Bashar Syed Khairul, Han Dong, Zieneddin Fearass, Ding Eric, Walkey Allan J, McManus David D, Chon Ki H
Annu Int Conf IEEE Eng Med Biol Soc. 2020 Jul;2020:2594-2597. doi: 10.1109/EMBC44109.2020.9175216.
Detection of Atrial fibrillation (AF) from premature atrial contraction (PAC) and premature ventricular contraction (PVC) is challenging as frequent occurrences of these ectopic beats can mimic the typical irregular patterns of AF. In this paper, we present a preliminary study of using density Poincare plot based machine learning method to detect AF from PAC/PVCs using electrocardiogram (ECG) recordings. First, we propose creation of this new density Poincare plot which is derived from the difference of the heart rate. Next, from this density Poincare plot, template correlation and discrete wavelet transform are used to extract suitable image-based features, which is followed by infinite latent feature selection algorithm to rank the features. Finally, classification of AF vs PAC/PVC is performed using K-Nearest Neighbor, discriminant analysis and support vector machine (SVM) classifiers. Our method is developed and validated using a subset of Medical Information Mart for Intensive Care (MIMIC) III database containing 8 AF and 8 PAC/PVC subjects. Both 10-fold and leave-one-subject-out cross validations are performed to show the robustness of our proposed method. During the 10-fold cross-validation, SVM achieved the best performance with 99.49% sensitivity, 94.51% specificity and 97.29% accuracy with the extracted features while for the leave-one-subject-out, the highest overall accuracy is 90.91%. Moreover, when compared with two state-of-the-art methods, the proposed algorithm achieves superior AF vs. PAC/PVC discrimination performance.Clinical Relevance-This preliminary study shows that with the help of density Poincare plot, AF can be separated from PAC/PVC with better accuracy.
从房性早搏（PAC）和室性早搏（PVC）中检测心房颤动（AF）具有挑战性，因为这些异位搏动的频繁出现可能会模仿AF典型的不规则模式。在本文中，我们展示了一项初步研究，即使用基于密度庞加莱图的机器学习方法，通过心电图（ECG）记录从PAC/PVC中检测AF。首先，我们提出创建这种新的密度庞加莱图，它由心率差异得出。接下来，从这个密度庞加莱图中，使用模板相关性和离散小波变换来提取合适的基于图像的特征，随后使用无限潜在特征选择算法对特征进行排序。最后，使用K近邻、判别分析和支持向量机（SVM）分类器对AF与PAC/PVC进行分类。我们的方法是使用重症监护医学信息集市（MIMIC）III数据库的一个子集开发和验证的，该子集包含8名AF患者和8名PAC/PVC患者。进行了10折交叉验证和留一法交叉验证，以证明我们提出的方法的稳健性。在10折交叉验证期间，SVM使用提取的特征实现了最佳性能，灵敏度为99.49%，特异性为94.51%，准确率为97.29%；而对于留一法，最高总体准确率为90.91%。此外，与两种最先进的方法相比，所提出的算法在AF与PAC/PVC的判别性能上更优。临床相关性——这项初步研究表明，借助密度庞加莱图，可以更准确地将AF与PAC/PVC区分开来。
