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Authors: Soodeh Nikan ; Femida Gwadry-Sridhar and Michael Bauer

Affiliation: University of Western Ontario, Canada

Keyword(s): Arrhythmia Classification, Pattern Recognition, Beat Segmentation, 1-D LBP, ELM Classification.

Related Ontology Subjects/Areas/Topics: Biomedical Engineering ; Health Information Systems ; Pattern Recognition and Machine Learning

Abstract: In this paper, we propose a pattern recognition algorithm for arrhythmia recognition. Irregularity in the electrical activity of the heart (arrhythmia) is one of the leading reasons for sudden cardiac death in the world. Developing automatic computer aided techniques to diagnose this condition with high accuracy can play an important role in aiding cardiologists with decisions. In this work, we apply an adaptive segmentation approach, based on the median value of R-R intervals, on the de-noised ECG signals from the publically available MIT-BIH arrhythmia database and split signal into beat segments. The combination of wavelet transform and uniform one dimensional local binary pattern (1-D LBP) is applied to extract sudden variances and distinctive hidden patterns from ECG beats. Uniform 1-D LBP is not sensitive to noise and is computationally effective. ELM classification is adopted to classify beat segments into five types, based on the ANSI/AAMI EC57:1998 standard recommendation. O ur preliminary experimental results show the effectiveness of the proposed algorithm in beat classification with 98.99% accuracy compared to the state of the art approaches. (More)

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Paper citation in several formats:
Nikan, S.; Gwadry-Sridhar, F. and Bauer, M. (2017). Pattern Recognition Application in ECG Arrhythmia Classification. In Proceedings of the 10th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2017) - HEALTHINF; ISBN 978-989-758-213-4; ISSN 2184-4305, SciTePress, pages 48-56. DOI: 10.5220/0006116300480056

@conference{healthinf17,
author={Soodeh Nikan. and Femida Gwadry{-}Sridhar. and Michael Bauer.},
title={Pattern Recognition Application in ECG Arrhythmia Classification},
booktitle={Proceedings of the 10th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2017) - HEALTHINF},
year={2017},
pages={48-56},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006116300480056},
isbn={978-989-758-213-4},
issn={2184-4305},
}

TY - CONF

JO - Proceedings of the 10th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2017) - HEALTHINF
TI - Pattern Recognition Application in ECG Arrhythmia Classification
SN - 978-989-758-213-4
IS - 2184-4305
AU - Nikan, S.
AU - Gwadry-Sridhar, F.
AU - Bauer, M.
PY - 2017
SP - 48
EP - 56
DO - 10.5220/0006116300480056
PB - SciTePress