title: A knowledge-based technique for automated detection of ischaemic episodes in long duration electrocardiograms.
abstract: A novel method for the detection of ischaemic episodes in long duration ECGs is proposed. It includes noise handling, feature extraction, rule-based beat classification, sliding window classification and ischaemic episode identification, all integrated in a four-stage procedure. It can be executed in real time and is able to provide explanations for the diagnostic decisions obtained. The method was tested on the ESC ST-T database and high scores were obtained for both sensitivity and positive predictive accuracy (93.8% and 78.5% respectively using aggregate gross statistics, and 90.7% and 80.7% using aggregate average statistics).
authors.affiliation: Department of Medical Physics, Medical School, University of Ioannina, Greece.
authors.forename: C
authors.initials: C
authors.lastname: Papaloukas
authors.forename: D I
authors.initials: DI
authors.lastname: Fotiadis
authors.forename: A P
authors.initials: AP
authors.lastname: Liavas
authors.forename: A
authors.initials: A
authors.lastname: Likas
authors.forename: L K
authors.initials: LK
authors.lastname: Michalis
mesh_terms: D004562:Electrocardiography; D006801:Humans; D017202:Myocardial Ischemia / Q000175:diagnosis*; D012815:Signal Processing, Computer-Assisted