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An End-to-End 12-Leading Electrocardiogram Diagnosis System Based on Deformable Convolutional Neural Network With Good Antinoise Ability
IEEE Transactions on Instrumentation and Measurement ( IF 5.6 ) Pub Date : 2021-04-16 , DOI: 10.1109/tim.2021.3073707
Lang Qin , Yuntao Xie , Xinwen Liu , Xiangchi Yuan , Huan Wang

Electrocardiogram (ECG) is a tool to help judge heart activity. In recent years, the convolutional neural network (CNN) and various deep learning algorithms have been widely used in ECG diagnosis. CNN only considers the local feature. However, the ECG signal is susceptible to noise, and the waveform is complex, making it difficult for existing methods to get a good result. This article presents a novel neural network architecture for ECG diagnosis based on deformable CNN (Deform-CNN). The architecture makes good use of the feature-learning capability of deformable convolution to learn the time-domain and lead characteristics of multilead ECG signals. The proposed end-to-end method can achieve an overall diagnostic accuracy of 86.3% in the 12-lead ECG data of CPSC-2018, with good antinoise ability, which makes the method have a more competitive performance than other deep learning algorithms. The source code is publicly available at https://github.com/HeartbeatAI/Deform-CNN .

中文翻译:

基于具有良好抗噪能力的可变形卷积神经网络的端到端十二导心电图诊断系统

心电图(ECG)是帮助判断心脏活动的工具。近年来,卷积神经网络(CNN)和各种深度学习算法已广泛用于ECG诊断。CNN仅考虑本地功能。但是,ECG信号易受噪声影响,并且波形复杂,使得现有方法难以获得良好的结果。本文提出了一种基于可变形CNN(Deform-CNN)的用于心电图诊断的新型神经网络架构。该架构充分利用了可变形卷积的特征学习能力,以学习多导联心电图信号的时域和导联特性。在CPSC-2018的12导联心电图数据中,提出的端到端方法可以实现86.3%的总体诊断准确性,并且具有良好的抗噪能力,这使得该方法比其他深度学习算法更具竞争力。源代码可在以下位置公开获得https://github.com/HeartbeatAI/Deform-CNN
更新日期:2021-05-07
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