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The Performance of Short-Term Heart Rate Variability in the Detection of Congestive Heart Failure
Congestive heart failure (CHF) is a cardiac disease associated with the decreasing capacity of the cardiac output. It has been shown that the CHF is the main cause of the cardiac death around the world. Some works proposed to discriminate CHF subjects from healthy subjects using either electrocardio...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Hindawi Publishing Corporation
2016
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5116360/ https://www.ncbi.nlm.nih.gov/pubmed/27891509 http://dx.doi.org/10.1155/2016/1675785 |
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author | Lucena, Fausto Barros, Allan Kardec Ohnishi, Noboru |
author_facet | Lucena, Fausto Barros, Allan Kardec Ohnishi, Noboru |
author_sort | Lucena, Fausto |
collection | PubMed |
description | Congestive heart failure (CHF) is a cardiac disease associated with the decreasing capacity of the cardiac output. It has been shown that the CHF is the main cause of the cardiac death around the world. Some works proposed to discriminate CHF subjects from healthy subjects using either electrocardiogram (ECG) or heart rate variability (HRV) from long-term recordings. In this work, we propose an alternative framework to discriminate CHF from healthy subjects by using HRV short-term intervals based on 256 RR continuous samples. Our framework uses a matching pursuit algorithm based on Gabor functions. From the selected Gabor functions, we derived a set of features that are inputted into a hybrid framework which uses a genetic algorithm and k-nearest neighbour classifier to select a subset of features that has the best classification performance. The performance of the framework is analyzed using both Fantasia and CHF database from Physionet archives which are, respectively, composed of 40 healthy volunteers and 29 subjects. From a set of nonstandard 16 features, the proposed framework reaches an overall accuracy of 100% with five features. Our results suggest that the application of hybrid frameworks whose classifier algorithms are based on genetic algorithms has outperformed well-known classifier methods. |
format | Online Article Text |
id | pubmed-5116360 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2016 |
publisher | Hindawi Publishing Corporation |
record_format | MEDLINE/PubMed |
spelling | pubmed-51163602016-11-27 The Performance of Short-Term Heart Rate Variability in the Detection of Congestive Heart Failure Lucena, Fausto Barros, Allan Kardec Ohnishi, Noboru Biomed Res Int Research Article Congestive heart failure (CHF) is a cardiac disease associated with the decreasing capacity of the cardiac output. It has been shown that the CHF is the main cause of the cardiac death around the world. Some works proposed to discriminate CHF subjects from healthy subjects using either electrocardiogram (ECG) or heart rate variability (HRV) from long-term recordings. In this work, we propose an alternative framework to discriminate CHF from healthy subjects by using HRV short-term intervals based on 256 RR continuous samples. Our framework uses a matching pursuit algorithm based on Gabor functions. From the selected Gabor functions, we derived a set of features that are inputted into a hybrid framework which uses a genetic algorithm and k-nearest neighbour classifier to select a subset of features that has the best classification performance. The performance of the framework is analyzed using both Fantasia and CHF database from Physionet archives which are, respectively, composed of 40 healthy volunteers and 29 subjects. From a set of nonstandard 16 features, the proposed framework reaches an overall accuracy of 100% with five features. Our results suggest that the application of hybrid frameworks whose classifier algorithms are based on genetic algorithms has outperformed well-known classifier methods. Hindawi Publishing Corporation 2016 2016-11-06 /pmc/articles/PMC5116360/ /pubmed/27891509 http://dx.doi.org/10.1155/2016/1675785 Text en Copyright © 2016 Fausto Lucena et al. https://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Research Article Lucena, Fausto Barros, Allan Kardec Ohnishi, Noboru The Performance of Short-Term Heart Rate Variability in the Detection of Congestive Heart Failure |
title | The Performance of Short-Term Heart Rate Variability in the Detection of Congestive Heart Failure |
title_full | The Performance of Short-Term Heart Rate Variability in the Detection of Congestive Heart Failure |
title_fullStr | The Performance of Short-Term Heart Rate Variability in the Detection of Congestive Heart Failure |
title_full_unstemmed | The Performance of Short-Term Heart Rate Variability in the Detection of Congestive Heart Failure |
title_short | The Performance of Short-Term Heart Rate Variability in the Detection of Congestive Heart Failure |
title_sort | performance of short-term heart rate variability in the detection of congestive heart failure |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5116360/ https://www.ncbi.nlm.nih.gov/pubmed/27891509 http://dx.doi.org/10.1155/2016/1675785 |
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