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Sadoughi A, Shamsollahi MB, Fatemizadeh E, et al. Detection of Apnea Bradycardia from ECG Signals of Preterm Infants Using Layered Hidden Markov Model. Ann Biomed Eng. 2021;49(9):2159-2169doi: 10.1007/s10439-021-02732-z.
Sadoughi, A., Shamsollahi, M. B., Fatemizadeh, E., Beuchée, A., Hernández, A. I., & Montazeri Ghahjaverestan, N. (2021). Detection of Apnea Bradycardia from ECG Signals of Preterm Infants Using Layered Hidden Markov Model. Annals of biomedical engineering, 49(9), 2159-2169. https://doi.org/10.1007/s10439-021-02732-z
Sadoughi, Azadeh, et al. "Detection of Apnea Bradycardia from ECG Signals of Preterm Infants Using Layered Hidden Markov Model." Annals of biomedical engineering vol. 49,9 (2021): 2159-2169. doi: https://doi.org/10.1007/s10439-021-02732-z
Sadoughi A, Shamsollahi MB, Fatemizadeh E, Beuchée A, Hernández AI, Montazeri Ghahjaverestan N. Detection of Apnea Bradycardia from ECG Signals of Preterm Infants Using Layered Hidden Markov Model. Ann Biomed Eng. 2021 Sep;49(9):2159-2169. doi: 10.1007/s10439-021-02732-z. Epub 2021 Feb 26. PMID: 33638031.
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Ann Biomed Eng. 2021 Sep;49(9):2159-2169. doi: 10.1007/s10439-021-02732-z. Epub 2021 Feb 26.

Detection of Apnea Bradycardia from ECG Signals of Preterm Infants Using Layered Hidden Markov Model.

Annals of biomedical engineering

Azadeh Sadoughi, Mohammad Bagher Shamsollahi, Emad Fatemizadeh, Alain Beuchée, Alfredo I Hernández, Nasim Montazeri Ghahjaverestan

Affiliations

  1. Faculty of Medical Sciences and Technologies, Science and Research Branch, Islamic Azad University, Tehran, Iran.
  2. Biomedical Signal and Image Processing Laboratory (BiSIPL), School of Electrical Engineering, Sharif University of Technology, Tehran, Iran. [email protected].
  3. Biomedical Signal and Image Processing Laboratory (BiSIPL), School of Electrical Engineering, Sharif University of Technology, Tehran, Iran.
  4. Univ Rennes, CHU Rennes, Inserm, LTSI - UMR 1099, 35000, Rennes, France.
  5. KITE - Toronto Rehabilitation Institute, University Health Network, Toronto, ON, Canada.
  6. Institute of Biomaterials and Biomedical Engineering, University of Toronto, Toronto, ON, Canada.

PMID: 33638031 DOI: 10.1007/s10439-021-02732-z

Abstract

Apnea-bradycardia (AB) is a common complication in prematurely born infants, which is associated with reduced survival and neurodevelopmental outcomes. Thus, early detection or predication of AB episodes is critical for initiating preventive interventions. To develop automatic real-time operating systems for early detection of AB, recent advances in signal processing can be employed. Hidden Markov Models (HMM) are probabilistic models with the ability of learning different dynamics of the real time-series such as clinical recordings. In this study, a hierarchy of HMMs named as layered HMM was presented to detect AB episodes from pre-processed single-channel Electrocardiography (ECG). For training the hierarchical structure, RR interval, and width of QRS complex were extracted from ECG as observations. The recordings of 32 premature infants with median 31.2 (29.7, 31.9) weeks of gestation were used for this study. The performance of the proposed layered HMM was evaluated in detecting AB. The best average accuracy of 97.14 ± 0.31% with detection delay of - 5.05 ± 0.41 s was achieved. The results show that layered structure can improve the performance of the detection system in early detecting of AB episodes. Such system can be incorporated for more robust long-term monitoring of preterm infants.

© 2021. Biomedical Engineering Society.

Keywords: Apnea-Bradycardia; Early detection; Electrocardiography; Hidden Markov Model; Machine learning

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