IOP PUBLISHING PHYSIOLOGICAL MEASUREMENT Physiol. Meas. 28 (2007) 259–276 doi:10.1088/0967-3334/28/3/003 Real-time detection of pathological cardiac events in the electrocardiogram Ivo Iliev 1 , Vessela Krasteva 2 and Serafim Tabakov 1 1 Department of Electronics, Technical University of Sofia, 8 Kl. Ohridski str, 1000, Sofia, Bulgaria 2 Centre of Biomedical Engineering ‘Prof. Ivan Daskalov’, Bulgarian Academy of Science, Acad. G. Bonchev str., Bl.105, 1113, Sofia, Bulgaria E-mail: vessika@clbme.bas.bg Received 23 November 2006, accepted for publication 12 January 2007 Published 9 February 2007 Online at stacks.iop.org/PM/28/259 Abstract The development of accurate and fast methods for real-time electrocardiogram (ECG) analysis is mandatory in handheld fully automated monitoring devices for high-risk cardiac patients. The present work describes a simple software method for fast detection of pathological cardiac events. It implements real- time procedures for QRS detection, interbeat RR-intervals analysis, QRS waveform evaluation and a decision-tree beat classifier. Two QRS descriptors are defined to assess (i) the RR interval deviation from the mean RR interval and (ii) the QRS waveform deviation from the QRS pattern of the sustained rhythm. The calculation of the second parameter requires a specific technique, in order to satisfy the demand for straight signal processing with minimum iterations and small memory size. This technique includes fast and resource efficient estimation of a histogram matrix, which accumulates dynamically the amplitude-temporal distribution of the successive QRS pattern waveforms. The pilot version of the method is developed in Matlab and it is tested with internationally recognized ECG databases. The assessment of the online single lead QRS detector showed sensitivity and positive predictivity of above 99%. The classification rules for detection of pathological ventricular beats were defined empirically by statistical analysis. The attained specificity and sensitivity are about 99.5% and 95.7% for all databases and about 99.81% and 98.87% for the noise free dataset. The method is applicable in low computational cost systems for long-term ECG monitoring, such as intelligent holters, automatic event/alarm recorders or personal devices with intermittent wireless data transfer to a central terminal. Keywords: electrocardiography, monitoring of high-risk cardiac patients, real-time ECG analysis 0967-3334/07/030259+18$30.00 © 2007 IOP Publishing Ltd Printed in the UK 259