A knowledge-based technique for automated detection of ischaemic episodes in long duration electrocardiograms.
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).
Papaloukas
C
C
Department of Medical Physics, Medical School, University of Ioannina, Greece.
Fotiadis
D I
DI
Liavas
A P
AP
Likas
A
A
Michalis
L K
LK
D004562:Electrocardiography; D006801:Humans; D017202:Myocardial Ischemia / Q000175:diagnosis*; D012815:Signal Processing, Computer-Assisted