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European Heart Journal 1998 19(3):435-446; doi:10.1053/euhj.1997.0768
Copyright © 1998 by the European Society of Cardiology.
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Single-beat analysis of ventricular late potentials in the surface electrocardiogram using the spectrotemporal pattern recognition algorithm in patients with coronary artery disease

P. Steinbiglerf2, R. Haberl, G. Jilge and G. Steinbeck

Medical Hospital I, University of Munich, Munich, Germany

accepted August 14, 1997

Aims

Post-infarction risk stratification can be ascertained from beat-to-beat variations in ventricular late potentials. However, gaining such information by conventional late potential analysis using signal averaging is still not possible.

Methods

We therefore developed the spectrotemporal pattern recognition algorithm in order to detect beat-to-beat variations in late potentials. Based on the spectrotemporal pattern recognition algorithm two-dimensional correlation function, the typical spectral pattern of late potentials can be identified in spectrotemporal maps of single beats, even in the presence of noise.

Results

Surface electrocardiograms of 385 patients after myocardial infarction (85 with documented sustained ventricular tachycardia (group 1), 100 with fast, polymorphic ventricular tachycardia (>270 cycles.min–1) or primary ventricular fibrillation (group 2), 200 without ventricular arrhythmias (group 3) and 45 healthy volunteers (group 4), were analysed.The spectrotemporal pattern recognition algorithm detected late potentials in single beats in 89% of group 1 patients, in 79% of group 2, in 22% of group 3 and in 4% of normals. The spectrotemporal pattern recognition algorithm measured late potential frequency and extension of late potentials into the ST segment, which was significantly different between groups 1 and 2.Beat-to-beat variations in late potentials, with respect to frequency and extension into the ST segment, were markedly higher in patients with a history of primary ventricular fibrillation.

Conclusion

Single-beat analysis using the spectrotemporal pattern recognition algorithm may improve risk stratification of patients after myocardial infarction, and provides information on patients prone to ventricular fibrillation.

Key Words: Late potentials • single beat analysis • signal averaging • spectral analysis • ventricular tachycardia

Supported by Deutsche Forschungsgemeinschaft Ste 257/3-5.

f2 Correspondence: Peter Steinbigler, MD, Medical Hospital I, University of Munich, Marchioninistr. 15, D-81366 München, Germany.


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