Bashar Syed Khairul, Han Dong, Zieneddin Fearass, Ding Eric, Fitzgibbons Timothy P, Walkey Allan J, McManus David D, Javidi Bahram, Chon Ki H
IEEE Trans Biomed Eng. 2021 Feb;68(2):448-460. doi: 10.1109/TBME.2020.3004310. Epub 2021 Jan 20.
Detection of Atrial fibrillation (AF) from premature atrial contraction (PAC) and premature ventricular contraction (PVC) is difficult as frequent occurrences of these ectopic beats can mimic the typical irregular patterns of AF. In this paper, we present a novel density Poincaré plot-based machine learning method to detect AF from PAC/PVCs using electrocardiogram (ECG) recordings.
First, we propose the generation of this new density Poincaré plot which is derived from the difference of the heart rate (DHR) and provides the overlapping phase-space trajectory information of the DHR. Next, from this density Poincaré plot, several image processing domain-based approaches including statistical central moments, template correlation, Zernike moment, discrete wavelet transform and Hough transform features are used to extract suitable features. Subsequently, the infinite latent feature selection algorithm is implemented to rank the features. Finally, classification of AF vs. PAC/PVC is performed using K-Nearest Neighbor, Support vector machine (SVM) and Random Forest (RF) classifiers. Our method is developed and validated using a subset of Medical Information Mart for Intensive Care (MIMIC) III database containing 10 AF and 10 PAC/PVC subjects. Results- During the segment-wise 10-fold cross-validation, SVM achieved the best performance with 98.99% sensitivity, 95.18% specificity and 97.45% accuracy with the extracted features. In subject-wise scenario, RF achieved the highest accuracy of 91.93%. Moreover, we further validated the proposed method using two other databases: wearable armband ECG data and the Physionet AFPDB. 100% PAC detection accuracy was obtained for both databases without any further training.
Our proposed density Poincaré plot-based method showed superior performance when compared with four existing algorithms; thus showing the efficacy of the extracted image domain-based features.
From intensive care unit's ECG to wearable armband ECGs, the proposed method is shown to discriminate PAC/PVCs from AF with high accuracy.
从房性早搏（PAC）和室性早搏（PVC）中检测房颤（AF）具有挑战性，因为这些异位搏动的频繁出现可能会模仿房颤典型的不规则模式。在本文中，我们提出了一种基于密度庞加莱图的新型机器学习方法，用于使用心电图（ECG）记录从PAC/PVC中检测房颤。
首先，我们提出生成这种新的密度庞加莱图，它由心率差（DHR）导出，并提供DHR的重叠相空间轨迹信息。接下来，从这个密度庞加莱图中，使用包括统计中心矩、模板相关性、泽尼克矩、离散小波变换和霍夫变换特征在内的几种基于图像处理领域的方法来提取合适的特征。随后，实施无限潜在特征选择算法对特征进行排序。最后，使用K近邻、支持向量机（SVM）和随机森林（RF）分类器对房颤与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10名房颤患者和10名PAC/PVC患者。结果——在逐段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98.99%，特异性为95.18%，准确率为97.45%。在逐个受试者的情况下，RF实现了91.93%的最高准确率。此外，我们使用另外两个数据库进一步验证了所提出的方法：可穿戴臂带ECG数据和Physionet AFPDB。两个数据库均在无需进一步训练的情况下获得了100%的PAC检测准确率。
与四种现有算法相比，我们提出的基于密度庞加莱图的方法表现出卓越的性能；从而证明了基于图像域提取特征的有效性。
从重症监护病房的心电图到可穿戴臂带心电图，所提出的方法被证明能够高精度地区分PAC/PVC与房颤。
