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

Journal of Intelligent Systems and Internet of Things JISIoT 2690-6791 2769-786X 10.54216/JISIoT https://www.americaspg.com/journals/show/2861 2019 2019 An Improved Internet of Thing-based Optimized SVM Approach for ECG-founded Cardiac Arrhythmia Classification Assistant Professor CSE, Ajay Kumar Garg Engineering College, Ghaziabad, U.P. India Yogendra Yogendra Professor, Department of Mechanical Engineering, Faculty of Engineering and Technology, JAIN (Deemed-to-be University), Bangalore, Karnataka, India Beemkumar. N. Assistant professor ,Ece Vel Tech Rangarajan ,Dr.Sagunthala R&D Institute of Science and Technology Avadi, Chennai, Tamil Nadu Mary Christeena Thomas Centre for Research and Development, Chitkara University, Himachal Pradesh-174103 India Lovish Dhingra Centre of Research Impact and Outcome, Chitkara University, Rajpura- 140417, Punjab, India Rishabh Bhardwaj College of Medical Techniques, Al-Farahidi University, Baghdad, Iraq Aws Zuhair Sameen 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 2024 71 82 10.54216/JISIoT.130106 https://www.americaspg.com/articleinfo/18/show/2861