title: Wavelet transform analysis predicts outcome of DC cardioversion for atrial fibrillation patients.
abstract: The aim of this study was to examine whether wavelet transform analysis of the electrocardiogram (ECG) can improve the prediction of the maintenance of sinus rhythm in patients with atrial fibrillation (AF) after external DC cardioversion. We examined a variety of wavelet transform-based statistical markers as potential candidates for the prediction of patient status post-cardioversion. Considering a 'success' as a patient who remains in normal sinus rhythm for one month post cardioversion and 'failure' as a patient who does not, it was shown the proposed non-parametric classification system can achieve 89% specificity at 100% sensitivity using a non-parametric classification method.
authors.affiliation: CardioDigital Ltd., Elvingston Science Centre, Gladsmuir, East Lothian, EH33 1EH, Edinburgh, Scotland, UK.
authors.forename: J N
authors.initials: JN
authors.lastname: Watson
authors.forename: P S
authors.initials: PS
authors.lastname: Addison
authors.forename: N
authors.initials: N
authors.lastname: Uchaipichat
authors.forename: A S
authors.initials: AS
authors.lastname: Shah
authors.forename: N R
authors.initials: NR
authors.lastname: Grubb
mesh_terms: D000465:Algorithms; D001281:Atrial Fibrillation / Q000628:therapy*; D004554:Electric Countershock; D004562:Electrocardiography / Q000145:classification*; D006801:Humans; D011379:Prognosis; D012815:Signal Processing, Computer-Assisted; D018709:Statistics, Nonparametric; D016896:Treatment Outcome