Data from: Nonlinear dynamics forecasting of obstructive sleep apnea onsets

Recent advances in sensor technologies and predictive analytics are
fueling the growth in point-of-care (POC) therapies for obstructive sleep
apnea (OSA) and other sleep disorders. The effectiveness of POC therapies
can be enhanced by providing personalized and real-time prediction of OSA
episode onsets. Previous attempts at OSA prediction are limited to
capturing the nonlinear, nonstationary dynamics of the underlying
physiological processes. This paper reports an investigation into heart
rate dynamics aiming to predict in real time the onsets of OSA episode
before the clinical symptoms appear. A prognosis method based on a
nonparametric statistical Dirichlet-Process Mixture-Gaussian-Process
(DPMG) model to estimate the transition from normal states to an anomalous
(apnea) state is utilized to estimate the remaining time until the onset
of an impending OSA episode. The approach was tested using three datasets
including (1) 20 records from 14 OSA subjects in benchmark ECG apnea
databases (Physionet.org), (2) records of 10 OSA patients from the
University of Dublin OSA database and (3) records of eight subjects from
previous work. Validation tests suggest that the model can be used to
track the time until the onset of an OSA episode with the likelihood of
correctly predicting apnea onset in 1 min to 5 mins ahead is 83.6 ± 9.3%,
80 ± 8.1%, 76.2 ± 13.3%, 66.9 ± 15.4%, and 61.1 ± 16.7%, respectively. The
present prognosis approach can be integrated with wearable devices,
enhancing proactive treatment of OSA and real-time wearable sensor-based
of sleep disorders.