Feature selection of signal-averaged electrocardiograms by orthogonal least squares method

Michal Raczyk; Stanislaw Jankowski; Ewa Piatkowska-Janko

doi:10.1117/12.817954

6 November 2008 Feature selection of signal-averaged electrocardiograms by orthogonal least squares method

Michal Raczyk, Stanislaw Jankowski, Ewa Piatkowska-Janko

Proceedings Volume 7124, Photonics Applications in Astronomy, Communications, Industry, and High-Energy Physics Experiments 2008; 71240P (2008) https://doi.org/10.1117/12.817954
Event: Photonics Applications in Astronomy, Communications, Industry, and High Energy Physics Experiments 2008, 2008, Wilga, Poland

Abstract

A crucial problem in machine learning is finding the representative set of data for building a model for both classification and approximation task. In this paper we present the orthogonal least squares method for feature selection. The presented method was used for finding the most important features for selecting patients with sustained ventricular tachycardia after myocardial infarction (SVT+). We show that with the reduced set of descriptors used in the classification process we obtain the results that are better than those obtained with the full set.

Citation Download Citation

Michal Raczyk, Stanislaw Jankowski, and Ewa Piatkowska-Janko "Feature selection of signal-averaged electrocardiograms by orthogonal least squares method", Proc. SPIE 7124, Photonics Applications in Astronomy, Communications, Industry, and High-Energy Physics Experiments 2008, 71240P (6 November 2008); https://doi.org/10.1117/12.817954

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