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.
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