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    A combined dynamical sequential network for generating coupled cardiovascular signals with different beat types

    , Article ; 2010 3rd International Symposium on Applied Sciences in Biomedical and Communication Technologies, ISABEL 2010, 7 November 2010 through 10 November 2010 , 2010 ; 9781424481323 (ISBN) Sayadi, O ; Shamsollahi, M. B ; Sharif University of Technology
    Abstract
    We present generalizations of the previously published artificial models for generating abnormal cardiac rhythms to provide simulations of coupled cardiovascular (CV) signals with different beat morphologies. Using a joint dynamical formulation, we generate the normal morphologies of the cardiac cycle using a sum of Gaussian kernels, fitted to real CV recordings. The joint inter-dependencies of CV signals are introduced by assuming the same angular frequency and a phase coupling. Abnormal beats are then specified as new dynamical trajectories. An ergadic first-order Markov chain is also used for switching between normal and abnormal beat types. Probability transitions can be learned from... 

    An artificial multi-channel model for generating abnormal electrocardiographic rhythms

    , Article Computers in Cardiology 2008, CAR, Bologna, 14 September 2008 through 17 September 2008 ; Volume 35 , 2008 , Pages 773-776 ; 02766574 (ISSN); 1424437067 (ISBN); 9781424437061 (ISBN) Clifford, G. D ; Nemati, S ; Sameni, R ; Sharif University of Technology
    2008
    Abstract
    We present generalizations of our previously published artificial models for generating multi-channel ECG so that the simulation of abnormal rhythms is possible. Using a three-dimensional vectorcardiogram (VCG) formulation, we generate the normal cardiac dipole for a patient using a sum of Gaussian kernels, fitted to real VCG recordings. Abnormal beats are then specified either as new dipoles, or as perturbations of the existing dipole. Switching between normal and abnormal beat types is achieved using a hidden Markov model (HMM). Probability transitions can be learned from real data or modeled by coupling to heart rate and sympathovagal balance. Natural morphology changes form beat-to-beat... 

    Life-threatening arrhythmia verification in ICU patients using the joint cardiovascular dynamical model and a bayesian filter

    , Article IEEE Transactions on Biomedical Engineering ; Volume 58, Issue 10 PART 1 , 2011 , Pages 2748-2757 ; 00189294 (ISSN) Sayadi, O ; Shamsollahi, M. B ; Sharif University of Technology
    Abstract
    In this paper, a novel nonlinear joint dynamical model is presented, which is based on a set of coupled ordinary differential equations of motion and a Gaussian mixture model representation of pulsatile cardiovascular (CV) signals. In the proposed framework, the joint interdependences of CV signals are incorporated by assuming a unique angular frequency that controls the limit cycle of the heart rate. Moreover, the time consequence of CV signals is controlled by the same phase parameter that results in the space dimensionality reduction. These joint equations together with linear assignments to observation are further used in the Kalman filter structure for estimation and tracking. Moreover,...