Online Bayesian apnea-bradycardia detection using auto-regressive models

This paper proposes an online Bayesian method to detect change points in auto-regressive (AR) processes with unknown model orders. The AR model is frequently used in the spectral analysis of RR series extracted from electrocardio-graphic signals (ECG) [1, 2]. By relaxing the model order constraint, we aim to detect apnea-bradycardia (AB) episodes from abrupt changes in the model space. An efficient recursive algorithm inspired from the work of Godsil et al. [3, 4] is proposed to update with fixed complexity the joint posterior distribution of the AR coefficients and model orders. Simulation results show fast convergence of the estimated distribution, thus making it an efficient tool to detect underlying AR model changes in time series. For AB detections with annotated ECG data, the detection sensitivity (TP/(TP + FN)) reaches 98% over a total of 50 episodes with 92% specificity (TN/(TN + FP)). We also discovered an interesting property in terms of detection delay (-3.64s ± 4.34), compared with the experts´ off-line annotations. The negative mean in detection delay suggests that AR model changes might occur before the onset of AB episodes while from the clinical point of view, it is essential to achieve reliable early stage detection of AB episodes to enable the initiation of quick nursing actions [5].