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    Switching kalman filter based methods for apnea bradycardia detection from ECG signals

    , Article Physiological Measurement ; Volume 36, Issue 9 , 2015 , Pages 1763-1783 ; 09673334 (ISSN) Ghahjaverestan, N. M ; Shamsollahi, M. B ; Ge, D ; Hernandez, A. I ; Sharif University of Technology
    Apnea bradycardia (AB) is an outcome of apnea occurrence in preterm infants and is an observable phenomenon in cardiovascular signals. Early detection of apnea in infants under monitoring is a critical challenge for the early intervention of nurses. In this paper, we introduce two switching Kalman filter (SKF) based methods for AB detection using electrocardiogram (ECG) signal. The first SKF model uses McSharry's ECG dynamical model integrated in two Kalman filter (KF) models trained for normal and AB intervals. Whereas the second SKF model is established by using only the RR sequence extracted from ECG and two AR models to be fitted in normal and AB intervals. In both SKF approaches, a... 

    Coupled hidden markov model-based method for apnea bradycardia detection

    , Article IEEE Journal of Biomedical and Health Informatics ; Volume 20, Issue 2 , 2016 , Pages 527-538 ; 21682194 (ISSN) Montazeri Ghahjaverestan, N ; Masoudi, S ; Shamsollahi, M. B ; Beuchée, A ; Pladys, P ; Ge, D ; Hernández, A. I ; Sharif University of Technology
    Institute of Electrical and Electronics Engineers Inc 
    In this paper, we present a novel framework for the coupled hidden Markov model (CHMM), based on the forward and backward recursions and conditional probabilities, given a multidimensional observation. In the proposed framework, the interdependencies of states networks are modeled with Markovian-like transition laws that influence the evolution of hidden states in all channels. Moreover, an offline inference approach by maximum likelihood estimation is proposed for the learning procedure of model parameters. To evaluate its performance, we first apply the CHMM model to classify and detect disturbances using synthetic data generated by the FitzHugh-Nagumo model. The average sensitivity and... 

    Apnea bradycardia detection based on new coupled hidden semi Markov model

    , Article Medical and Biological Engineering and Computing ; 12 November , 2020 Montazeri Ghahjaverestan, N ; Shamsollahi, M. B ; Ge, D ; Beuchee, A ; Hernandez, A. I ; Sharif University of Technology
    Springer Science and Business Media Deutschland GmbH  2020
    In this paper, a method for apnea bradycardia detection in preterm infants is presented based on coupled hidden semi Markov model (CHSMM). CHSMM is a generalization of hidden Markov models (HMM) used for modeling mutual interactions among different observations of a stochastic process through using finite number of hidden states with corresponding resting time. We introduce a new set of equations for CHSMM to be integrated in a detection algorithm. The detection algorithm was evaluated on a simulated data to detect a specific dynamic and on a clinical dataset of electrocardiogram signals collected from preterm infants for early detection of apnea bradycardia episodes. For simulated data, the...