Adaptive threshold algorithm for detecting EEG-interburst intervals in extremely preterm neonates

Abstract

Objective. This study provides an adaptive threshold algorithm for burst detection in electroencephalograms (EEG) of preterm infantes and evaluates its performance using clinical real-world EEG data. Approach. We developed an adaptive threshold algorithm for burst detection in EEG recordings from preterm infants. To assess its applicability in the real-world, we tested the algorithm on a dataset of 30 clinical EEG recordings which were not preselected for good quality, to ensure a real-world scenario. Main results. Interrater agreement was substantial at a kappa of 0.73 (0.68-0.79 inter-quantile range). The performance of the algorithm showed a similar agreement with one clinical expert of 0.73 (0.67-0.76) and a sensitivity and specificity of 0.90 (0.82-0.94) and 0.95 (0.93-0.97), respectively. Significance. The adaptive threshold algorithm demonstrated robust performance in detecting burst patterns in clinical EEG data from preterm infants, highlighting its practical utility. The fine-tuned algorithm achieved similar performance to human raters. The algorithm proves to be a valuable tool for automated burst detection in the EEG of preterm infants.