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Authors: G. de Lannoy 1 ; A. de Decker 2 and M. Verleysen 2

Affiliations: 1 Machine Learning Group, Université catholique de Louvain; Université catholique de Louvain, Belgium ; 2 Machine Learning Group, Université catholique de Louvain, Belgium

ISBN: 978-989-8111-18-0

Keyword(s): Continuous wavelet transform, automatic ECG annotation, R spike detection, supervised learning.

Related Ontology Subjects/Areas/Topics: Applications and Services ; Artificial Intelligence ; Biomedical Engineering ; Biomedical Signal Processing ; Computational Intelligence ; Computer Vision, Visualization and Computer Graphics ; Medical Image Detection, Acquisition, Analysis and Processing ; Soft Computing ; Wavelet Transform

Abstract: One of the most important tasks in automatic annotation of the ECG is the detection of the R spike. The wavelet transform is a widely used tool for R spike detection. The time-frequency decomposition is indeed a powerful tool to analyze non-stationary signals. Still, current methods use consecutive wavelet scales in an a priori restricted range and may therefore lack adaptivity. This paper introduces a supervised learning algorithm which learns the optimal scales for each dataset using the annotations provided by physicians on a small training set. For each record, this method allows a specific set of non consecutive scales to be selected, based on the record characteristics. The selected scales are then used on the original long-term ECG signal recording and a hard thresholding rule is applied on the derivative of the wavelet coefficients to label the R spikes. This algorithm has been tested on the MIT-BIH arrhythmia database and obtains an average sensitivity rate of 99.7% and avera ge positive predictivity rate of 99.7%. (More)

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Paper citation in several formats:
de Lannoy G.; de Decker A.; Verleysen M. and (2008). A SUPERVISED LEARNING APPROACH BASED ON THE CONTINUOUS WAVELET TRANSFORM FOR R SPIKE DETECTION IN ECG.In Proceedings of the First International Conference on Bio-inspired Systems and Signal Processing - Volume 1: BIOSIGNALS, (BIOSTEC 2008) ISBN 978-989-8111-18-0, pages 140-145. DOI: 10.5220/0001062501400145

@conference{biosignals08,
author={G. {de Lannoy} and A. {de Decker} and M. Verleysen},
title={A SUPERVISED LEARNING APPROACH BASED ON THE CONTINUOUS WAVELET TRANSFORM FOR R SPIKE DETECTION IN ECG},
booktitle={Proceedings of the First International Conference on Bio-inspired Systems and Signal Processing - Volume 1: BIOSIGNALS, (BIOSTEC 2008)},
year={2008},
pages={140-145},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0001062501400145},
isbn={978-989-8111-18-0},
}

TY - CONF

JO - Proceedings of the First International Conference on Bio-inspired Systems and Signal Processing - Volume 1: BIOSIGNALS, (BIOSTEC 2008)
TI - A SUPERVISED LEARNING APPROACH BASED ON THE CONTINUOUS WAVELET TRANSFORM FOR R SPIKE DETECTION IN ECG
SN - 978-989-8111-18-0
AU - de Lannoy, G.
AU - de Decker, A.
AU - Verleysen, M.
PY - 2008
SP - 140
EP - 145
DO - 10.5220/0001062501400145

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