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Authors: G. de Lannoy 1 ; M. Verleysen 1 and J. Delbeke 2

Affiliations: 1 Machine Learning Group, Université catholique de Louvain, Belgium ; 2 Departement of Physiology and Pharmacology, Université catholique de Louvain, Belgium

ISBN: 978-989-8111-65-4

Keyword(s): Heart beat classification, Time realignment, Dynamic time warping, Trace segmentation, Wavelet transform, Nearest neighbor classifier.

Related Ontology Subjects/Areas/Topics: Applications and Services ; Artificial Intelligence ; Biomedical Engineering ; Biomedical Signal Processing ; Computational Intelligence ; Computer Vision, Visualization and Computer Graphics ; Data Manipulation ; Health Engineering and Technology Applications ; Human-Computer Interaction ; Medical Image Detection, Acquisition, Analysis and Processing ; Methodologies and Methods ; Neurocomputing ; Neurotechnology, Electronics and Informatics ; Pattern Recognition ; Physiological Computing Systems ; Sensor Networks ; Soft Computing ; Wavelet Transform

Abstract: A reliable diagnosis of cardiac diseases can sometimes only be obtained by observing the heart of a patient for a long time period where every single heart beat is of importance. Computer-aided classification of heart beats is therefore of great help. The classification of the complete heart beat has many advantages compared to a classification of the QRS complex only or feature extraction methods. Nevertheless, the task is challenging because of the time-varying property of the heart beats. In this work, four time-alignment methods are evaluated and compared in the context of supervised heart beat classification. Among the four methods are three time series resampling methods by linear interpolation, cubic splines interpolation and trace segmentation. The fourth method is a realignment algorithm by dynamic time warping. The multiple sources of artifacts are filtered by discrete wavelet transform. As it only relies on a dissimilarity measure, the $k-$nearest neighbor classifier is a s uitable choice for supervised classification of time series like ECG signals in multiple classes. Two different experiments corresponding to inter-patient and intra-patient classification are conducted on representative dataset built from the standard public MIT-BIH arrhythmia database. (More)

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Paper citation in several formats:
Lannoy G.; Verleysen M.; Delbeke J. and (2009). ASSESSMENT AND COMPARISON OF TIME REALIGNMENT METHODS FOR SUPERVISED HEART BEAT CLASSIFICATION.In Proceedings of the International Conference on Bio-inspired Systems and Signal Processing - Volume 1: BIOSIGNALS, (BIOSTEC 2009) ISBN 978-989-8111-65-4, pages 239-244. DOI: 10.5220/0001434602390244

@conference{biosignals09,
author={G. de Lannoy and M. Verleysen and J. Delbeke},
title={ASSESSMENT AND COMPARISON OF TIME REALIGNMENT METHODS FOR SUPERVISED HEART BEAT CLASSIFICATION},
booktitle={Proceedings of the International Conference on Bio-inspired Systems and Signal Processing - Volume 1: BIOSIGNALS, (BIOSTEC 2009)},
year={2009},
pages={239-244},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0001434602390244},
isbn={978-989-8111-65-4},
}

TY - CONF

JO - Proceedings of the International Conference on Bio-inspired Systems and Signal Processing - Volume 1: BIOSIGNALS, (BIOSTEC 2009)
TI - ASSESSMENT AND COMPARISON OF TIME REALIGNMENT METHODS FOR SUPERVISED HEART BEAT CLASSIFICATION
SN - 978-989-8111-65-4
AU - Lannoy, G.
AU - Verleysen, M.
AU - Delbeke, J.
PY - 2009
SP - 239
EP - 244
DO - 10.5220/0001434602390244

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