论文标题
Arnet-ECG：从原始心电图中检测房颤的深度学习
ArNet-ECG: Deep Learning for the Detection of Atrial Fibrillation from the Raw Electrocardiogram
论文作者
论文摘要
心房颤动（AF）是最普遍的心律失常。 AF在心电图（ECG）上表现出了不规则的节拍时间间隔的变化，不存在P波和纤颤波（F-Wave）的存在。我们假设对原始心电图进行培训的深度学习方法（DL）将使对AF事件的可靠检测和对AF负担的估计（AFB）。我们进一步假设，使用Beat-beat间隔变化时间序列序列序列的原始ECG的性能利用了原始ECG的优势。因此，我们开发了一种新的DL算法（表示Arnet-Ecg），以稳健地检测AF事件并估算AFB的原始ECG，并基于该算法对先前的工作进行基准测试。方法：使用弗吉尼亚大学（UVAF）的53,753小时的连续ECG，其中包括2,247名成年患者，总计超过53,753小时。结果：ARNET-ECG获得了0.96的F1，Arnet2获得了F1 0.94。讨论和结论：ARNET-ECG的表现优于ARNET2，因此表明使用RAW ECG在Beat-Beat间隔时间序列上提供了额外的性能。解释ARNET-ECG表现较高的主要原因是其在心房颤动实例上的高性能与ARNET2的这些录音表现不佳。
Atrial fibrillation (AF) is the most prevalent heart arrhythmia. AF manifests on the electrocardiogram (ECG) though irregular beat-to-beat time interval variation, the absence of P-wave and the presence of fibrillatory waves (f-wave). We hypothesize that a deep learning (DL) approach trained on the raw ECG will enable robust detection of AF events and the estimation of the AF burden (AFB). We further hypothesize that the performance reached leveraging the raw ECG will be superior to previously developed methods using the beat-to-beat interval variation time series. Consequently, we develop a new DL algorithm, denoted ArNet-ECG, to robustly detect AF events and estimate the AFB from the raw ECG and benchmark this algorithms against previous work. Methods: A dataset including 2,247 adult patients and totaling over 53,753 hours of continuous ECG from the University of Virginia (UVAF) was used. Results: ArNet-ECG obtained an F1 of 0.96 and ArNet2 obtained an F1 0.94. Discussion and conclusion: ArNet-ECG outperformed ArNet2 thus demonstrating that using the raw ECG provides added performance over the beat-to-beat interval time series. The main reason found for explaining the higher performance of ArNet-ECG was its high performance on atrial flutter examples versus poor performance on these recordings for ArNet2.
