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. 2011:2011:4374-7.
doi: 10.1109/IEMBS.2011.6091085.

On-line apnea-bradycardia detection using hidden semi-Markov models

Miguel Altuve  1 , Guy Carrault, Alain Beuchée, Patrick Pladys, Alfredo I Hernández
Affiliations

On-line apnea-bradycardia detection using hidden semi-Markov models

Miguel Altuve et al. Annu Int Conf IEEE Eng Med Biol Soc. 2011.

Abstract

In this work, we propose a detection method that exploits not only the instantaneous values, but also the intrinsic dynamics of the RR series, for the detection of apnea-bradycardia episodes in preterm infants. A hidden semi-Markov model is proposed to represent and characterize the temporal evolution of observed RR series and different pre-processing methods of these series are investigated. This approach is quantitatively evaluated through synthetic 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 of around 13% in sensitivity and 7% in specificity. Furthermore, a reduced detection delay of approximately 3 seconds is obtained with respect to conventional detectors.

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Figures

Fig. 1
Fig. 1
ROC curves for AB event detection.
Fig. 2
Fig. 2
Mean detection delay of AB episodes.
See this image and copyright information in PMC

References

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