Early Detection of Apnea-Bradycardia Episodes in Preterm Infants based on Coupled Hidden Markov Model

The incidence of apnea-bradycardia episodes in preterm infants may lead to neurological disorders. Prediction and detection of these episodes are an important task in healthcare systems. In this paper, a coupled hidden Markov model (CHMM) based method is applied to detect apnea-bradycardia episodes. This model is evaluated and compared with two other methods based on hidden Markov model (HMM) and hidden semi-Markov model (HSMM). Evaluation and comparison are performed on a dataset of 233 apnea-bradycardia episodes which have been manually annotated. Observations are composed of RR-interval time series and QRS duration time series. The performance of each method was evaluated in terms of sensitivity, specificity and time detection delay. Results show that CHMM has the sensitivity of 84.92%, specificity of 94.17% and time detection delay of 2.32 +/- 4.82 seconds, which are better than the reference methods.