English   español  
Please use this identifier to cite or link to this item: http://hdl.handle.net/10261/189424
logo share SHARE logo core CORE   Add this article to your Mendeley library MendeleyBASE

Visualizar otros formatos: MARC | Dublin Core | RDF | ORE | MODS | METS | DIDL | DATACITE
Exportar a otros formatos:


A Fast Machine Learning Model for ECG-Based Heartbeat Classification and Arrhythmia Detection

AuthorsAlfaras, Miquel; Soriano, Miguel C. ; Ortín González, Silvia
KeywordsEcho State Networks
Reservoir computing
Arrhythmia classification
Issue Date18-Jul-2019
PublisherFrontiers Media
CitationFrontiers in Physics 7: 103 (2019)
AbstractWe present a fully automatic and fast ECG arrhythmia classifier based on a simple brain-inspired machine learning approach known as Echo State Networks. Our classifier has a low-demanding feature processing that only requires a single ECG lead. Its training and validation follows an inter-patient procedure. Our approach is compatible with an online classification that aligns well with recent advances in health-monitoring wireless devices and wearables. The use of a combination of ensembles allows us to exploit parallelism to train the classifier with remarkable speeds. The heartbeat classifier is evaluated over two ECG databases, the MIT-BIH AR and the AHA. In the MIT-BIH AR database, our classification approach provides a sensitivity of 92.7% and positive predictive value of 86.1% for the ventricular ectopic beats, using the single lead II, and a sensitivity of 95.7% and positive predictive value of 75.1% when using the lead V1'. These results are comparable with the state of the art in fully automatic ECG classifiers and even outperform other ECG classifiers that follow more complex feature-selection approaches.
Publisher version (URL)https://doi.org/10.3389/fphy.2019.00103
Appears in Collections:(IFISC) Artículos
Files in This Item:
File Description SizeFormat 
fphy-07-00103.pdf1,37 MBAdobe PDFThumbnail
Show full item record
Review this work

Related articles:

WARNING: Items in Digital.CSIC are protected by copyright, with all rights reserved, unless otherwise indicated.