Line length as a robust method to detect high-activity events: Automated burst detection in premature EEG recordings

Abstract

ObjectiveEEG is a valuable tool for evaluation of brain maturation in preterm babies. Preterm EEG constitutes of high voltage burst activities and more suppressed episodes, called interburst intervals (IBIs). Evolution of background characteristics provides information on brain maturation and helps in prediction of neurological outcome. The aim is to develop a method for automated burst detection. MethodsThirteen polysomnography recordings were used, collected at preterm postmenstrual age of 31.4 (26.1–34.4)weeks. We developed a burst detection algorithm based on the feature line length and compared it with manual scorings of clinical experts and other published methods. ResultsThe line length–based algorithm is robust (84.27% accuracy, 84.00% sensitivity, 85.70% specificity). It is not critically dependent on the number of measurement channels, because two channels still provide 82% accuracy. Furthermore, it approximates well clinically relevant features, such as median IBI duration 5.45 (4.00–7.11)s, maximum IBI duration 14.02 (8.73–18.80)s and burst percentage 48.89 (35.45–60.12)%, with a median deviation of respectively 0.65s, 1.96s and 6.55%. ConclusionAutomated assessment of long-term preterm EEG is possible and its use will optimize EEG interpretation in the NICU. SignificanceThis study takes a first step towards fully automatic analysis of the preterm brain.