An efficient automated technique for CAD diagnosis using flexible analytic wavelet transform and entropy features extracted from HRV signals |
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| Affiliation: | 1. Discipline of Electrical Engineering, Indian Institute of Technology Indore, Indore, 453552, India;2. Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore 599489, Singapore;1. Department of Mathematics, VIT University Vellore Campus, Tamil Nadu, INDIA;2. Department of Mathematics, NIT, Silchar, Assam, India;1. Facultad de Ingeniería Informática, Instituto Superior Politécnico “José Antonio Echeverría” (CUJAE), Marianao 19390, La Habana, Cuba;2. Dep. Ingeniería del Software e Inteligencia Artificial, Universidad Complutense de Madrid, Spain;1. National Key Lab for Novel Software Technology, Nanjing University, China;2. Computer Vision Laboratory, ETH Zurich, Switzerland;3. Faculty of Computer Science and Information Technology, University of Malaya, Malaysia;4. School of Computing, National University of Singapore, Singapore;1. Institute of Computer and Communication Engineering, National Cheng Kung University, Tainan 701, Taiwan, ROC;2. Department of Electronic Engineering, National Kaohsiung University of Applied Sciences, Kaohsiung 807, Taiwan, ROC;3. Department of Information Management, Lunghwa University of Science and Technology, Taoyuan 333, Taiwan, ROC;1. School of Computing, National University of Singapore, 117417, Singapore;2. Handal Indah Sdn Bhd, 728789, Singapore;3. Department of Computer and Information, Hefei University of Technology, Anhui, 230009, China;4. National University of Singapore Research Institute, Suzhou, 215123, China;5. College of Computer Science, Zhejiang University, Hangzhou, 310027, China |
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| Abstract: | Coronary Artery Disease (CAD) causes maximum death among all types of heart disorders. An early detection of CAD can save many human lives. Therefore, we have developed a new technique which is capable of detecting CAD using the Heart Rate Variability (HRV) signals. These HRV signals are decomposed to sub-band signals using Flexible Analytic Wavelet Transform (FAWT). Then, two nonlinear parameters namely; K-Nearest Neighbour (K-NN) entropy estimator and Fuzzy Entropy (FzEn) are extracted from the decomposed sub-band signals. Ranking methods namely Wilcoxon, entropy, Receiver Operating Characteristic (ROC) and Bhattacharya space algorithm are implemented to optimize the performance of the designed system. The proposed methodology has shown better performance using entropy ranking technique. The Least Squares-Support Vector Machine (LS-SVM) with Morlet wavelet and Radial Basis Function (RBF) kernels obtained the highest classification accuracy of 100% for the diagnosis of CAD. The developed novel algorithm can be used to design an expert system for the diagnosis of CAD automatically using Heart Rate (HR) signals. Our system can be used in hospitals, polyclinics and community screening to aid the cardiologists in their regular diagnosis. |
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