Martínez Coq P, Rey A, Rosso O A, Armentano R, Legnani W
Signal and Image Processing Center (CPSI), Facultad Regional Buenos Aires. Universidad Tecnológica Nacional, Ciudad Autónoma de Buenos Aires C1179AAQ, Argentina.
Physics Institute, Universidade Federal de Alagoas (UFAL), Maceió CEP 57072-900, Brazil.
Chaos. 2022 Dec;32(12):123118. doi: 10.1063/5.0118717.
The aim of this study is to formulate a new methodology based upon informational tools to detect patients with cardiac arrhythmias. As it is known, sudden death is the consequence of a final arrhythmia, and here lies the relevance of the efforts aimed at the early detection of arrhythmias. The information content in the time series from an electrocardiogram (ECG) signal is conveyed in the form of a probability distribution function, to compute the permutation entropy proposed by Bandt and Pompe. This selection was made seeking its remarkable conceptual simplicity, computational speed, and robustness to noise. In this work, two well-known databases were used, one containing normal sinus rhythms and another one containing arrhythmias, both from the MIT medical databank. For different values of embedding time delay τ, normalized permutation entropy and statistical complexity measure are computed to finally represent them on the horizontal and vertical axes, respectively, which define the causal plane H×C. To improve the results obtained in previous works, a feature set composed by these two magnitudes is built to train the following supervised machine learning algorithms: random forest (RF), support vector machine (SVM), and k nearest neighbors (kNN). To evaluate the performance of each classification technique, a 10-fold cross-validation scheme repeated 10 times was implemented. Finally, to select the best model, three quality parameters were computed, namely, accuracy, the area under the receiver operative characteristic (ROC) curve (AUC), and the F1-score. The results obtained show that the best classification model to detect the ECG coming from arrhythmic patients is RF. The values of the quality parameters were at the same levels reported in the available literature using a larger data set, thus supporting this proposal that uses a very small-sized feature space to train the model later used to classify. Summarizing, the attained results show the possibility to discriminate both groups of patients, with normal sinus rhythm or arrhythmic ECG, showing a promising efficiency in the definition of new markers for the detection of cardiovascular pathologies.
本研究的目的是基于信息工具制定一种新方法，以检测心律失常患者。众所周知，猝死是最终心律失常的结果，这就是致力于心律失常早期检测的努力的重要性所在。心电图（ECG）信号的时间序列中的信息内容以概率分布函数的形式传递，以计算Bandt和Pompe提出的排列熵。做出这种选择是因为其具有显著的概念简单性、计算速度和抗噪声鲁棒性。在这项工作中，使用了两个著名的数据库，一个包含正常窦性心律，另一个包含心律失常，均来自麻省理工学院医学数据库。对于不同的嵌入时间延迟τ值，分别计算归一化排列熵和统计复杂度度量，最后分别在水平轴和垂直轴上表示它们，这定义了因果平面H×C。为了改进先前工作中获得的结果，构建了由这两个量组成的特征集，以训练以下监督式机器学习算法：随机森林（RF）、支持向量机（SVM）和k近邻（kNN）。为了评估每种分类技术的性能，实施了重复10次的10折交叉验证方案。最后，为了选择最佳模型，计算了三个质量参数，即准确率、接收器操作特征（ROC）曲线下面积（AUC）和F1分数。获得的结果表明，检测心律失常患者心电图的最佳分类模型是RF。质量参数的值与使用更大数据集的现有文献中报告的水平相同，从而支持了该提议，即使用非常小的特征空间来训练随后用于分类的模型。总之，获得的结果表明有可能区分两组患者，即正常窦性心律或心律失常心电图患者，在定义用于检测心血管疾病的新标志物方面显示出有前景的效率。
