Modelling temporal evolution of cardiac electrophysiological features using Hidden Semi-Markov Models.
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%.
Dumont
Jerome
J
INSERM, U642, and Université de Rennes, Rennes, F-35000, France. jerome.dumont@univ-rennes1.fr
Hernández
Alfredo I
AI
Fleureau
Julien
J
Carrault
Guy
G
D000465:Algorithms; D001185:Artificial Intelligence; D003198:Computer Simulation; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D004562:Electrocardiography / Q000379:methods*; D006801:Humans; D008390:Markov Chains; D008955:Models, Cardiovascular; D015233:Models, Statistical; D017202:Myocardial Ischemia / Q000175:diagnosis*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity