Myocardial Ischemia Detection Using Body Surface Potential Mappings and Machine Learning.
Myocardial Ischemia Detection Using Body Surface Potential Mappings and Machine Learning.
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使用体表电位映射和机器学习进行心肌缺血检测。
DOI：
10.23919/cinc53138.2021.9662808
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发表时间：
2021-09
期刊：
影响因子：
--
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中科院分区：
文献类型：
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作者：
现代机器学习推动了通过无创体表记录检测急性心肌缺血的最新进展。虽然已经使用单导联和 12 导联心电图进行了广泛的研究，但几乎没有模型包含体表电位映射。我们创建了两种对比的机器学习模型：逻辑回归和 XGBoost 分类器，并根据从心脏内部记录的真实缺血测量结果通过实验获取的体表映射对它们进行训练。这些模型的平均准确度为 96.46% 和 97.63%，Logistic 回归和 XGBoost 分类器的平均 AUC 分别为 0.9927 和 0.9972。每个电极的解剖位置和相对贡献被可视化并排序。然后，仅使用来自前 12、8 和 3 个电极的数据来训练新模型。这些仅在电极子集上训练的模型仍然表现出相对较高的准确性和 AUC，尽管训练时间要快得多。
Recent improvements in detecting acute myocardial ischemia via noninvasive body surface recordings have been driven by modern machine learning. While extensive research has been done using single and 12 lead ECGs, almost no models have incorporated body surface potential mappings. We created two contrasting machine learning models, logistic regression and XGBoost Classifier, and trained them on experimentally acquired body surface mappings with ground truth ischemia measurements recorded from within the heart. These models achieved a mean accuracy of 96.46% and 97.63%, as well as a mean AUC of 0.9927 and 0.9972 for the Logistic Regression and XGBoost classifiers, respectively. The anatomical location and relative contribution of each electrode were visualized and ranked. Then, new models were trained using data from only the top 12, 8, and 3 electrodes. These models trained on only a subset of the electrodes still exhibited relatively high accuracy and AUC, although at much faster training times.
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影响因子：
3.6
作者：
Ornato, Joseph P.;Menown, Ian B. A.;Adgey, Jennifer
通讯作者：
Adgey, Jennifer
影响因子：
20.1
作者：
Trayanova NA;Popescu DM;Shade JK
通讯作者：
Shade JK
影响因子：
3.2
作者：
Zenger B;Good WW;Bergquist JA;Burton BM;Tate JD;Berkenbile L;Sharma V;MacLeod RS
通讯作者：
MacLeod RS
影响因子：
6.2
作者：
Hoekstra, James W.;O'Neill, Brian J.;Krucoff, Mitchell
通讯作者：
Krucoff, Mitchell
DOI：
10.1023/a:1016364622124
发表时间：
2002-09-01
期刊：
Cardiac electrophysiology review
影响因子：
--
作者：
Stern, Shlomo
通讯作者：
Stern, Shlomo