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European Heart Journal 1995 16(9):1244-1254;
Copyright © 1995 by the European Society of Cardiology.
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© 1995 The European Society of Cardiology

Reproducibility of the signal-averaged electrocardiogram using individual lead analysis

J. AGANAUSKIENE*, L. SÖRNMO{dagger},{ddagger}, R. ATARIUS{dagger} and C. BLOMSTRÖM-LUNDQVIST§

*Department of Cardology, Lund University Lund
{dagger}Department of Signal Processing, Lund University Lund
{ddagger}Department of Clinical Physiology, Lund University Lund
§Department of Cardiology, Uppsala University Uppsala, Sweden

Received 30 November 1994; .

Correspondence. Dr Leif Sömmo, Dept of Clinical Physiology. Lund University. S-221 85 Lund. Sweden

Abstract

The aim of the study was to evaluate the immediate reproducibility of time domain parameters in the signal averaged electrocardiogram using a new method for endpoint determination in individual Frank leads. The method is based on a statistical model of the electrocardiogram (ECG) in which maximum likelihood (ML) estimation is employed. The reproducibility of the ML method was compared to that of conventional time domain analysis using the vector magnitude (VM) of Frank leads. Fifty-nine patients were included in the study and two consecutive ECGs were recorded for signal averaging. The results showed that the mean of the absolute difference of the filtered QRS duration (QRSD) between two consecutive recordings was significantly lower for the ML method than the conventional method when employing 60 Hz highpass filtering (2.1 ± 2.2 ms vs 5.9 ± 10.2 ms, P < 0.05). Moreover, the ML method resulted in a significantly longer QRSD compared to the VM-based method (P < 0.05). The terminal amplitude of the QRS complex (RMS40) showed a greater variability than the QRSD for both methods, although the ML method was associated with a higher reproducibility than the VM method for the 60 Hz filter. These findings may contribute to a better identification of patients at high risk of ventricular arrhythmias. A reduction in the number of measurement errors has important implications when QRS changes are analysed over time.

Key Words: Reproducibility • late potentials • ECG • algorithm


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