用于多标签心电图分类的导联聚类多分支网络

IF 1.7 4区 医学 Q3 ENGINEERING, BIOMEDICAL
Feiyan Zhou , Lingzhi Chen
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引用次数: 0

摘要

在临床实践中,12 导联心电图(ECG)被广泛用于诊断心血管疾病。最近,深度学习方法在自动对心电图信号进行分类方面变得越来越有效。然而,目前大多数研究只是简单地将 12 导联心电图信号组合成一个矩阵,而没有充分考虑导联与心脏结构之间的内在关系。为了更好地利用医学领域的知识,我们提出了一种用于多标签心电图分类的多分支网络,并引入了一种直观有效的导联分组策略。相应地,我们设计了多分支网络,每个分支采用多尺度卷积网络结构,以提取更全面的特征,每个分支对应一个导联组合。为了更好地整合来自不同线索的特征,我们提出了一个特征加权融合模块。我们在 PTB-XL 数据集上评估了我们的方法,对 4 种心律失常类型和正常节律进行了分类,并在 2018 年中国生理信号挑战赛(CPSC2018)数据库上评估了我们的方法,对 8 种心律失常类型和正常节律进行了分类。在多个多标签数据集上的实验结果表明,我们提出的多分支网络在多标签分类任务中的表现优于最先进的网络
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Leadwise clustering multi-branch network for multi-label ECG classification

The 12-lead electrocardiogram (ECG) is widely used for diagnosing cardiovascular diseases in clinical practice. Recently, deep learning methods have become increasingly effective for automatically classifying ECG signals. However, most current research simply combines the 12-lead ECG signals into a matrix without fully considering the intrinsic relationships between the leads and the heart's structure. To better utilize medical domain knowledge, we propose a multi-branch network for multi-label ECG classification and introduce an intuitive and effective lead grouping strategy. Correspondingly, we design multi-branch networks where each branch employs a multi-scale convolutional network structure to extract more comprehensive features, with each branch corresponding to a lead combination. To better integrate features from different leads, we propose a feature weighting fusion module. We evaluate our method on the PTB-XL dataset for classifying 4 arrhythmia types and normal rhythm, and on the China Physiological Signal Challenge 2018 (CPSC2018) database for classifying 8 arrhythmia types and normal rhythm. Experimental results on multiple multi-label datasets demonstrate that our proposed multi-branch network outperforms state-of-the-art networks in multi-label classification tasks

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来源期刊
Medical Engineering & Physics
Medical Engineering & Physics 工程技术-工程:生物医学
CiteScore
4.30
自引率
4.50%
发文量
172
审稿时长
3.0 months
期刊介绍: Medical Engineering & Physics provides a forum for the publication of the latest developments in biomedical engineering, and reflects the essential multidisciplinary nature of the subject. The journal publishes in-depth critical reviews, scientific papers and technical notes. Our focus encompasses the application of the basic principles of physics and engineering to the development of medical devices and technology, with the ultimate aim of producing improvements in the quality of health care.Topics covered include biomechanics, biomaterials, mechanobiology, rehabilitation engineering, biomedical signal processing and medical device development. Medical Engineering & Physics aims to keep both engineers and clinicians abreast of the latest applications of technology to health care.
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