1D-CADCapsNet: One dimensional deep capsule networks for coronary artery disease detection using ECG signals |
| |
| Institution: | 1. Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore;2. Department of Biomedical Engineering, School of Science and Technology, SIM University, Singapore;3. Department of Biomedical Engineering, Faculty of Engineering, University of Malaya, Malaysia;4. Iwate Prefectural University (IPU), Faculty of Software and Information Science, Iwate 020-0693 Japan;5. Department of Cardiology, National Heart Centre, Singapore;1. Clinical Research Institute, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea;2. Department of Electronic Engineering, Sogang University, Seoul, Republic of Korea;3. Department of Radiology and HVSI Imaging Center, Heart Vascular Stroke Institute, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea;2. Department of Anesthesiology, Perioperative, and Pain Medicine Icahn School of Medicine at Mount Sinai, New York, NY;3. Department of Anesthesia, Critical Care and Pain Medicine Beth Israel, Deaconess Medical Center, Boston, MA;4. Department of Anesthesiology and Critical Care, Perelman School of Medicine University of Pennsylvania, Philadelphia, PA;5. Department of Anesthesiology and Perioperative Medicine, University of Alabama at Birmingham School of Medicine, Birmingham, AL;11. Department of Anesthesiology, University of Iowa Hospitals and Clinics, Iowa City, IA;12. Department of Anesthesiology and Perioperative Medicine, Mayo Clinic, Rochester, MN |
| |
| Abstract: | PurposeCardiovascular disease (CVD) is a leading cause of death globally. Electrocardiogram (ECG), which records the electrical activity of the heart, has been used for the diagnosis of CVD. The automated and robust detection of CVD from ECG signals plays a significant role for early and accurate clinical diagnosis. The purpose of this study is to provide automated detection of coronary artery disease (CAD) from ECG signals using capsule networks (CapsNet).MethodsDeep learning-based approaches have become increasingly popular in computer aided diagnosis systems. Capsule networks are one of the new promising approaches in the field of deep learning. In this study, we used 1D version of CapsNet for the automated detection of coronary artery disease (CAD) on two second (95,300) and five second-long (38,120) ECG segments. These segments are obtained from 40 normal and 7 CAD subjects. In the experimental studies, 5-fold cross validation technique is employed to evaluate performance of the model.ResultsThe proposed model, which is named as 1D-CADCapsNet, yielded a promising 5-fold diagnosis accuracy of 99.44% and 98.62% for two- and five-second ECG signal groups, respectively. We have obtained the highest performance results using 2 s ECG segment than the state-of-art studies reported in the literature.Conclusions1D-CADCapsNet model automatically learns the pertinent representations from raw ECG data without using any hand-crafted technique and can be used as a fast and accurate diagnostic tool to help cardiologists. |
| |
| Keywords: | Capsule networks Coronary artery disease Deep learning ECG signals |
| 本文献已被 ScienceDirect 等数据库收录！ |
|
