Urteaga Jon, Aramendi Elisabete, Elola Andoni, Irusta Unai, Idris Ahamed
Department of Communications Engineering, University of the Basque Country, 48013 Bilbao, Spain.
Biocruces Bizkaia Health Research Institute, Cruces University Hospital, 48903 Baracaldo, Spain.
Entropy (Basel). 2021 Jun 30;23(7):847. doi: 10.3390/e23070847.
Pulseless electrical activity (PEA) is characterized by the disassociation of the mechanical and electrical activity of the heart and appears as the initial rhythm in 20-30% of out-of-hospital cardiac arrest (OHCA) cases. Predicting whether a patient in PEA will convert to return of spontaneous circulation (ROSC) is important because different therapeutic strategies are needed depending on the type of PEA. The aim of this study was to develop a machine learning model to differentiate PEA with unfavorable (unPEA) and favorable (faPEA) evolution to ROSC. An OHCA dataset of 1921 5s PEA signal segments from defibrillator files was used, 703 faPEA segments from 107 patients with ROSC and 1218 unPEA segments from 153 patients with no ROSC. The solution consisted of a signal-processing stage of the ECG and the thoracic impedance (TI) and the extraction of the TI circulation component (ICC), which is associated with ventricular wall movement. Then, a set of 17 features was obtained from the ECG and ICC signals, and a random forest classifier was used to differentiate faPEA from unPEA. All models were trained and tested using patientwise and stratified 10-fold cross-validation partitions. The best model showed a median (interquartile range) area under the curve (AUC) of 85.7(9.8)% and a balance accuracy of 78.8(9.8)%, improving the previously available solutions at more than four points in the AUC and three points in balanced accuracy. It was demonstrated that the evolution of PEA can be predicted using the ECG and TI signals, opening the possibility of targeted PEA treatment in OHCA.
无脉电活动（PEA）的特征是心脏机械活动与电活动分离，在20%-30%的院外心脏骤停（OHCA）病例中表现为初始心律。预测PEA患者是否会恢复自主循环（ROSC）很重要，因为根据PEA的类型需要不同的治疗策略。本研究的目的是开发一种机器学习模型，以区分发展为ROSC的可能性不佳（unPEA）和良好（faPEA）的PEA。使用了一个来自除颤器文件的包含1921个5秒PEA信号段的OHCA数据集，其中703个faPEA段来自107例恢复自主循环的患者，1218个unPEA段来自153例未恢复自主循环的患者。该解决方案包括心电图和胸阻抗（TI）的信号处理阶段以及与心室壁运动相关的TI循环成分（ICC）的提取。然后，从心电图和ICC信号中获得一组17个特征，并使用随机森林分类器区分faPEA和unPEA。所有模型均使用患者层面和分层的10折交叉验证分区进行训练和测试。最佳模型的曲线下面积（AUC）中位数（四分位间距）为85.7（9.8）%，平衡准确率为78.8（9.8）%，在AUC方面比之前可用的解决方案提高了四个多百分点，在平衡准确率方面提高了三个百分点。结果表明，使用心电图和TI信号可以预测PEA的发展情况，这为OHCA中针对性的PEA治疗开辟了可能性。
