A Trainable Neural Network Ensemble for ECG Beat Classification

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Ensemble for ECG Beat Classification</title> <meta name="description" content="A Trainable Neural Network Ensemble for ECG Beat Classification"> <meta name="keywords" content="ECG beat Classification; Combining Classifiers;Premature Ventricular Contraction (PVC); Multi Layer Perceptrons;Wavelet Transform"> <meta name="viewport" content="width=device-width, initial-scale=1, minimum-scale=1, maximum-scale=1, user-scalable=no"> <meta charset="utf-8"> <meta name="citation_title" content="A Trainable Neural Network Ensemble for ECG Beat Classification"> <meta name="citation_author" content="Atena Sajedin"> <meta name="citation_author" content="Shokoufeh Zakernejad"> <meta name="citation_author" content="Soheil Faridi"> <meta name="citation_author" content="Mehrdad Javadi"> <meta name="citation_author" content="Reza Ebrahimpour"> <meta name="citation_publication_date" content="2010/09/24"> <meta name="citation_journal_title" content="International Journal of Biomedical and Biological Engineering"> <meta name="citation_volume" content="4"> <meta name="citation_issue" content="9"> <meta name="citation_firstpage" content="479"> <meta name="citation_lastpage" content="485"> <meta name="citation_pdf_url" content="https://publications.waset.org/915/pdf"> <link href="https://cdn.waset.org/favicon.ico" type="image/x-icon" rel="shortcut icon"> <link href="https://cdn.waset.org/static/plugins/bootstrap-4.2.1/css/bootstrap.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/plugins/fontawesome/css/all.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/css/site.css?v=150220211555" rel="stylesheet"> </head> <body> <header> <div class="container"> <nav class="navbar navbar-expand-lg navbar-light"> <a class="navbar-brand" href="https://waset.org"> <img src="https://cdn.waset.org/static/images/wasetc.png" alt="Open Science Research Excellence" title="Open Science Research Excellence" /> </a> <button class="d-block d-lg-none navbar-toggler ml-auto" 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href="https://publications.waset.org/search?q=Atena%20Sajedin">Atena Sajedin</a>, <a href="https://publications.waset.org/search?q=Shokoufeh%20Zakernejad"> Shokoufeh Zakernejad</a>, <a href="https://publications.waset.org/search?q=Soheil%20Faridi"> Soheil Faridi</a>, <a href="https://publications.waset.org/search?q=Mehrdad%20Javadi"> Mehrdad Javadi</a>, <a href="https://publications.waset.org/search?q=Reza%20Ebrahimpour"> Reza Ebrahimpour</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper illustrates the use of a combined neural network model for classification of electrocardiogram (ECG) beats. We present a trainable neural network ensemble approach to develop customized electrocardiogram beat classifier in an effort to further improve the performance of ECG processing and to offer individualized health care. We process a three stage technique for detection of premature ventricular contraction (PVC) from normal beats and other heart diseases. This method includes a denoising, a feature extraction and a classification. At first we investigate the application of stationary wavelet transform (SWT) for noise reduction of the electrocardiogram (ECG) signals. Then feature extraction module extracts 10 ECG morphological features and one timing interval feature. Then a number of multilayer perceptrons (MLPs) neural networks with different topologies are designed. The performance of the different combination methods as well as the efficiency of the whole system is presented. Among them, Stacked Generalization as a proposed trainable combined neural network model possesses the highest recognition rate of around 95%. Therefore, this network proves to be a suitable candidate in ECG signal diagnosis systems. ECG samples attributing to the different ECG beat types were extracted from the MIT-BIH arrhythmia database for the study. <iframe src="https://publications.waset.org/915.pdf" style="width:100%; height:400px;" frameborder="0"></iframe> <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=ECG%20beat%20Classification%3B%20Combining%20Classifiers%3BPremature%20Ventricular%20Contraction%20%28PVC%29%3B%20Multi%20Layer%20Perceptrons%3BWavelet%20Transform" title="ECG beat Classification; Combining Classifiers;Premature Ventricular Contraction (PVC); Multi Layer Perceptrons;Wavelet Transform">ECG beat Classification; Combining Classifiers;Premature Ventricular Contraction (PVC); Multi Layer Perceptrons;Wavelet Transform</a> </p> <p class="card-text"><strong>Digital Object Identifier (DOI):</strong> <a href="https://doi.org/10.5281/zenodo.1329306" target="_blank">doi.org/10.5281/zenodo.1329306</a> </p> <a href="https://publications.waset.org/915/a-trainable-neural-network-ensemble-for-ecg-beat-classification" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/915/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/915/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/915/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/915/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/915/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/915/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/915/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/915/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/915/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/915/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/915.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">2216</span> </span> <p class="card-text"><strong>References:</strong></p> <br>[1] R. 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A Trainable Neural Network Ensemble for ECG Beat Classification