Detection of abnormality in the electrocardiogram without prior knowledge by using the quantisation error of a self-organising map, tested on the European ischaemia database

ELMER ANDRÉS FERNÁNDEZ; PETER WILLSHAW; CARLOS ALBERTO PERAZZO

Medical & Biological Engineering & Computing

Springer

Año: 2001 vol. 39 p. 330 - 337

Most systems for the automatic detection of abnormalities in the ECG require prior knowledge of normal and abnormal ECG morphology from pre-existing databases. An automated system for abnormality detection has been developed based on learning normal ECG morphology directly from the patient. The quantisation error from a self-organizing map which “learns” the form of the patient’s ECG and detects any change in its morphology. The system does not require prior knowledge about normal and abnormal morphologies. It was tested on 76 records from the European Society of Cardiology database and detected 90.5% of those first abnormalities declared by the database to be ischaemic. The system also responded to abnormalities arising from ECG axis changes and slow baseline drifts and revealed that ischaemic episodes are often followed by long-term changes in ECG morphology.