Modelling temporal evolution of cardiac electrophysiological features using Hidden Semi-Markov Models

Autor: Alfredo Hernandez, Jérôme Dumont, Guy Carrault, Julien Fleureau
Přispěvatelé: Laboratoire Traitement du Signal et de l'Image (LTSI), Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Institut National de la Santé et de la Recherche Médicale (INSERM), IEEE, Université de Rennes (UR)-Institut National de la Santé et de la Recherche Médicale (INSERM), Senhadji, Lotfi
Jazyk: angličtina
Rok vydání: 2008
Předmět:
Multivariate statistics
MESH: Models
Cardiovascular
Continuous density
Computer science
Myocardial Ischemia
02 engineering and technology
01 natural sciences
Pattern Recognition
Automated
010104 statistics & probability
Electrocardiography
[INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing
[MATH.MATH-ST]Mathematics [math]/Statistics [math.ST]
0202 electrical engineering
electronic engineering
information engineering
MESH: Pattern Recognition
Automated
Diagnosis
Computer-Assisted
[MATH.MATH-ST] Mathematics [math]/Statistics [math.ST]
[STAT.TH] Statistics [stat]/Statistics Theory [stat.TH]
Models
Cardiovascular
Markov Chains
[SDV.MHEP.CSC] Life Sciences [q-bio]/Human health and pathology/Cardiology and cardiovascular system
MESH: Reproducibility of Results
MESH: Myocardial Ischemia
020201 artificial intelligence & image processing
[SDV.IB]Life Sciences [q-bio]/Bioengineering
[SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing
Algorithms
[INFO.INFO-TS] Computer Science [cs]/Signal and Image Processing
Quantitative Biology::Tissues and Organs
MESH: Algorithms
Markov model
Sensitivity and Specificity
Article
MESH: Computer Simulation
[SDV.MHEP.CSC]Life Sciences [q-bio]/Human health and pathology/Cardiology and cardiovascular system
Artificial Intelligence
MESH: Markov Chains
Humans
MESH: Artificial Intelligence
Computer Simulation
0101 mathematics
Cluster analysis
[SPI.SIGNAL] Engineering Sciences [physics]/Signal and Image processing
[SDV.IB] Life Sciences [q-bio]/Bioengineering
Models
Statistical
MESH: Humans
Series (mathematics)
Markov chain
business.industry
MESH: Diagnosis
Computer-Assisted
Reproducibility of Results
Pattern recognition
[STAT.TH]Statistics [stat]/Statistics Theory [stat.TH]
MESH: Sensitivity and Specificity
MESH: Electrocardiography
Artificial intelligence
business
MESH: Models
Statistical
Zdroj: Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference
Proceedings of the 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society
Annual International Conference of the IEEE Engineering in Medicine and Biology Society : Personalized Healthcare through Technology
Conference proceedings : .. Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference, Institute of Electrical and Electronics Engineers (IEEE), 2008, 2008, pp.165-8. ⟨10.1109/IEMBS.2008.4649116⟩
Conference proceedings : .. Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference, 2008, 2008, pp.165-8. ⟨10.1109/IEMBS.2008.4649116⟩
ISSN: 1557-170X
DOI: 10.1109/IEMBS.2008.4649116⟩
Popis: International audience; This paper presents a new method to analyse cardiac electrophysiological dynamics. It aims to classify or to cluster (i.e. to find natural groups) patients according to the dynamics of features extracted from their ECG. In this work, the dynamics of the features are modelled with Continuous Density Hidden Semi-Markovian Models (CDHSMM) which are interesting for the characterization of continuous multivariate time series without a priori information. These models can be easily used for classification and clustering. In this last case, a specific method, based on a fuzzy Expectation Maximisation (EM) algorithm, is proposed. Both tasks are applied to the analysis of ischemic episodes with encouraging results and a classification accuracy of 71%.
Databáze: OpenAIRE
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