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Article Dans Une Revue Annals of Biomedical Engineering Année : 2021

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

Résumé

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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Dates et versions

hal-03188757 , version 1 (02-04-2021)

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Citer

Azadeh Sadoughi, Mohammad Bagher Shamsollahi, Emad Fatemizadeh, Alain Beuchee, Alfredo Hernandez, et al.. Detection of Apnea Bradycardia from ECG Signals of Preterm Infants Using Layered Hidden Markov Model. Annals of Biomedical Engineering, 2021, 49 (9), pp.2159-2169. ⟨10.1007/s10439-021-02732-z⟩. ⟨hal-03188757⟩
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