UTHSC-ORNL Center for Biomedical Informatics, University of Tennessee Health Science Center, Memphis, TN, United States of America.
Physiol Meas. 2018 Mar 27;39(3):035006. doi: 10.1088/1361-6579/aaaa9d.
Atrial fibrillation (AF) is a major cause of hospitalization and death in the United States. Moreover, as the average age of individuals increases around the world, early detection and diagnosis of AF become even more pressing. In this paper, we introduce a novel deep learning architecture for the detection of normal sinus rhythm, AF, other abnormal rhythms, and noise.
We have demonstrated through a systematic approach many hyperparameters, input sets, and optimization methods that yielded influence in both training time and performance accuracy. We have focused on these properties to identify an optimal 13-layer convolutional neural network (CNN) model which was trained on 8528 short single-lead ECG recordings and evaluated on a test dataset of 3658 recordings.
The proposed CNN architecture achieved a state-of-the-art performance in identifying normal, AF and other rhythms with an average F -score of 0.83.
We have presented a robust deep learning-based architecture that can identify abnormal cardiac rhythms using short single-lead ECG recordings. The proposed architecture is computationally fast and can also be used in real-time cardiac arrhythmia detection applications.
心房颤动（AF）是美国住院和死亡的主要原因。此外，随着全球个体平均年龄的增长，早期检测和诊断 AF 变得更加紧迫。在本文中，我们介绍了一种用于检测正常窦性节律、AF、其他异常节律和噪声的新型深度学习架构。
我们通过系统的方法展示了许多超参数、输入集和优化方法，这些方法对训练时间和性能准确性都有影响。我们专注于这些特性，以确定一个最优的 13 层卷积神经网络（CNN）模型，该模型在 8528 个短单导联 ECG 记录上进行训练，并在 3658 个记录的测试数据集上进行评估。
所提出的 CNN 架构在识别正常、AF 和其他节律方面表现出了最先进的性能，平均 F 分数为 0.83。
我们提出了一种基于深度学习的强大架构，可使用短单导联 ECG 记录识别异常心脏节律。所提出的架构计算速度快，也可用于实时心脏心律失常检测应用。
