A modeling and machine learning approach to ECG feature engineering for the detection of ischemia using pseudo-ECG
A modeling and machine learning approach to ECG feature engineering for the detection of ischemia using pseudo-ECG
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DOI：
10.1371/journal.pone.0220294
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发表时间：
2019-08-12
期刊：
影响因子：
3.7
通讯作者：
Diaz-Zuccarini, Vanessa
中科院分区：
文献类型：
--
作者：
Ledezma, Carlos A.;Zhou, Xin;Diaz-Zuccarini, Vanessa
冠心病（CHD）的早期发现有可能防止这种疾病每年在全世界造成的数百万人死亡。然而，存在很少的自动方法来在早期阶段检测CHD。开发这些方法的一个挑战是缺乏相关的数据集进行培训和验证。在此，使用Tusscher-Panfilov 2006模型和O 'Hara-Rudy模型（用于人肌细胞）创建两个模型群体，这两个模型群体与从健康个体（对照群体）获得的数据一致，并包括受试者间变异性。随后将缺血的影响包括在对照群体中，以模拟轻度和重度缺血事件对单个细胞、完全缺血的细胞缆线和具有各种大小的缺血区域的细胞缆线的影响。测量动作电位和伪ECG生物标志物以评估如何量化缺血的演变。最后，训练两个神经网络分类器，以使用伪ECG生物标志物识别不同程度的缺血。对照人群显示生理范围内的动作电位和伪ECG生物标志物，在缺血性人群中观察到缺血性患者中常见的生物标志物趋势。一方面，缺血性伪ECG的受试者间变异性排除了使用任何单一生物标志物检测和分类早期缺血性事件。另一方面，神经网络显示出95%以上的灵敏度和阳性预测值。此外，神经网络显示，与缺血检测相关的生物标志物与其分类相关的生物标志物不同。这项工作表明，当数据稀缺时，可以使用计算方法来验证概念验证机器学习方法以检测缺血事件。
Early detection of coronary heart disease (CHD) has the potential to prevent the millions of deaths that this disease causes worldwide every year. However, there exist few automatic methods to detect CHD at an early stage. A challenge in the development of these methods is the absence of relevant datasets for their training and validation. Here, the ten Tusscher-Panfilov 2006 model and the O'Hara-Rudy model for human myocytes were used to create two populations of models that were in concordance with data obtained from healthy individuals (control populations) and included inter-subject variability. The effects of ischemia were subsequently included in the control populations to simulate the effects of mild and severe ischemic events on single cells, full ischemic cables of cells and cables of cells with various sizes of ischemic regions. Action potential and pseudo-ECG biomarkers were measured to assess how the evolution of ischemia could be quantified. Finally, two neural network classifiers were trained to identify the different degrees of ischemia using the pseudo-ECG biomarkers. The control populations showed action potential and pseudo-ECG biomarkers within the physiological ranges and the trends in the biomarkers commonly identified in ischemic patients were observed in the ischemic populations. On the one hand, inter-subject variability in the ischemic pseudo-ECGs precluded the detection and classification of early ischemic events using any single biomarker. On the other hand, the neural networks showed sensitivity and positive predictive value above 95%. Additionally, the neural networks revealed that the biomarkers that were relevant for the detection of ischemia were different from those relevant for its classification. This work showed that a computational approach could be used, when data is scarce, to validate proof-of-concept machine learning methods to detect ischemic events.