@inproceedings{d169dd70503f42458e65a11b18097e41,
title = "Hybrid feature vector creation for atrial fibrillation detection improvement",
abstract = "Two stages system consisting of feature extraction and selection part followed by neural classifier dedicated for atrial fibrillation (AF) detection, with preliminary ventricular activation cancellation is presented. According to proposed in this paper method the set of parameters obtained from time-frequency signal analysis mixed with features characterizing these signals in separately time and frequency domains was created. As a efficient tool for non-stationary signals analysis the discrete wavelet transform was used to obtain the T-F signal representation and then new parameters based on energy and entropy measure were computed. Features selected based on discrimination measure are the input to neural ECG classifier, where both supervised learnt multilayer perceptron and unsupervised Kohonen maps (SOMs) were tested on the set of 20 AF and 20 patients from control group divided into learning and verifying subsets.",
keywords = "Atrial fibrillation, Feature extraction, Neural networks, Wavelet transform",
author = "Kostka, \{Pawel Stanislaw\} and Ewaryst Tkacz",
note = "Publisher Copyright: {\textcopyright} International Federation for Medical and Biological Engineering 2007.; 10th World Congress on Medical Physics and Biomedical Engineering, WC 2006 ; Conference date: 27-08-2006 Through 01-09-2006",
year = "2007",
doi = "10.1007/978-3-540-36841-0\_245",
language = "English",
isbn = "9783540368397",
series = "IFMBE Proceedings",
publisher = "Springer Verlag",
number = "1",
pages = "1030--1033",
editor = "Kim, \{Sun I.\} and Suh, \{Tae Suk\}",
booktitle = "IFMBE Proceedings",
address = "Germany",
edition = "1",
}