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Apnea bradycardia detection based on new coupled hidden semi Markov model

Montazeri Ghahjaverestan, N ; Sharif University of Technology | 2020

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  1. Type of Document: Article
  2. DOI: 10.1007/s11517-020-02277-8
  3. Publisher: Springer Science and Business Media Deutschland GmbH , 2020
  4. Abstract:
  5. 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 proposed algorithm was able to detect the desired dynamic with sensitivity of 96.67% and specificity of 98.98%. Furthermore, the method detected the apnea bradycardia episodes with 94.87% sensitivity and 96.52% specificity with mean time delay of 0.73 s. The results show that the algorithm based on CHSMM is a robust tool for monitoring of preterm infants in detecting apnea bradycardia episodes. [Figure not available: see fulltext.]. © 2020, International Federation for Medical and Biological Engineering
  6. Keywords:
  7. Apnea bradycardia (AB) ; Coupled hidden Markov model (CHMM) ; Coupled hidden semi Markov model (CHSMM) ; Electrocardiography (ECG) ; Forward-backward (FB) algorithm ; Signal detection ; Stochastic models ; Stochastic systems ; Detection algorithm ; Electrocardiogram signal ; Finite number ; Hidden semi-Markov modeling ; Hidden state ; Mean time delays ; Mutual interaction ; Preterm infants ; Hidden Markov models
  8. Source: Medical and Biological Engineering and Computing ; 12 November , 2020
  9. URL: https://link.springer.com/article/10.1007/s11517-020-02277-8