Tripathy Rajesh Kumar, Dandapat Samarendra
Department of Electronics and Electrical Engineering, Indian Institute of Technology Guwahati, Guwahati 781039, India.
Healthc Technol Lett. 2017 Feb 16;4(2):57-63. doi: 10.1049/htl.2016.0089. eCollection 2017 Apr.
The complex wavelet sub-band bi-spectrum (CWSB) features are proposed for detection and classification of myocardial infarction (MI), heart muscle disease (HMD) and bundle branch block (BBB) from 12-lead ECG. The dual tree CW transform of 12-lead ECG produces CW coefficients at different sub-bands. The higher-order CW analysis is used for evaluation of CWSB. The mean of the absolute value of CWSB, and the number of negative phase angle and the number of positive phase angle features from the phase of CWSB of 12-lead ECG are evaluated. Extreme learning machine and support vector machine (SVM) classifiers are used to evaluate the performance of CWSB features. Experimental results show that the proposed CWSB features of 12-lead ECG and the SVM classifier are successful for classification of various heart pathologies. The individual accuracy values for MI, HMD and BBB classes are obtained as 98.37, 97.39 and 96.40%, respectively, using SVM classifier and radial basis function kernel function. A comparison has also been made with existing 12-lead ECG-based cardiac disease detection techniques.
提出了复小波子带双谱（CWSB）特征，用于从12导联心电图中检测和分类心肌梗死（MI）、心肌疾病（HMD）和束支传导阻滞（BBB）。12导联心电图的双树复小波变换在不同子带产生复小波系数。高阶复小波分析用于评估CWSB。评估了12导联心电图CWSB绝对值的均值、负相位角数量和正相位角特征数量。采用极限学习机和支持向量机（SVM）分类器评估CWSB特征的性能。实验结果表明，所提出的12导联心电图CWSB特征和SVM分类器成功地对各种心脏病变进行了分类。使用SVM分类器和径向基函数核函数时，MI、HMD和BBB类别的个体准确率分别为98.37%、97.39%和96.40%。还与现有的基于12导联心电图的心脏病检测技术进行了比较。
