ECGNET: Learning where to attend for detection of atrial fibrillation with deep visual attention.
ECGNET: Learning where to attend for detection of atrial fibrillation with deep visual attention.
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DOI：
10.1109/bhi.2019.8834637
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发表时间：
2019-05
期刊：
影响因子：
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通讯作者：
Acharya UR
中科院分区：
文献类型：
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作者：
Mousavi SS;Afghah F;Razi A;Acharya UR
与心房颤动 (AF) 相关的模式的复杂性以及影响这些模式的高水平噪声极大地限制了当前信号处理和浅层机器学习方法准确检测这种情况的应用。深度神经网络已被证明非常强大，可以学习计算机视觉任务等各种问题中的非线性模式。虽然深度学习方法已被用来学习与心电图 (ECG) 信号中 AF 存在相关的复杂模式，但了解学习过程中更重要的信号部分可以让它们受益匪浅。在本文中，我们引入了一个双通道深度神经网络来更准确地检测心电图信号中房颤的存在。第一个通道接收心电图信号并自动学习在哪里进行 AF 检测。第二个通道同时接收相同的心电图信号，以考虑整个信号的所有特征。除了提高检测准确性之外，该模型还可以通过可视化指导医生在尝试检测心房颤动时需要注意给定心电图信号的哪些部分。实验结果证实，该模型显着提高了著名的MIT-BIH AF数据库上5秒心电图段的AF检测性能（灵敏度为99.53%，特异性为99.26%，准确度为99.40%）。
The complexity of the patterns associated with atrial fibrillation (AF) and the high level of noise affecting these patterns have significantly limited the application of current signal processing and shallow machine learning approaches to accurately detect this condition. Deep neural networks have shown to be very powerful to learn the non-linear patterns in various problems such as computer vision tasks. While deep learning approaches have been utilized to learn complex patterns related to the presence of AF in electrocardiogram (ECG) signals, they can considerably benefit from knowing which parts of the signal is more important to focus on during learning. In this paper, we introduce a two-channel deep neural network to more accurately detect the presence of AF in the ECG signals. The first channel takes in an ECG signal and automatically learns where to attend for detection of AF. The second channel simultaneously takes in the same ECG signal to consider all features of the entire signal. Besides improving detection accuracy, this model can guide the physicians via visualization that what parts of the given ECG signal are important to attend while trying to detect atrial fibrillation. The experimental results confirm that the proposed model significantly improves the performance of AF detection on well-known MIT-BIH AF database with 5-s ECG segments (achieved a sensitivity of 99.53%, specificity of 99.26% and accuracy of 99.40%).