Automatic recognition of coronary artery disease and congestive heart failure using a multi-granularity cascaded hybrid network

Numerous researchers have developed electrocardiogram (ECG) classification systems to automatically diagnose coronary artery disease (CAD) and congestive heart failure (CHF). However, they have failed to assess the applicability of their proposed methods on inter-patient, noisy, imbalanced and small-scale data, limiting their use in real-world situations. To address these issues, a multi-granularity cascaded hybrid network (MGCH-Net) is proposed to automatically identify abnormal ECGs. MGCH-Net mainly consists of a multi-granularity cascaded task-related component analysis (TRCA)-principal component analysis (PCA) network (MGC-TPNet), a multi-granularity cascaded independent component analysis (ICA)-PCA network (MGC-IPNet), and a cascaded weighted averaging and Dempster–Shafer (CWA-DS) method. Among them, MGC-TPNet extracts multi-scale inter-lead correlation features from multi-lead ECGs, by which the information from the sagittal and horizontal planes of the heart is comprehensively utilized by maximizing the reproducibility under the same task. Then, MGC-IPNet mines multi-scale intra-lead specificity features from more-valuable-lead ECGs. Finally, CWA-DS is employed to fuse linear support vector machine (SVM)- and softmax-based decision probabilities for the above features to obtain the final results. Since the convolutional kernels of MGCH-Net can be directly calculated through TRCA, PCA, and FastICA algorithms, multiple iterative processes are not required. Hence, MGCH-Net has a faster training speed than most convolutional neural networks. In this work, accuracies of 99.92% and 97.46% were achieved in recognizing normal, CAD and CHF heartbeats in intra- and inter-patient experiments, respectively. In addition, MGCH-Net performed well on imbalanced, noisy, and small-scale data. These results show that our method outperforms the state-of-the-art methods.