Classifying neonatal spells using real-time temporal analysis of physiological data streams: Algorithm development

Abstract

Neonatal spells are cardiorespiratory events that occur in newborn infants with variable combinations of cessation of breathing, decrease in blood oxygen saturation and decrease in heart rate. A system using real-time temporal analysis of physiological data streams to accurately detect pauses in breathing together with changes in heart rate and blood oxygen saturation is described. The system uses a multidimensional online health analytics environment that supports the acquisition, transmission and real-time processing of high volume, high rate data. A family of algorithms has been developed using IBM Infosphere Streams, a scalable middleware component for analysing multiple streams of data in real-time. Respiratory pauses are identified by accurately detecting breaths and by calculating the time interval since the last breath. Changes in heart rate and blood oxygen saturation are identified by both threshold breaches and the detection of relative change. The algorithms detect relative change by assessing a sliding normal baseline and generating alerts when values fall out of range. The output of these algorithms has been shown to detect clinically significant relative changes in both heart rate and blood oxygen saturation in a single use case study. The specificity of the algorithm is 98.5%; the sensitivity is 100%. Future research will focus on the application of these algorithms for the assessment and classification of neonatal spells.