RECOVERY OF BEAT-TO-BEAT VARIATIONS OF QRS

There is a growing interest in the analysis of beat-to-beat variations of the morphology (BBM) of cardiac waves in electrocardiograms (ECG). Such analyses are confronted with the low BBM-to-noise ratio. An ECG clustering technique is introduced that brings the benefits of signal averaging to BBM analysis and recovers the beat-to-beat pattern of BBM . ECG clustering aligns waves and sorts them into clusters. The precis ion of the alignment was enhanced by sub-sample alignment. Kohonen's s elf-organising neural networks identified the clusters of the cardiac waves during training. The subsequent clustering of a wave results in a label for the closest cluster, a distance to the cluster and optimal alignment. Furthermore, ECG clustering avoids base-line variations an d amplitude modulation sufficiently to be applied to the QRS wave in t he raw EGG. The technique is demonstrated on 14 subjects with coronary heart disease and no myocardial infarction, myocardial infarction, or inducible ventricular tachycardia. ECG clustering is a general-purpos e technique for beat-to-beat analysis, where the variations are cyclic as in the sinus rhythm. Results show that beat-to-beat variations in the QRS morphology are in general cyclic, with a main period of about four cardiac cycles. All calculations were performed with the Cardio s oftware.