Online apnea–bradycardia detection based on hidden semi-Markov models

  • Miguel Altuve
  • Guy Carrault
  • Alain Beuchée
  • Patrick Pladys
  • Alfredo I. Hernández
Original Article


In this paper, we propose a new online apnea–bradycardia detection scheme that takes into account not only the instantaneous values of time series, but also their temporal evolution. The detector is based on a set of hidden semi-Markov models, representing the temporal evolution of beat-to-beat interval (RR interval) time series. A preprocessing step, including quantization and delayed version of the observation vector, is also proposed to maximize detection performance. This approach is quantitatively evaluated through simulated and real signals, the latter being acquired in neonatal intensive care units (NICU). Compared to two conventional detectors used in NICU, our best detector shows an improvement on average of around 15 % in sensitivity and 7 % in specificity. Furthermore, a reduced detection delay of approximately 2 s is also observed with respect to conventional detectors.


Time series analysis Hidden semi-Markov models Data mining Electrocardiogram Apnea–bradycardia 



This work was partly funded by the ECOS NORD project V09S04.


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Copyright information

© International Federation for Medical and Biological Engineering 2014

Authors and Affiliations

  • Miguel Altuve
    • 1
    • 2
  • Guy Carrault
    • 3
    • 4
  • Alain Beuchée
    • 3
    • 4
    • 5
  • Patrick Pladys
    • 3
    • 4
    • 5
  • Alfredo I. Hernández
    • 3
    • 4
  1. 1.Grupo de Bioingeniería y Biofísica AplicadaUniversidad Simón BolívarCaracasVenezuela
  2. 2.Departamento de Tecnología IndustrialUniversidad Simón Bolívar, Camurí GrandeCaracasVenezuela
  3. 3.Université de Rennes 1, LTSIRennesFrance
  4. 4.INSERM, U1099RennesFrance
  5. 5.Pôle Médico-Chirurgical de Pédiatrie et de Génétique Clinique, NéonatologieCHU RennesRennesFrance

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