loading
Papers Papers/2022 Papers Papers/2022

Research.Publish.Connect.

Paper

Paper Unlock

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

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 aver age positive predictivity rate of 99.7%. (More)

CC BY-NC-ND 4.0

Sign In Guest: Register as new SciTePress user now for free.

Sign In SciTePress user: please login.

PDF ImageMy Papers

You are not signed in, therefore limits apply to your IP address 3.85.211.2

In the current month:
Recent papers: 100 available of 100 total
2+ years older papers: 200 available of 200 total

Paper citation in several formats:
de Lannoy, G.; de Decker, A. and Verleysen, M. (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 (BIOSTEC 2008) - Volume 1: BIOSIGNALS; ISBN 978-989-8111-18-0; ISSN 2184-4305, SciTePress, 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 (BIOSTEC 2008) - Volume 1: BIOSIGNALS},
year={2008},
pages={140-145},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0001062501400145},
isbn={978-989-8111-18-0},
issn={2184-4305},
}

TY - CONF

JO - Proceedings of the First International Conference on Bio-inspired Systems and Signal Processing (BIOSTEC 2008) - Volume 1: BIOSIGNALS
TI - A SUPERVISED LEARNING APPROACH BASED ON THE CONTINUOUS WAVELET TRANSFORM FOR R SPIKE DETECTION IN ECG
SN - 978-989-8111-18-0
IS - 2184-4305
AU - de Lannoy, G.
AU - de Decker, A.
AU - Verleysen, M.
PY - 2008
SP - 140
EP - 145
DO - 10.5220/0001062501400145
PB - SciTePress