Bergquist Jake A, Zenger Brian, Brundage James, MacLeod Rob S, Shah Rashmee, Ye Xiangyang, Lyones Ann, Ranjan Ravi, Tasdizen Tolga, Bunch T Jared, Steinberg Benjamin A
Scientific Computing and Imaging Institute, University of Utah, SLC, UT, USA.
Nora Eccles Treadwell CVRTI, University of Utah, SLC, UT, USA.
Comput Cardiol (2010). 2023 Oct;50. doi: 10.22489/cinc.2023.047. Epub 2023 Dec 26.
The 12-lead electrocardiogram (ECG) is the most common front-line diagnosis tool for assessing cardiovascular health, yet traditional ECG analysis cannot detect many diseases. Machine learning (ML) techniques have emerged as a powerful set of techniques for producing automated and robust ECG analysis tools that can often predict diseases and conditions not detectable by traditional ECG analysis. Many contemporary ECG-ML studies have focused on utilizing the full 12-lead ECG; however, with the increased availability of single-lead ECG data from wearable devices, there is a clear motivation to explore the development of single-lead ECG-ML techniques. In this study we developed and applied a deep learning architecture for the detection of low left ventricular ejection fraction (LVEF), and compared the performance of this architecture when it was trained with individual leads of the 12-lead ECG to the performance when trained using the entire 12-lead ECG. We observed that single-lead-trained networks performed similarly to the full 12-lead-trained network. We also noted patterns of agreement and disagreement between network low LVEF predictions across the different lead-trained networks.
12导联心电图（ECG）是评估心血管健康最常用的一线诊断工具，然而传统的心电图分析无法检测出许多疾病。机器学习（ML）技术已成为一套强大的技术，可用于生成自动化且强大的心电图分析工具，这些工具通常能够预测传统心电图分析无法检测到的疾病和状况。许多当代的心电图-机器学习研究都集中在利用完整的12导联心电图上；然而，随着可穿戴设备中单导联心电图数据的可用性增加，探索单导联心电图-机器学习技术的发展显然很有必要。在本研究中，我们开发并应用了一种深度学习架构来检测左心室射血分数（LVEF）降低的情况，并将该架构在使用12导联心电图的各个导联进行训练时的性能与使用整个12导联心电图进行训练时的性能进行了比较。我们观察到，单导联训练的网络与全12导联训练的网络表现相似。我们还注意到不同导联训练的网络在低LVEF预测方面的一致和不一致模式。
