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A hybrid optimization algorithm is used for feature selection and proposed to optimize the parameters of the existing Support Vector Machine (SVM) classifier. Proposed hybrid optimization algorithm was developed using Particle Swarm Optimization (PSO) and Migration Modified Biogeography Based Optimization (MMBBO) algorithm. Algorithm provides an improved solution to the optimizing the parameters of ECG signals. Results are evaluated by implementing in MATLAB software and the performance is justified with comparative analysis. The proposed framework enhances the process of automatic prediction of various arrhythmias or rhythm abnormalities which performs in gaining better accuracy. For data sets, the average classification accuracy of this method is 97.89%. This result is an improvement of 4\u20135% over the comparison of other methods.<\/jats:p>","DOI":"10.3233\/jifs-212373","type":"journal-article","created":{"date-parts":[[2022,5,3]],"date-time":"2022-05-03T11:28:31Z","timestamp":1651577311000},"page":"627-642","source":"Crossref","is-referenced-by-count":4,"title":["Automatic ECG analysis system with hybrid optimization algorithm based feature selection and classifier"],"prefix":"10.1177","volume":"43","author":[{"given":"Manikandan","family":"Kaliappan","sequence":"first","affiliation":[{"name":"Department of Bio Medical Engineering, Sona College of Technology, Salem, Tamilnadu, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sumithra","family":"Manimegalai Govindan","sequence":"additional","affiliation":[{"name":"Department of ECE, Dr. N.G.P Institute of Technology, Coimbatore, Tamilnadu, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mohana Sundaram","family":"Kuppusamy","sequence":"additional","affiliation":[{"name":"Department of EEE, KPR Institute of Engineering and Technology, Coimbatore, Tamilnadu, India"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","reference":[{"issue":"1","key":"10.3233\/JIFS-212373_ref1","doi-asserted-by":"publisher","first-page":"13","DOI":"10.1109\/JBHI.2020.2982935","article-title":"An Adaptive Kalman Filter Bank for ECG Denoising","volume":"25","author":"Hesar","year":"2021","journal-title":"IEEE Journal of Biomedical and Health Informatics"},{"key":"10.3233\/JIFS-212373_ref2","doi-asserted-by":"publisher","first-page":"1198","DOI":"10.1049\/el.2016.1171","article-title":"Efficient algorithm for classification of electrocardiogram beats based on artificial bee colony-based least-squares support vector machines classifier","volume":"52.14","author":"Jain","year":"2016","journal-title":"Electronics Letters"},{"key":"10.3233\/JIFS-212373_ref3","doi-asserted-by":"crossref","first-page":"596","DOI":"10.1016\/j.asoc.2018.04.005","article-title":"A unique feature extraction using MRDWT for automatic classification of abnormal heartbeat from ECG big data with Multilayered Probabilistic Neural Network classifier","volume":"72","author":"Rai","year":"2018","journal-title":"Applied Soft Computing"},{"key":"10.3233\/JIFS-212373_ref4","doi-asserted-by":"publisher","first-page":"1034","DOI":"10.1109\/TBME.2013.2292930","article-title":"Atrial electrical activity detection using linear combination of 12-lead ECG signals","volume":"61.4","author":"Or","year":"2013","journal-title":"IEEE Transactions on Biomedical Engineering"},{"key":"10.3233\/JIFS-212373_ref5","doi-asserted-by":"crossref","unstructured":"Sadaghiyanfam Safa and Kuntalp Mehmet , Comparing the Performances of PCA and LDA Transformations on PAF Patient Detection. 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