Name
Title | ||
|---|---|---|
A novel method for automated congestive heart failure and coronary artery disease recognition using THC-Net |
Abstract | ||
|---|---|---|
Coronary artery disease (CAD) and congestive heart failure (CHF) lead to many deaths worldwide. Generally, an electrocardiogram (ECG) is employed as the diagnostic tool for CAD/CHF recognition. However, since ECG changes are sometimes subtle, visually distinguishing long-term ECG abnormalities is time consuming and laborious. To address these issues, we proposed a novel two-channel hybrid convolutional network (THC-Net) for automatic ECG recognition. THC-Net contains a canonical correlation analysis (CCA)-principal component analysis (PCA) convolutional network, an independent component analysis (ICA)-PCA convolutional network, and a Dempster-Shafer (D-S) theory-based linear support vector machine (SVM). The CCA-PCA and ICA-PCA convolutional networks are developed to extract deep features containing the lead-correlation and lead-specific information, respectively, from ECGs. Compared to common convolutional neural networks (CNNs), their kernels can be directly extracted by CCA, ICA, and PCA with a faster training time. Then, the D-S theory-based linear SVM, which can process multi-channel uncertainty information, is employed as the classification model. In this work, an accuracy of 95.54% was obtained for classifying normal, CHF and CAD patients based on leave-one-out cross-validation. Additionally, experiments on multi-level noisy and imbalanced data yielded remarkable results. Hence, the proposed method has the potential to diagnose CAD and CHF in clinical settings. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1016/j.ins.2021.04.036 | Information Sciences |
Keywords | DocType | Volume |
CAD and CHF recognition,Multi-channel ECG classification,CCA-PCA convolutional network,ICA-PCA convolutional network,Dempster-Shafer theory,Support vector machine | Journal | 568 |
ISSN | Citations | PageRank |
0020-0255 | 0 | 0.34 |
References | Authors | |
0 | 8 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Weiyi Yang | 1 | 9 | 3.24 |
| Yujuan Si | 2 | 13 | 4.64 |
| Gong Zhang | 3 | 0 | 0.34 |
| Di Wang | 4 | 0 | 0.34 |
| Meiqi Sun | 5 | 0 | 0.68 |
| Wei Fan | 6 | 0 | 0.34 |
| Xin Liu | 7 | 3919 | 320.56 |
| Liangliang Li | 8 | 0 | 0.34 |