利用心动周期的深度学习从单导联心电图中自动检测心房颤动。

IF 5 Q1 ENGINEERING, BIOMEDICAL BME frontiers Pub Date : 2022-04-12 eCollection Date: 2022-01-01 DOI:10.34133/2022/9813062
Alina Dubatovka, Joachim M Buhmann
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引用次数: 0

摘要

目标和影响声明。心房颤动(AF)是一种严重的疾病,需要及时有效的治疗来预防中风。我们探索了深度神经网络(DNN),用于学习心动周期并从单导联心电图(ECG)信号中可靠地检测AF。介绍心电图被广泛用于诊断包括房颤在内的各种心脏功能障碍。大量收集的心电图和最近使用DNN处理时间序列数据的算法进步大大提高了房颤诊断的准确性。然而,DNN通常被设计为通用的黑盒模型,并且缺乏其决策的可解释性。方法。我们设计了一个从心电图中检测AF的三步流水线。首先,基于R峰值检测,将记录分割为单个心跳序列。然后使用DNN对单个心跳进行编码,该DNN通过将心跳的持续时间与其形状解开来提取心跳的可解释特征。其次,将心跳代码序列传递给DNN以组合捕获心律的信号电平表示。第三,将信号表示传递给DNN以检测AF。结果。我们的方法在AF检测方面的性能优于现有的ECG分析方法。此外,该方法提供了DNN从心跳中提取的特征的解释,并使心脏病专家能够根据单个心跳的形状和整个信号的节律来研究心电图。结论通过在两个水平上考虑心电图,并使用DNN对心动周期进行建模,这项工作提出了一种从单导联心电图中可靠检测AF的方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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Automatic Detection of Atrial Fibrillation from Single-Lead ECG Using Deep Learning of the Cardiac Cycle.

Objective and Impact Statement. Atrial fibrillation (AF) is a serious medical condition that requires effective and timely treatment to prevent stroke. We explore deep neural networks (DNNs) for learning cardiac cycles and reliably detecting AF from single-lead electrocardiogram (ECG) signals. Introduction. Electrocardiograms are widely used for diagnosis of various cardiac dysfunctions including AF. The huge amount of collected ECGs and recent algorithmic advances to process time-series data with DNNs substantially improve the accuracy of the AF diagnosis. DNNs, however, are often designed as general purpose black-box models and lack interpretability of their decisions. Methods. We design a three-step pipeline for AF detection from ECGs. First, a recording is split into a sequence of individual heartbeats based on R-peak detection. Individual heartbeats are then encoded using a DNN that extracts interpretable features of a heartbeat by disentangling the duration of a heartbeat from its shape. Second, the sequence of heartbeat codes is passed to a DNN to combine a signal-level representation capturing heart rhythm. Third, the signal representations are passed to a DNN for detecting AF. Results. Our approach demonstrates a superior performance to existing ECG analysis methods on AF detection. Additionally, the method provides interpretations of the features extracted from heartbeats by DNNs and enables cardiologists to study ECGs in terms of the shapes of individual heartbeats and rhythm of the whole signals. Conclusion. By considering ECGs on two levels and employing DNNs for modelling of cardiac cycles, this work presents a method for reliable detection of AF from single-lead ECGs.

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审稿时长
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