1D-CADCapsNet: One dimensional deep capsule networks for coronary artery disease detection using ECG signals. (February 2020)

• Record Type: Journal Article
• Authors: Butun, Ertan; Yildirim, Ozal; Talo, Muhammed; Tan, Ru-San; Rajendra Acharya, U.
• Is Part Of: Physica medica. Volume 70(2020)
• Page Start: 39
• Page End: 48
• Publication Date: 2020-02
• Subjects: Capsule networks -- Coronary artery disease -- Deep learning -- ECG signals; Medical physics -- Periodicals; Biophysics -- Periodicals; Biophysics -- Periodicals; Imagerie médicale -- Périodiques; Radiothérapie -- Périodiques; Rayons X -- Sécurité -- Mesures -- Périodiques; Physique -- Périodiques; Médecine -- Périodiques; 610.153
• DOI: 10.1016/j.ejmp.2020.01.007
• Languages: English
• ISSNs: 1120-1797

Abstract

Highlights: A deep learning based 1D-CapsNet model is proposed for the automatic detection of CAD. ECG signals of two- and five-second duration are used. Obtained diagnosis accuracy of 99.44% and 98.62% for 2- and 5-second durations respectively. The proposed system can be used in real-time clinical implementation with a cloud system. Abstract: Purpose: Cardiovascular 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). Methods: Deep 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. Results: The 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. Conclusions: 1D-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.