NDLI: Premature Ventricular beat classification using a dynamic Bayesian Network

Content Provider	IEEE Xplore Digital Library
Author	de Oliveira, L.S.C. Andreao, R.V. Sarcinelli-Filho, M.
Copyright Year	2011
Description	Author affiliation: Federal Institute of Education, Science and Technology of Espirito Santo, Vitoria, ES, 29040-780, Brazil (Andreao, R.V.) \|\| Science e Technology Institute (ICT), Federal University of Vales do Jequitinhonha e Mucuri, Teófilo Otoni, Minas Gerais, Brazil (de Oliveira, L.S.C.) \|\| Department of Electrical Engineering, Federal University of Espirito Santo, Vitoria, ES, 29075-910, Brazil (Sarcinelli-Filho, M.)
Abstract	This paper investigates the viability of using the dynamic Bayesian Network framework as a tool to classify heart beats in long term ECG records. A Decision Support System composed by two layers is considered. The first layer performs the segmentation of each heartbeat available in the ECG record, whereas the second layer classifies the heartbeat as Premature Ventricular Contraction (PVC) or Other. The use of both static and dynamic Bayesian Networks is evaluated through using the records available in the MIT-BIH database, and the results show that the Dynamic one performs better, obtaining 95% of sensitivity and 98% of positive predictivity, showing that to consider the temporal relation among events is a good strategy to increase the certainty about present events.
Starting Page	4984
Ending Page	4987
File Size	832700
Page Count	4
File Format	PDF
ISBN	9781424441211
ISSN	1557170X
e-ISBN	9781457715891
e-ISBN	9781424441228
DOI	10.1109/IEMBS.2011.6091235
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-08-30
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Heart rate variability Bayesian methods Electrocardiography Databases Heart beat Training Probabilistic logic
Content Type	Text
Resource Type	Article
Subject	Signal Processing Biomedical Engineering Health Informatics Computer Vision and Pattern Recognition

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