Cargando…
A Novel Automatic Detection System for ECG Arrhythmias Using Maximum Margin Clustering with Immune Evolutionary Algorithm
This paper presents a novel maximum margin clustering method with immune evolution (IEMMC) for automatic diagnosis of electrocardiogram (ECG) arrhythmias. This diagnostic system consists of signal processing, feature extraction, and the IEMMC algorithm for clustering of ECG arrhythmias. First, raw E...
Autores principales: | , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Hindawi Publishing Corporation
2013
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3652208/ https://www.ncbi.nlm.nih.gov/pubmed/23690875 http://dx.doi.org/10.1155/2013/453402 |
_version_ | 1782269302854909952 |
---|---|
author | Zhu, Bohui Ding, Yongsheng Hao, Kuangrong |
author_facet | Zhu, Bohui Ding, Yongsheng Hao, Kuangrong |
author_sort | Zhu, Bohui |
collection | PubMed |
description | This paper presents a novel maximum margin clustering method with immune evolution (IEMMC) for automatic diagnosis of electrocardiogram (ECG) arrhythmias. This diagnostic system consists of signal processing, feature extraction, and the IEMMC algorithm for clustering of ECG arrhythmias. First, raw ECG signal is processed by an adaptive ECG filter based on wavelet transforms, and waveform of the ECG signal is detected; then, features are extracted from ECG signal to cluster different types of arrhythmias by the IEMMC algorithm. Three types of performance evaluation indicators are used to assess the effect of the IEMMC method for ECG arrhythmias, such as sensitivity, specificity, and accuracy. Compared with K-means and iterSVR algorithms, the IEMMC algorithm reflects better performance not only in clustering result but also in terms of global search ability and convergence ability, which proves its effectiveness for the detection of ECG arrhythmias. |
format | Online Article Text |
id | pubmed-3652208 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2013 |
publisher | Hindawi Publishing Corporation |
record_format | MEDLINE/PubMed |
spelling | pubmed-36522082013-05-20 A Novel Automatic Detection System for ECG Arrhythmias Using Maximum Margin Clustering with Immune Evolutionary Algorithm Zhu, Bohui Ding, Yongsheng Hao, Kuangrong Comput Math Methods Med Research Article This paper presents a novel maximum margin clustering method with immune evolution (IEMMC) for automatic diagnosis of electrocardiogram (ECG) arrhythmias. This diagnostic system consists of signal processing, feature extraction, and the IEMMC algorithm for clustering of ECG arrhythmias. First, raw ECG signal is processed by an adaptive ECG filter based on wavelet transforms, and waveform of the ECG signal is detected; then, features are extracted from ECG signal to cluster different types of arrhythmias by the IEMMC algorithm. Three types of performance evaluation indicators are used to assess the effect of the IEMMC method for ECG arrhythmias, such as sensitivity, specificity, and accuracy. Compared with K-means and iterSVR algorithms, the IEMMC algorithm reflects better performance not only in clustering result but also in terms of global search ability and convergence ability, which proves its effectiveness for the detection of ECG arrhythmias. Hindawi Publishing Corporation 2013 2013-04-18 /pmc/articles/PMC3652208/ /pubmed/23690875 http://dx.doi.org/10.1155/2013/453402 Text en Copyright © 2013 Bohui Zhu et al. https://creativecommons.org/licenses/by/3.0/ 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 Zhu, Bohui Ding, Yongsheng Hao, Kuangrong A Novel Automatic Detection System for ECG Arrhythmias Using Maximum Margin Clustering with Immune Evolutionary Algorithm |
title | A Novel Automatic Detection System for ECG Arrhythmias Using Maximum Margin Clustering with Immune Evolutionary Algorithm |
title_full | A Novel Automatic Detection System for ECG Arrhythmias Using Maximum Margin Clustering with Immune Evolutionary Algorithm |
title_fullStr | A Novel Automatic Detection System for ECG Arrhythmias Using Maximum Margin Clustering with Immune Evolutionary Algorithm |
title_full_unstemmed | A Novel Automatic Detection System for ECG Arrhythmias Using Maximum Margin Clustering with Immune Evolutionary Algorithm |
title_short | A Novel Automatic Detection System for ECG Arrhythmias Using Maximum Margin Clustering with Immune Evolutionary Algorithm |
title_sort | novel automatic detection system for ecg arrhythmias using maximum margin clustering with immune evolutionary algorithm |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3652208/ https://www.ncbi.nlm.nih.gov/pubmed/23690875 http://dx.doi.org/10.1155/2013/453402 |
work_keys_str_mv | AT zhubohui anovelautomaticdetectionsystemforecgarrhythmiasusingmaximummarginclusteringwithimmuneevolutionaryalgorithm AT dingyongsheng anovelautomaticdetectionsystemforecgarrhythmiasusingmaximummarginclusteringwithimmuneevolutionaryalgorithm AT haokuangrong anovelautomaticdetectionsystemforecgarrhythmiasusingmaximummarginclusteringwithimmuneevolutionaryalgorithm AT zhubohui novelautomaticdetectionsystemforecgarrhythmiasusingmaximummarginclusteringwithimmuneevolutionaryalgorithm AT dingyongsheng novelautomaticdetectionsystemforecgarrhythmiasusingmaximummarginclusteringwithimmuneevolutionaryalgorithm AT haokuangrong novelautomaticdetectionsystemforecgarrhythmiasusingmaximummarginclusteringwithimmuneevolutionaryalgorithm |