基于深度神经网络的心电图描记。
Electrocardiogram Delineation Using Deep Neural Networks.
机构信息
Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, Vienna, Austria.
Ludwig Boltzmann Institute for Cardiovascular Engineering, Vienna, Austria.
出版信息
Stud Health Technol Inform. 2022 May 16;293:117-118. doi: 10.3233/SHTI220356.
BACKGROUND
In recent years, there has been a rising interest in the application of deep neural networks (DNN) for the delineation of the electrocardiogram (ECG).
OBJECTIVES
A variety of DNN architectures has been investigated in a 5-fold cross-validation approach.
RESULTS
The best performing network achieved 100% sensitivity and >97% positive predictive value for all ECG waves.
CONCLUSION
Our DNN could achieve similar classification performance as other DNN approaches described in the literature at a reduced computational cost.
背景
近年来，人们对将深度学习神经网络（DNN）应用于心电图（ECG）描记的兴趣日益浓厚。
目的
采用 5 折交叉验证方法研究了各种 DNN 架构。
结果
表现最佳的网络对所有 ECG 波的敏感性达到 100%，阳性预测值>97%。
结论
与文献中描述的其他 DNN 方法相比，我们的 DNN 可以以较低的计算成本实现类似的分类性能。
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