A Knowledge-based Technique for Automated Detection of Ischaemic Episodes in Long Duration Electrocardiograms

Overview

Journal Med Biol Eng Comput

Publisher Springer

Specialties Biomedical Engineering
Medical Informatics

Date 2001 Feb 24

PMID 11214261

Citations 6

Authors

C Papaloukas

D I Fotiadis

A P Liavas

A Likas

L K Michalis

Affiliations

Soon will be listed here.

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).

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