IEEE/ACM Trans Comput Biol Bioinform. 2019 Jan-Feb;16(1):131-138. doi: 10.1109/TCBB.2018.2846611. Epub 2018 Jun 12.
In this work, arrhythmia appearing with the presence of abnormal heart electrical activity is efficiently recognized and classified. A novel method is proposed for accurate recognition and classification of cardiac arrhythmias. Firstly, P-QRS-T waves is segmented from ECG waveform; secondly, morphological features are extracted from P-QRS-T waves, and ECG segment features are extracted from the selected ECG segment by using PCA and dynamic time warping(DTW); finally, SVM is applied to the features and automatic diagnosis results is presented. ECG data set used is derived from the MIT-BIH in which ECG signals are divided into the four classes: normal beats(N), supraventricular ectopic beats (SVEBs), ventricular ectopic beats (VEBs) and fusion of ventricular and normal (F). Our proposed method can distinguish N, SVEBs, VEBs and F with an accuracy of 97.80 percent. The sensitivities for the classes N, SVEBs, VEBs and F are 99.27, 87.47, 94.71, and 73.88 percent and the positive predictivities are 98.48, 95.25, 95.22 and 86.09 percent respectively. The detection sensitivity of SVEBs and VEBs has a better performance by combining proposed features than by using the ECG morphology or ECG segment features separately. The proposed method is compared with four selected peer algorithms and delivers solid results.
在这项工作中，有效地识别和分类了出现异常心脏电活动的心律失常。提出了一种新的方法来准确识别和分类心脏心律失常。首先，从 ECG 波形中分割 P-QRS-T 波；其次，从 P-QRS-T 波中提取形态特征，并通过 PCA 和动态时间规整（DTW）从选定的 ECG 段中提取 ECG 段特征；最后，将 SVM 应用于特征，并给出自动诊断结果。所使用的 ECG 数据集来自 MIT-BIH，其中 ECG 信号分为四类：正常搏动（N）、室上性异位搏动（SVEB）、室性异位搏动（VEB）和心室与正常搏动的融合（F）。我们提出的方法可以以 97.80%的准确率区分 N、SVEB、VEB 和 F。对于 N、SVEB、VEB 和 F 的灵敏度分别为 99.27%、87.47%、94.71%和 73.88%，阳性预测值分别为 98.48%、95.25%、95.22%和 86.09%。与分别使用 ECG 形态或 ECG 段特征相比，通过组合所提出的特征，SVEB 和 VEB 的检测灵敏度具有更好的性能。将所提出的方法与四个选定的同行算法进行了比较，结果可靠。
