IMPROVING AN AUTOMATIC ARRHYTHMIAS RECOGNISER BASED IN ECG SIGNALS

Jorge Corsino, Carlos M. Travieso, Jesús B. Alonso, Miguel A. Ferrer

Abstract

In the present work, we have developed and improved a tool for the automatic arrhythmias detection, based on neural network with the “more-voted” algorithm. Arrhythmia Database MIT has been used in the work in order to detect eight different states, seven are pathologies and one is normal. The unions of different blocks and its optimization have found an improvement of success rates. In particular, we have used wavelet transform in order to characterize the patron wave of electrocardiogram (ECG), and principal components analysis in order to improve the discrimination of the coefficients. Finally, a neural network with more-voted method has been applied.

References

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Paper Citation


in Harvard Style

Corsino J., M. Travieso C., B. Alonso J. and A. Ferrer M. (2008). IMPROVING AN AUTOMATIC ARRHYTHMIAS RECOGNISER BASED IN ECG SIGNALS . In Proceedings of the First International Conference on Bio-inspired Systems and Signal Processing - Volume 2: BIOSIGNALS, (BIOSTEC 2008) ISBN 978-989-8111-18-0, pages 453-457. DOI: 10.5220/0001066604530457


in Bibtex Style

@conference{biosignals08,
author={Jorge Corsino and Carlos M. Travieso and Jesús B. Alonso and Miguel A. Ferrer},
title={IMPROVING AN AUTOMATIC ARRHYTHMIAS RECOGNISER BASED IN ECG SIGNALS},
booktitle={Proceedings of the First International Conference on Bio-inspired Systems and Signal Processing - Volume 2: BIOSIGNALS, (BIOSTEC 2008)},
year={2008},
pages={453-457},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0001066604530457},
isbn={978-989-8111-18-0},
}


in EndNote Style

TY - CONF
JO - Proceedings of the First International Conference on Bio-inspired Systems and Signal Processing - Volume 2: BIOSIGNALS, (BIOSTEC 2008)
TI - IMPROVING AN AUTOMATIC ARRHYTHMIAS RECOGNISER BASED IN ECG SIGNALS
SN - 978-989-8111-18-0
AU - Corsino J.
AU - M. Travieso C.
AU - B. Alonso J.
AU - A. Ferrer M.
PY - 2008
SP - 453
EP - 457
DO - 10.5220/0001066604530457