title: Developing a New Computer-Aided Clinical Decision Support System for Prediction of Successful Postcardioversion Patients with Persistent Atrial Fibrillation.
abstract: We propose a new algorithm to predict the outcome of direct-current electric (DCE) cardioversion for atrial fibrillation (AF) patients. AF is the most common cardiac arrhythmia and DCE cardioversion is a noninvasive treatment to end AF and return the patient to sinus rhythm (SR). 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's 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.
authors.affiliation: Biomedical Engineering Department, Rochester Institute of Technology, Rochester, NY 14623, USA.
authors.forename: Mark
authors.initials: M
authors.lastname: Sterling
authors.affiliation: University of Rochester Medical Center, School of Medicine and Dentistry, Rochester, NY 14642, USA.
authors.forename: David T
authors.initials: DT
authors.lastname: Huang
authors.affiliation: Biomedical Engineering Department, Rochester Institute of Technology, Rochester, NY 14623, USA.
authors.forename: Behnaz
authors.initials: B
authors.lastname: Ghoraani
mesh_terms: D000368:Aged; D000465:Algorithms; D001281:Atrial Fibrillation / Q000175:diagnosis* / Q000473:pathology; D016208:Databases, Factual; D020000:Decision Support Systems, Clinical; D004554:Electric Countershock; D004562:Electrocardiography; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D015233:Models, Statistical; D012008:Recurrence; D015203:Reproducibility of Results; D012306:Risk; D012680:Sensitivity and Specificity; D012815:Signal Processing, Computer-Assisted; D012984:Software