Utilizing ECG-Based Heartbeat Classification for Hypertrophic Cardiomyopathy Identification.
Utilizing ECG-Based Heartbeat Classification for Hypertrophic Cardiomyopathy Identification.
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DOI：
10.1109/tnb.2015.2426213
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发表时间：
2015-07
影响因子：
3.9
通讯作者：
Shatkay H
中科院分区：
文献类型：
--
作者：
Rahman QA;Tereshchenko LG;Kongkatong M;Abraham T;Abraham MR;Shatkay H
肥厚型心肌病（HCM）是一种心血管疾病，其中心肌部分增厚，血流受阻（可能致命）。基于记录心脏电活动的心电图（ECG）的测试可以帮助早期发现HCM患者。本文提出了一种心血管患者分类器，我们开发的HCM患者使用标准的10秒，12导联ECG信号识别。如果患者记录的大多数心跳被认为是HCM的特征，则将其归类为HCM。因此，分类器的基本任务是将从12导联心电图信号中分割出来的单个心跳识别为HCM心跳，其中非HCM心血管患者的心跳用作对照。我们提取了504形态和时间特征-常用的和新开发的-从心电信号的心跳分类。为了评估分类性能，我们使用5折交叉验证训练和测试了随机森林分类器和支持向量机分类器。我们还比较了这两种分类器的性能，通过逻辑回归分类器获得的，前两种方法的表现优于逻辑回归。随机森林和支持向量机分类器的患者分类精度接近0.85。回忆（灵敏度）和特异性约为0.90。我们还进行了特征选择实验，逐渐删除信息量最少的功能，结果表明，一个相对较小的子集264个高信息量的功能可以实现性能指标相比，通过使用完整的功能集。
Hypertrophic cardiomyopathy (HCM) is a cardiovascular disease where the heart muscle is partially thickened and blood flow is (potentially fatally) obstructed. A test based on electrocardiograms (ECG) that record the heart electrical activity can help in early detection of HCM patients. This paper presents a cardiovascular-patient classifier we developed to identify HCM patients using standard 10-seconds, 12-lead ECG signals. Patients are classified as having HCM if the majority of their recorded heartbeats are recognized as characteristic of HCM. Thus, the classifier’s underlying task is to recognize individual heartbeats segmented from 12-lead ECG signals as HCM beats, where heartbeats from non-HCM cardiovascular patients are used as controls. We extracted 504 morphological and temporal features - both commonly used and newly-developed ones - from ECG signals for heartbeat classification. To assess classification performance, we trained and tested a random forest classifier and a support vector machine classifier using 5-fold cross validation. We also compared the performance of these two classifiers to that obtained by a logistic regression classifier, and the first two methods performed better than logistic regression. The patient-classification precision of random forests and of support vector machine classifiers is close to 0.85. Recall (sensitivity) and specificity are approximately 0.90. We also conducted feature selection experiments by gradually removing the least informative features; the results show that a relatively small subset of 264 highly informative features can achieve performance measures comparable to those achieved by using the complete set of features.