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DeepECG: Image-based electrocardiogram interpretation with deep convolutional neural networks

机译:Deepecg:基于图像的心电图与深卷积神经网络的解释

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Electrocardiogram (ECG) plays a critical role in the diagnosis of cardiovascular disease (CVDs). In this paper, we develop DeepECG, a system that diagnoses 7 kinds of arrhythmia from 51,579 ECGs. DeepECG takes ECG images as inputs, and performs arrhythmia classification using deep convolutional neural network models (DCNN) and transfer learning. We conduct a comprehensive study of different neural network architectures, where the best model Inception-V3 achieves mean balanced accuracy of 98.46 %, recall of 95.43 %, and specificity of 96.75 %. The experimental results have successfully validated that our system can achieve excellent multi-label classification based on image formats, making it possible for cardiologists to use image-based ECG interpretation with DCNN to aid diagnosis and reduce misdiagnosis rates.
机译:心电图(ECG)在诊断心血管疾病(CVDS)中起着关键作用。 在本文中,我们开发Deepecg,一种诊断来自51,579个ECG的7种心律失常的系统。 Deepecg将ECG图像作为输入,使用深卷积神经网络模型(DCNN)和转移学习进行心律失常分类。 我们对不同的神经网络架构进行了全面的研究,其中最佳型号型号-V3实现了98.46%的平均均衡准确度,召回的95.43%,特异性为96.75%。 实验结果已成功验证,我们的系统可以基于图像格式实现优异的多标签分类,使心脏病学家可以使用与DCNN的基于图像的ECG解释,以辅助诊断并降低误诊率。

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