NDLI: Detection of myocardial ischemia with hidden Semi-Markovian models

Content Provider	IEEE Xplore Digital Library
Author	Dumont, J. Carrault, G. Gomis, P. Wagner, G.S. Hernandez, A.I.
Copyright Year	2009
Description	Author affiliation: Universitat Politècnica de Catalunya (UPC), ESAII, Barcelona, Espagne, Spain (Gomis, P.) \|\| Duke University Medical Center, Durham NC, USA (Wagner, G.S.) \|\| Université de Rennes 1, LTSI, F-35000, France (Dumont, J.; Carrault, G.; Hernandez, A.I.)
Abstract	A new method for myocardial ischemia detection is proposed in this communication. The originality of this method relies on the analysis of the dynamics of times series extracted from the ECG, whereas traditional methods are based on static decision rules. After the extraction of a feature vector, from ECG signals from the STAFF3 database, the dynamics are characterised with an Hidden Semi-Markovian Model (HSMM). The ischemic detector uses a reference HSMM and an ischemic HSMM and then compare the log-likelihood of the time series. Results obtained with percutaneous transluminal coronary angioplasty (PTCA) records of the STAFF3 database show an improved detection rate (96% of sensibility and 80% of specificity) with respect to other methods applied on the same database.
Starting Page	121
Ending Page	124
File Size	2687481
Page Count	4
File Format	PDF
ISBN	9781424472819
ISSN	02766547
e-ISBN	9781424472826
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-09-13
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Computers in Cardiology(CinC)
Subject Keyword	Myocardium Ischemic pain Electrocardiography Detectors Databases Feature extraction Time series analysis Frequency Cardiology Artificial intelligence
Content Type	Text
Resource Type	Article

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