PDF(Pubmed)
Abstract：
Background: Left ventricular hypertrophy (LVH) is a powerful predictor of future cardiovascular events. Objectives: The objectives of this study were to conduct a systematic review of machine learning (ML) algorithms for the identification of LVH and compare them with respect to the classical features of test sensitivity, specificity, accuracy, ROC and the traditional ECG criteria for LVH. Methods: A search string was constructed with the operators \"left ventricular hypertrophy, electrocardiogram\" AND machine learning; then, Medline and PubMed were systematically searched. Results: There were 14 studies that examined the detection of LVH utilizing the ECG and utilized at least one ML approach. ML approaches encompassed support vector machines, logistic regression, Random Forest, GLMNet, Gradient Boosting Machine, XGBoost, AdaBoost, ensemble neural networks, convolutional neural networks, deep neural networks and a back-propagation neural network. Sensitivity ranged from 0.29 to 0.966 and specificity ranged from 0.53 to 0.99. A comparison with the classical ECG criteria for LVH was performed in nine studies. ML algorithms were universally more sensitive than the Cornell voltage, Cornell product, Sokolow-Lyons or Romhilt-Estes criteria. However, none of the ML algorithms had meaningfully better specificity, and four were worse. Many of the ML algorithms included a large number of clinical (age, sex, height, weight), laboratory and detailed ECG waveform data (P, QRS and T wave), making them difficult to utilize in a clinical screening situation. Conclusions: There are over a dozen different ML algorithms for the detection of LVH on a 12-lead ECG that use various ECG signal analyses and/or the inclusion of clinical and laboratory variables. Most improved in terms of sensitivity, but most also failed to outperform specificity compared to the classic ECG criteria. ML algorithms should be compared or tested on the same (standard) database.
摘要：
背景：左心室肥厚（LVH）是未来心血管事件的有力预测因子。目标：本研究的目的是对用于识别LVH的机器学习（ML）算法进行系统回顾，并将其与测试灵敏度的经典特征进行比较。特异性，准确度,ROC和LVH的传统ECG标准。方法：用操作者“左心室肥厚，心电图和机器学习；然后，系统地搜索了Medline和PubMed。结果：有14项研究使用ECG检查了LVH的检测，并使用了至少一种ML方法。机器学习方法包括支持向量机，逻辑回归，随机森林,GLMNet,梯度增压机，XGBoost,AdaBoost,集成神经网络，卷积神经网络，深度神经网络和反向传播神经网络。敏感性为0.29至0.966，特异性为0.53至0.99。在9项研究中进行了与经典ECGLVH标准的比较。ML算法普遍比康奈尔电压更敏感，康奈尔产品，Sokolow-Lyons或Romhilt-Estes标准。然而,没有一个ML算法具有有意义的更好的特异性，四个更糟。许多ML算法包括大量的临床(年龄，性别,高度,weight),实验室和详细的心电图波形数据(P，QRS和T波)，使它们难以在临床筛查情况下使用。结论:有超过十种不同的ML算法用于在12导联ECG上检测LVH,其使用各种ECG信号分析和/或包括临床和实验室变量。在灵敏度方面得到了最大的改善，但与经典ECG标准相比,大多数也未能优于特异性.ML算法应在相同（标准）数据库上进行比较或测试。
