@inproceedings{befb2308002140478f038338b3d9bf42,
title = "Individualized apnea prediction in preterm infants using cardio-respiratory and movement signals",
abstract = "Apnea of prematurity is a common developmental disorder in preterm infants that is implicated in a number of acute and long-term complications. Therapeutic stochastic resonance (TSR) is a noninvasive preventative intervention for stabilizing breathing patterns and reducing the incidence of apnea and hypoxia. Because the stabilizing effect of TSR lags its initiation, it can be used most effectively if it is linked to a system for apnea prediction. We present a real-time algorithm for generating apnea predictions based on cardio-respiratory and movement features extracted from multiple physiological sensors. The features are used to create patient-specific statistical models of apnea precursors. The state parameters generated by these models are evaluated over time to form apnea predictions. The algorithms predictions are evaluated using a short, 5.5 minute prediction horizon. The algorithm obtains highly accurate predictions, with statistical significance obtained on five out of the six patients that it is evaluated on.",
keywords = "algorithm, bradycardia, feature vector, hypoxia, machine learning, monitoring, prematurity",
author = "Williamson, {James R.} and Daniel Bliss and Browne, {David W.} and Premananda Indic and Elisabeth Bloch-Salisbury and David Paydarfar",
year = "2013",
doi = "10.1109/BSN.2013.6575523",
language = "English (US)",
isbn = "9781479903306",
series = "2013 IEEE International Conference on Body Sensor Networks, BSN 2013",
booktitle = "2013 IEEE International Conference on Body Sensor Networks, BSN 2013",
note = "2013 IEEE International Conference on Body Sensor Networks, BSN 2013 ; Conference date: 06-05-2013 Through 09-05-2013",
}