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Electrocardiogram ST-Segment Morphology Delineation Method Using Orthogonal Transformations

Differentiation between ischaemic and non-ischaemic transient ST segment events of long term ambulatory electrocardiograms is a persisting weakness in present ischaemia detection systems. Traditional ST segment level measuring is not a sufficiently precise technique due to the single point of measur...

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Autores principales: Amon, Miha, Jager, Franc
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4749300/
https://www.ncbi.nlm.nih.gov/pubmed/26863140
http://dx.doi.org/10.1371/journal.pone.0148814
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author Amon, Miha
Jager, Franc
author_facet Amon, Miha
Jager, Franc
author_sort Amon, Miha
collection PubMed
description Differentiation between ischaemic and non-ischaemic transient ST segment events of long term ambulatory electrocardiograms is a persisting weakness in present ischaemia detection systems. Traditional ST segment level measuring is not a sufficiently precise technique due to the single point of measurement and severe noise which is often present. We developed a robust noise resistant orthogonal-transformation based delineation method, which allows tracing the shape of transient ST segment morphology changes from the entire ST segment in terms of diagnostic and morphologic feature-vector time series, and also allows further analysis. For these purposes, we developed a new Legendre Polynomials based Transformation (LPT) of ST segment. Its basis functions have similar shapes to typical transient changes of ST segment morphology categories during myocardial ischaemia (level, slope and scooping), thus providing direct insight into the types of time domain morphology changes through the LPT feature-vector space. We also generated new Karhunen and Lo ève Transformation (KLT) ST segment basis functions using a robust covariance matrix constructed from the ST segment pattern vectors derived from the Long Term ST Database (LTST DB). As for the delineation of significant transient ischaemic and non-ischaemic ST segment episodes, we present a study on the representation of transient ST segment morphology categories, and an evaluation study on the classification power of the KLT- and LPT-based feature vectors to classify between ischaemic and non-ischaemic ST segment episodes of the LTST DB. Classification accuracy using the KLT and LPT feature vectors was 90% and 82%, respectively, when using the k-Nearest Neighbors (k = 3) classifier and 10-fold cross-validation. New sets of feature-vector time series for both transformations were derived for the records of the LTST DB which is freely available on the PhysioNet website and were contributed to the LTST DB. The KLT and LPT present new possibilities for human-expert diagnostics, and for automated ischaemia detection.
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spelling pubmed-47493002016-02-26 Electrocardiogram ST-Segment Morphology Delineation Method Using Orthogonal Transformations Amon, Miha Jager, Franc PLoS One Research Article Differentiation between ischaemic and non-ischaemic transient ST segment events of long term ambulatory electrocardiograms is a persisting weakness in present ischaemia detection systems. Traditional ST segment level measuring is not a sufficiently precise technique due to the single point of measurement and severe noise which is often present. We developed a robust noise resistant orthogonal-transformation based delineation method, which allows tracing the shape of transient ST segment morphology changes from the entire ST segment in terms of diagnostic and morphologic feature-vector time series, and also allows further analysis. For these purposes, we developed a new Legendre Polynomials based Transformation (LPT) of ST segment. Its basis functions have similar shapes to typical transient changes of ST segment morphology categories during myocardial ischaemia (level, slope and scooping), thus providing direct insight into the types of time domain morphology changes through the LPT feature-vector space. We also generated new Karhunen and Lo ève Transformation (KLT) ST segment basis functions using a robust covariance matrix constructed from the ST segment pattern vectors derived from the Long Term ST Database (LTST DB). As for the delineation of significant transient ischaemic and non-ischaemic ST segment episodes, we present a study on the representation of transient ST segment morphology categories, and an evaluation study on the classification power of the KLT- and LPT-based feature vectors to classify between ischaemic and non-ischaemic ST segment episodes of the LTST DB. Classification accuracy using the KLT and LPT feature vectors was 90% and 82%, respectively, when using the k-Nearest Neighbors (k = 3) classifier and 10-fold cross-validation. New sets of feature-vector time series for both transformations were derived for the records of the LTST DB which is freely available on the PhysioNet website and were contributed to the LTST DB. The KLT and LPT present new possibilities for human-expert diagnostics, and for automated ischaemia detection. Public Library of Science 2016-02-10 /pmc/articles/PMC4749300/ /pubmed/26863140 http://dx.doi.org/10.1371/journal.pone.0148814 Text en © 2016 Amon, Jager http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Amon, Miha
Jager, Franc
Electrocardiogram ST-Segment Morphology Delineation Method Using Orthogonal Transformations
title Electrocardiogram ST-Segment Morphology Delineation Method Using Orthogonal Transformations
title_full Electrocardiogram ST-Segment Morphology Delineation Method Using Orthogonal Transformations
title_fullStr Electrocardiogram ST-Segment Morphology Delineation Method Using Orthogonal Transformations
title_full_unstemmed Electrocardiogram ST-Segment Morphology Delineation Method Using Orthogonal Transformations
title_short Electrocardiogram ST-Segment Morphology Delineation Method Using Orthogonal Transformations
title_sort electrocardiogram st-segment morphology delineation method using orthogonal transformations
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4749300/
https://www.ncbi.nlm.nih.gov/pubmed/26863140
http://dx.doi.org/10.1371/journal.pone.0148814
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