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Improved recognition of sustained ventricular tachycardia from SAECG by support vector machine [Anatol J Cardiol]
Anatol J Cardiol. 2007; 7(Suppl 1): 112-115

Improved recognition of sustained ventricular tachycardia from SAECG by support vector machine

Stanislaw Jankowski1, Zbigniew Szymanski2, Ewa Piatkowska- Janko3, Artur Oreziak4
1From Institute of Electronic Systems,Warsaw University of Technology, Warsaw
2From Institute of Computer Science,Warsaw University of Technology, Warsaw
3From Institute of Radioelectronics Warsaw University of Technology, Warsaw
4Ist Department of Cardiology, Medical University of Warsaw, Warsaw, Poland

Objective: We present the improved method of recognition of sustained ventricular tachycardia (SVT) based on new filtering technique (FIR), extended signal-averaged electrocardiography (SAECG) description by 9 parameters and the application of support vector machine (SVM) classifier. Methods: The dataset consisted of 376 patients (100 patients with sustained ventricular tachycardia after myocardial infarction (MI) labelled as class SVT+, 176 patients without sustained ventricular tachycardia after MI and 77 healthy persons, 50% of data were left for validation. The analysis of SAECG was performed by 2 types of filtration: low pass four-pole IIR Butterworth filter and FIR filter with Kaiser window. We calculated 3 commonly used SAECG parameters: hfQRS (ms), RMS40 (V), LAS<40 V(ms) and 6 new parameters: LAS<25 V(ms) - duration of the low amplitude <25V signals at the end of QRS complex; RMS QRS(V) root mean square voltage of the filtered QRS complex; pRMS(V) - root mean square voltage of the first 40ms of filtered QRS complex; pLAS(ms) - duration of the low amplitude <40V signals in front of QRS complex; RMS t1(V) - root mean square voltage of the last 10ms the filtered QRS complex; RMS t2(V) - root mean square voltage of the last 20ms the filtered QRS complex. For the recognition of SVT+ class patients we used the SVM with the Gaussian kernel. Results: The results confirmed good generalization of obtained models. The recognition score (calculated as correct classification/total number of patients) of SVT+patients on data set containing 3 standard parameters (Butterworth filter) is 92.55%. The same score was obtained for data set containing 9 parameters (Butterworth filter). The best score (95.21%) was obtained for data set based on 9 parameters and FIR filter. Conclusion: Our approach improved risk stratification up to 95% based on SAECG due to the application of FIR filter, 6 new parameters and efficient statistical classifier, the support vector machine.

Keywords: Ventricular tachycardia, signal-averaged electrocardiography


Stanislaw Jankowski, Zbigniew Szymanski, Ewa Piatkowska- Janko, Artur Oreziak. Improved recognition of sustained ventricular tachycardia from SAECG by support vector machine. Anatol J Cardiol. 2007; 7(Suppl 1): 112-115


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