NDLI: Cardiac Arrhythmia Detection by Parameters Sharing and MMIE Training of Hidden Markov Models

Content Provider	IEEE Xplore Digital Library
Author	Lima, C.S. Cardoso, M.J.
Copyright Year	2007
Description	Author affiliation: Univ. of Minho, Guimaraes (Lima, C.S.; Cardoso, M.J.)
Abstract	This paper is concerned to the cardiac arrhythmia classification by using hidden Markov models and maximum mutual information estimation (MMIE) theory. The types of beat being selected are normal (N), premature ventricular contraction (V), and the most common class of supra-ventricular arrhythmia (S), named atrial fibrillation (AF). The approach followed in this paper is based on the supposition that atrial fibrillation and normal beats are morphologically similar except that the former does not exhibit the P wave. In fact there are more differences as the irregularity of the RR interval, but ventricular conduction in AF is normal in morphology. Regarding to the Hidden Markov Models (HMM) modelling this can mean that these two classes can be modelled by HMM's of similar topology and sharing some parameters excepting the part of the HMM structure that models the P wave. This paper shows, under that underlying assumption, how this information can be compacted in only one HMM, increasing the classification accuracy by using MMIE training, and saving computational resources at run-time decoding. The algorithm performance was tested by using the MIT-BIH database. Better performance was obtained comparatively to the case where Maximum Likelihood Estimation training is used alone.
Starting Page	3836
Ending Page	3839
File Size	224385
Page Count	4
File Format	PDF
ISBN	9781424407873
ISSN	1557170X
DOI	10.1109/IEMBS.2007.4353169
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2007-08-22
Publisher Place	France
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Hidden Markov models Atrial fibrillation Mutual information Estimation theory Heart rate variability Morphology Topology Runtime Maximum likelihood decoding Testing
Content Type	Text
Resource Type	Article
Subject	Signal Processing Biomedical Engineering Health Informatics Computer Vision and Pattern Recognition

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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