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Unfortunately, there is a high risk of AF recurrence in persistent AF patients; hence clinically it is important to predict the DCE outcome in order to avoid the procedure\u2019s side effects. This study develops a feature extraction and classification framework to predict AF recurrence patients from the underlying structure of atrial activity (AA). A multiresolution signal decomposition technique, based on matching pursuit (MP), was used to project the AA over a dictionary of wavelets. Seven novel features were derived from the decompositions and were employed in a quadratic discrimination analysis classification to predict the success of post-DCE cardioversion in 40 patients with persistent AF. The proposed algorithm achieved 100% sensitivity and 95% specificity, indicating that the proposed computational approach captures detailed structural information about the underlying AA and could provide reliable information for effective management of AF.<\/jats:p>","DOI":"10.1155\/2015\/527815","type":"journal-article","created":{"date-parts":[[2015,5,18]],"date-time":"2015-05-18T21:45:20Z","timestamp":1431985520000},"page":"1-10","source":"Crossref","is-referenced-by-count":5,"title":["Developing a New Computer-Aided Clinical Decision Support System for Prediction of Successful Postcardioversion Patients with Persistent Atrial Fibrillation"],"prefix":"10.1155","volume":"2015","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6000-1919","authenticated-orcid":true,"given":"Mark","family":"Sterling","sequence":"first","affiliation":[{"name":"Biomedical Engineering Department, Rochester Institute of Technology, Rochester, NY 14623, 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