English   español  
Please use this identifier to cite or link to this item: http://hdl.handle.net/10261/189909
Share/Impact:
Statistics
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:

Title

Automated real-time method for ventricular heartbeat classification

AuthorsOrtín González, Silvia ; Soriano, Miguel C. ; Alfaras, Miquel; Mirasso, Claudio R.
KeywordsBiomedical signal processing
ECG heartbeat classification
Reservoir computing
Template matching
Issue DateFeb-2019
PublisherElsevier
CitationComputer Methods and Programs in Biomedicine 169: 1-8 (2019)
Abstract[Background and objective] In this work, we develop a fully automatic and real-time ventricular heartbeat classifier based on a single ECG lead. Single ECG lead classifiers can be especially useful for wearable technologies that provide continuous and long-term monitoring of the electrocardiogram. These wearables usually have a few non-standard leads and the quality of the signals depends on the user physical activity.
[Methods] The proposed method uses an Echo State Network (ESN) to classify ECG signals following the Association for the Advancement of Medical Instrumentation (AAMI) recommendations with an inter-patient scheme. To achieve real-time classification, the classifier itself and the feature extraction approach are fast and computationally efficient. In addition, our approach allows transferring the knowledge from one database to another without additional training.
[Results] The classification performance of the proposed model is validated on the MIT-BIH arrhythmia and INCART databases. The sensitivity and precision of the proposed method for MIT-BIH arrhythmia database are 95.3 and 88.8 for the modified lead II and 90.9 and 89.2 for the V1 lead. The results reported are further compared to the existing methodologies in literature. Our methodology is a competitive single lead ventricular heartbeat classifier, that is comparable to state-of-the-art algorithms using multiple leads.
[Conclusions] The proposed fully automated, single-lead and real-time heartbeat classifier of ventricular heartbeats reports an improved classification accuracy in different leads of the evaluated databases in comparison with other single lead heartbeat classifiers. These results open the possibility of applying our methodology to wearable long-term monitoring devices with an unconventional placement of the electrodes.
Publisher version (URL)https://doi.org/10.1016/j.cmpb.2018.11.005
URIhttp://hdl.handle.net/10261/189909
DOI10.1016/j.cmpb.2018.11.005
ISSN0169-2607
E-ISSN1872-7565
Appears in Collections:(IFISC) Artículos
Files in This Item:
File Description SizeFormat 
accesoRestringido.pdf59,24 kBAdobe PDFThumbnail
View/Open
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.