Ecg Arrhythmia Signals Classification Using Particle Swarm Optimization-Support Vector Machines Optimized With Independent Component Analysis

Abstract

Cardiac Arrhythmia is one of the serious disorders which are most commonly found among humans larger in number. This study is based on proposing a novel approach for heart (Cardiac) arrhythmia disease classification. Many Machine learning algorithms are implemented for the cardiac arrhythmia classification from which the ECG signal are extracted from MIT-BIH Database. The main objective of this study is to do the classification of ECG signals to the normal and abnormal (Ventricular Tachycardia) category using PSO-SVM optimized with Independent Component Analysis using Genetic Algorithm. The extraction of ECG signal is done with twenty four features consisting of Normal and Abnormal clinical clusters. ECG Signals under these categories are extracted from MIT-BIH Arrhythmia database which is read in terms of P,Q,R,S and T voltage-time parametric signal. Genetic Algorithm and Particle Swarm Optimization together used to enhance the performance of the Support Vector Machine (SVM) classifier. Initially the SVM Classifier is designed and it is optimized by searching for the best parametric value where the discriminate function is tuned to extract the features under the best subsets and as a result the fitness functions which are classified are identified with better optimization. Additionally the PSO-SVM Classifier is allowed to undergo the adaptive mechanism wherein which the optimization factor is allowed to restrict the boundaries of classification of ECG arrhythmia with maximum accuracy by the implementation of Independent Component Analysis Optimization using Genetic Algorithm. The results are experimentally demonstrated with the comparison of PCA, ICA, PSO-SVM with ICA and G-ICA. Sensitivity, Specificity, False Positive Rate, True Positive Rate and Accuracy are the experimental parameters used for the performance metrics comparison to classify for normal and diabetic clinical condition. The parameters yield better results for PSO-SVM-ICA and G-ICA with respect to the above mentioned metrics. The Classification Accuracy is attained with 96% with best optimization strategies by using these hybrid classifiers.

Cardiovascular Diseases Identification Using Wavelet Optimization and Modify Cuckoo Search Algorithm

“…In this study, we are using the cutting-edge SVM approach as an approach to classification. This approach has been especially useful in many fields of application including ECG signals (Ramkumar et al, 2021;Khandoker et al, 2008;Bazi and Melgani, 2007;Melgani and Bazi, 2008). The generally acknowledged dominance over conventional classifiers brings the emphasis on the SVM classifier.…”

“…We will show this resource briefly below. The reader is directed to Ramkumar et al (2021) for further information. Let us first consider a binary classification problem supervised for simplicity.…”

“…This method also corresponded to labeling known and missing data. The validation was efficient enough as it labeled the training data and also helped in separating the invalidated samples (Ramkumar et al, 2021). The change of classifier namely the Multilayer Perceptron (MP) along with GA was more efficient in detecting cardiac arrhythmias.…”

Optimal AdaBoost kernel support vector machine for monitoring arrhythmia patients utilizing Internet of Things‐cloud environment

“…The results has proven that the accuracy rate and F‐measure value was higher than other techniques. The particle swarm optimization‐based support vector machine with independent component analysis (PSO‐SVM‐ICA) method was presented by Ramkumar et al 19 to classify the ECG arrhythmic signals into normal and abnormal classes. The dataset was acquired from the MIT‐BIH database and was analyzed in terms of accuracy, sensitivity, specificity, true positive rate, and false‐positive rate.…”

An Optimization Algorithm for Computer-Aided Diagnosis of Breast Cancer Based on Support Vector Machine

“…The choice of penalty parameter c and g in SVM kernel function is directly related to the effectiveness and accuracy of SVM algorithm in solving dichotomy. According to previous research methods, there are mainly 5 optimization methods for the above two important parameters, namely, empirical selection method, grid selection method, genetic optimization algorithm, particle swarm optimization algorithm, and ant colony optimization algorithm so on ( Ali and Abdullah, 2020 ; Kouziokas, 2020 ; Li X. et al, 2020 ; Arya Azar et al, 2021 ; Ramkumar et al, 2021 ). Although these optimization algorithms have been applied to some extent and achieved some effects, they all have problems of different degrees.…”