A novel convolutional neural network for reconstructing surface electrocardiograms from intracardiac electrograms and vice versa.
A novel convolutional neural network for reconstructing surface electrocardiograms from intracardiac electrograms and vice versa.
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DOI：
10.1016/j.artmed.2021.102135
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发表时间：
2021-08
影响因子：
7.5
通讯作者：
Aazhang B
中科院分区：
文献类型：
--
作者：
Banta A;Cosentino R;John MM;Post A;Buchan S;Razavi M;Aazhang B
我们提出了一种新的卷积神经网络框架，用于将多变量输入映射到多变量输出。特别是，我们实现我们的算法范围内的12导联体表心电图（ECG）重建心内电图（EGM），反之亦然。执行此任务的目的是改善对植入器械的患者的床旁监测，以治疗心脏病。我们将通过12导联心电图重建和提供一种新的诊断工具对五种不同的心电图类型进行分类来实现这一目标。该算法的评价数据集追溯收集14例患者。重建和实际ECG之间的相关系数的计算表明，所提出的卷积神经网络模型表示一种有效的，准确的，和上级的方式来合成12导联ECG相比，以前的方法。我们也可以实现相同的重建精度，只有一个EGM导联作为输入。我们还以非患者特定的方式测试了该模型，并看到了合理的相关系数。该模型还在反方向上执行，以从12导联ECG产生EGM信号，并发现相关性与正方向相当。最后，我们分析了在模型中学习的特征，并确定模型学习了我们的12导联ECG空间的过完备基础。然后，我们使用这个基础上的功能，以创建一个新的诊断工具，用于分类不同的心电图心律失常的MIT-BIH心律失常数据库的平均准确率为0.98。
We propose a novel convolutional neural network framework for mapping a multivariate input to a multivariate output. In particular, we implement our algorithm within the scope of 12-lead surface electrocardiogram (ECG) reconstruction from intracardiac electrograms (EGM) and vice versa. The goal of performing this task is to allow for improved point-of-care monitoring of patients with an implanted device to treat cardiac pathologies. We will achieve this goal with 12-lead ECG reconstruction and by providing a new diagnostic tool for classifying five different ECG types. The algorithm is evaluated on a dataset retroactively collected from 14 patients. Correlation coefficients calculated between the reconstructed and the actual ECG show that the proposed convolutional neural network model represents an efficient, accurate, and superior way to synthesize a 12-lead ECG when compared to previous methods. We can also achieve the same reconstruction accuracy with only one EGM lead as input. We also tested the model in a non-patient specific way and saw a reasonable correlation coefficient. The model was also executed in the reverse direction to produce EGM signals from a 12-lead ECG and found that the correlation was comparable to the forward direction. Lastly, we analyzed the features learned in the model and determined that the model learns an overcomplete basis of our 12-lead ECG space. We then use this basis of features to create a new diagnostic tool for classifying different ECG arrhythmia’s on the MIT-BIH arrhythmia database with an average accuracy of 0.98.
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影响因子：
49.6
作者：
Bui, Anh L.;Horwich, Tamara B.;Fonarow, Gregg C.
通讯作者：
Fonarow, Gregg C.
影响因子：
3.2
作者：
Abdi, Herve;Williams, Lynne J.
通讯作者：
Williams, Lynne J.
DOI：
10.1109/tasl.2008.919072
发表时间：
2008-05-01
影响因子：
--
作者：
Benesty, Jacob;Chen, Jingdong;Huang, Yiteng (Arden)
通讯作者：
Huang, Yiteng (Arden)
影响因子：
2.5
作者：
HOERL, AE;KENNARD, RW
通讯作者：
KENNARD, RW
影响因子：
7.8
作者：
HORNIK, K;STINCHCOMBE, M;WHITE, H
通讯作者：
WHITE, H