An Improved Internet of Thing-based Optimized SVM Approach for ECG-founded Cardiac Arrhythmia Classification

aspg-18-2861-1791423340 20261008013540 American Scientific Publishing Group admin@americaspg.com American Scientific Publishing Group Journal of Intelligent Systems and Internet of Things JISIoT 2769-786X 2690-6791 2024 13 1 An Improved Internet of Thing-based Optimized SVM Approach for ECG-founded Cardiac Arrhythmia Classification Yogendra Narayan Prajapati Assistant Professor CSE, Ajay Kumar Garg Engineering College, Ghaziabad, U.P. India Beemkumar N. Professor, Department of Mechanical Engineering, Faculty of Engineering and Technology, JAIN (Deemed-to-be University), Bangalore, Karnataka, India Mary Christeena Thomas Assistant professor ,Ece Vel Tech Rangarajan ,Dr.Sagunthala R&D Institute of Science and Technology Avadi, Chennai, Tamil Nadu Lovish Dhingra Centre for Research and Development, Chitkara University, Himachal Pradesh-174103 India Rishabh Bhardwaj Centre of Research Impact and Outcome, Chitkara University, Rajpura- 140417, Punjab, India Aws Zuhair Sameen College of Medical Techniques, Al-Farahidi University, Baghdad, Iraq Cardiovascular diseases (CVD) stand as the leading cause of global mortality, claiming millions of lives annually. An electrocardiogram (ECG) records the heart's electrical activity based on the Internet of Things (IoT), crucial in detecting cardiac arrhythmias (CA), characterized by irregular heart rates and rhythms. Signals from the MIT-BIH Arrhythmia Physio net database are analyzed. This chapter aims to propose a hybrid approach merging Genetic Algorithm-Support Vector Machine (GSVM) and Particle Swarm Optimization-Support Vector Machine (PSVM) for CA classification. The study introduces an algorithm for categorizing ECG beats into six groups using Independent Component Analysis (ICA)-derived features. Optimal SVM settings are determined using Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) on ICA features computed via non-parametric power spectral estimation. The research delves into the origins and methodologies of GA and PSO. Simulation results comparing GSVM and PSVM are presented, emphasizing PSVM's superior performance in accuracy, sensitivity, specificity, and positive predictivity. Detailed performance metrics, including Sensitivity, Specificity, Positive Predictivity, and Accuracy percentages, are scrutinized and compared against the top classifier. The findings endorse PSVM's superiority over GSVM, indicating enhanced performance across multiple evaluation criteria. 2024 71 82 2861 https://creativecommons.org/licenses/by/4.0/ 10.54216/JISIoT.130106 https://www.americaspg.com/journal/18/article/2861 https://www.americaspg.com/storage/41717402994.pdf