title: On-line apnea-bradycardia detection using hidden semi-Markov models.
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.
authors.affiliation: Departamento de Technología Industrial, Universidad Simón Bolívar Caracas 1080, Venezuela, LTSI, Rennes F-35000, France. maltuve@usb.ve
authors.forename: Miguel
authors.initials: M
authors.lastname: Altuve
authors.forename: Guy
authors.initials: G
authors.lastname: Carrault
authors.forename: Alain
authors.initials: A
authors.lastname: Beuchée
authors.forename: Patrick
authors.initials: P
authors.lastname: Pladys
authors.forename: Alfredo I
authors.initials: AI
authors.lastname: Hernández
mesh_terms: D001049:Apnea / Q000175:diagnosis*; D001919:Bradycardia / Q000175:diagnosis*; D006801:Humans; D007231:Infant, Newborn; D008390:Markov Chains; D008962:Models, Theoretical; D017216:Telemedicine