Abstract
Arrhythmias consist on electrical alterations in the heart beat control. They can be identified by means of surface ECG leads. The main goal of this work is to provide a signal classification based on ECG signal waveform in the time-frequency domain especially targeted to Ventricular Fibrillation detection. The use of a classifier based on a Boltzmann network is proposed. However, a previous signal preprocessing is also required so that the Boltzmann network is fed with the appropriate data. In this case, an R-wave detector is used; after that, the Pseudo Wigner-Ville time-frequency distribution is obtained. This distribution is used to train and test the network, which handles it as an image and thus, provides a classification. Results show the ability of the network to provide a similar or higher classification ratio compared to other algorithms.
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© 2015 Springer International Publishing Switzerland
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Mjahad, A., Rosado-Muńoz, A., Guerrero-Martínez, J., Bataller-Mompeán, M., Francés-Víllora, J.V. (2015). ECG Analysis for Ventricular Fibrillation Detection Using a Boltzmann Network. In: Braidot, A., Hadad, A. (eds) VI Latin American Congress on Biomedical Engineering CLAIB 2014, Paraná, Argentina 29, 30 & 31 October 2014. IFMBE Proceedings, vol 49. Springer, Cham. https://doi.org/10.1007/978-3-319-13117-7_136
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DOI: https://doi.org/10.1007/978-3-319-13117-7_136
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-13116-0
Online ISBN: 978-3-319-13117-7
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