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

Annals of Biomedical Engineering volume 49, pages 2159–2169 (2021)Cite this article

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.

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Abbreviations

AB:

Apnea-bradycardia

NO:

Normal

NICU:

Neonatal intensive care unit

ECG:

Electrocardiography

HMM:

Hidden Markov Model

LHMM:

Layered Hidden Markov Model

CHMM:

Coupled Hidden Markov Model

HsMM:

Hidden semi-Markov Model

CHSMM:

Coupled Hidden semi-Markov Model

ACC:

Accuracy

SEN:

Sensitivity

SPC:

Specificity

ROC:

Receiver operating characteristic

AUC:

Area under the ROC curve

PD:

Perfect detection

DPD:

Distance to PD

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Conflict of interest

The authors have no conflict of interest to declare.

Author information

Authors and Affiliations

  1. Faculty of Medical Sciences and Technologies, Science and Research Branch, Islamic Azad University, Tehran, Iran

    Azadeh Sadoughi

  2. Biomedical Signal and Image Processing Laboratory (BiSIPL), School of Electrical Engineering, Sharif University of Technology, Tehran, Iran

    Mohammad Bagher Shamsollahi & Emad Fatemizadeh

  3. Univ Rennes, CHU Rennes, Inserm, LTSI - UMR 1099, 35000, Rennes, France

    Alain Beuchée & Alfredo I. Hernández

  4. KITE - Toronto Rehabilitation Institute, University Health Network, Toronto, ON, Canada

    Nasim Montazeri Ghahjaverestan

  5. Institute of Biomaterials and Biomedical Engineering, University of Toronto, Toronto, ON, Canada

    Nasim Montazeri Ghahjaverestan

Authors
  1. Azadeh Sadoughi
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  2. Mohammad Bagher Shamsollahi
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  3. Emad Fatemizadeh
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  4. Alain Beuchée
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  5. Alfredo I. Hernández
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  6. Nasim Montazeri Ghahjaverestan
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Correspondence to Mohammad Bagher Shamsollahi.

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Associate Editor Ka-Wai Kwok oversaw the review of this article.

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Sadoughi, A., Shamsollahi, M.B., Fatemizadeh, E. et al. Detection of Apnea Bradycardia from ECG Signals of Preterm Infants Using Layered Hidden Markov Model. Ann Biomed Eng 49, 2159–2169 (2021). https://doi.org/10.1007/s10439-021-02732-z

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  • DOI: https://doi.org/10.1007/s10439-021-02732-z

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