDB. Using Pearson correlation coefficient, signal-to-noise ratio, and root mean square error performance evaluation model, the experimental results show that MrSeNet performs better than the traditional method, the model can achieve a good denoising effect to different degrees of noise signal data, and has a good future application prospect.
MrSeNet:基于多分辨率剩余注意网络的心电图信号去噪。
心电图(Electrocardiography, ECG)是一种应用广泛、无创、低成本的诊断方法,在心脏疾病的早期发现和治疗中起着至关重要的作用。然而,在现实世界中,心电信号很容易受到各种噪声信号的干扰,导致信号质量下降,并可能影响准确的临床解释。为了降低心电信号中的噪声,本研究提出了一种具有注意机制的端到端多分辨率深度学习网络MrSeNet对心电数据进行有效去噪。我们的MrSeNet融合了不同尺度的特征,通过压缩和激励模块有效地去噪,以增强心电信号通道的特征。利用CPSC2018数据库和MIT-BIH数据库,在NSTDB基础上加入不同强度噪声,验证模型的有效性。利用Pearson相关系数、信噪比和均方根误差性能评价模型,实验结果表明,MrSeNet的性能优于传统方法,该模型对不同程度的噪声信号数据都能达到良好的去噪效果,具有良好的未来应用前景。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Journal of electrocardiology
Journal of electrocardiology 医学-心血管系统
CiteScore
2.70
自引率
7.70%
发文量
152
审稿时长
38 days
期刊介绍: The Journal of Electrocardiology is devoted exclusively to clinical and experimental studies of the electrical activities of the heart. It seeks to contribute significantly to the accuracy of diagnosis and prognosis and the effective treatment, prevention, or delay of heart disease. Editorial contents include electrocardiography, vectorcardiography, arrhythmias, membrane action potential, cardiac pacing, monitoring defibrillation, instrumentation, drug effects, and computer applications.
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