Rad Ali Bahrami, Eftestol Trygve, Engan Kjersti, Irusta Unai, Kvaloy Jan Terje, Kramer-Johansen Jo, Wik Lars, Katsaggelos Aggelos K
IEEE Trans Biomed Eng. 2017 Oct;64(10):2411-2418. doi: 10.1109/TBME.2017.2688380. Epub 2017 Mar 30.
There is a need to monitor the heart rhythm in resuscitation to improve treatment quality. Resuscitation rhythms are categorized into: ventricular tachycardia (VT), ventricular fibrillation (VF), pulseless electrical activity (PEA), asystole (AS), and pulse-generating rhythm (PR). Manual annotation of rhythms is time-consuming and infeasible for large datasets. Our objective was to develop ECG-based algorithms for the retrospective and automatic classification of resuscitation cardiac rhythms.
The dataset consisted of 1631 3-s ECG segments with clinical rhythm annotations, obtained from 298 out-of-hospital cardiac arrest patients. In total, 47 wavelet- and time-domain-based features were computed from the ECG. Features were selected using a wrapper-based feature selection architecture. Classifiers based on Bayesian decision theory, k-nearest neighbor, k-local hyperplane distance nearest neighbor, artificial neural network (ANN), and ensemble of decision trees were studied.
The best results were obtained for ANN classifier with Bayesian regularization backpropagation training algorithm with 14 features, which forms the proposed algorithm. The overall accuracy for the proposed algorithm was 78.5%. The sensitivities (and positive-predictive-values) for AS, PEA, PR, VF, and VT were 88.7% (91.0%), 68.9% (70.4%), 65.9% (69.0%), 86.2% (83.8%), and 78.8% (72.9%), respectively.
The results demonstrate that it is possible to classify resuscitation cardiac rhythms automatically, but the accuracy for the organized rhythms (PEA and PR) is low.
We have made an important step toward making classification of resuscitation rhythms more efficient in the sense of minimal feedback from human experts.
在复苏过程中需要监测心律以提高治疗质量。复苏心律分为：室性心动过速（VT）、心室颤动（VF）、无脉电活动（PEA）、心搏停止（AS）和有脉搏心律（PR）。人工标注心律耗时且对于大型数据集不可行。我们的目标是开发基于心电图的算法，用于对复苏心律进行回顾性自动分类。
数据集由1631个3秒心电图片段组成，带有临床心律标注，取自298例院外心脏骤停患者。总共从心电图中计算了47个基于小波和时域的特征。使用基于包装器的特征选择架构来选择特征。研究了基于贝叶斯决策理论、k近邻、k局部超平面距离近邻、人工神经网络（ANN）和决策树集成的分类器。
采用具有贝叶斯正则化反向传播训练算法且包含14个特征的ANN分类器获得了最佳结果，这构成了所提出的算法。所提出算法的总体准确率为78.5%。AS、PEA、PR、VF和VT的敏感性（以及阳性预测值）分别为88.7%（91.0%）、68.9%（70.4%）、65.9%（69.0%）、86.2%（83.8%）和78.8%（72.9%）。
结果表明自动分类复苏心律是可行的，但对于规整心律（PEA和PR）的准确率较低。
在从人类专家获得最少反馈的意义上，我们朝着使复苏心律分类更高效迈出了重要一步。
