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

• Published in: Physiological measurement Vol. 45; no. 9; pp. 95017 - 95044
• Main Authors: Mader, Johannes, Hartmann, Manfred, Dressler, Anastasia, Oberdorfer, Lisa, Rona, Zsofia, Glatter, Sarah, Czaba-Hnizdo, Christine, Herta, Johannes, Kluge, Tilmann, Werther, Tobias, Berger, Angelika, Koren, Johannes, Klebermass-Schrehof, Katrin, Giordano, Vito
• Format: Journal Article
• Language: English
• Published: England IOP Publishing 01.09.2024
• Subjects: Algorithms; automated detection; EEG signal processing; Electroencephalography - methods; Humans; Infant, Extremely Premature - physiology; Infant, Newborn; preterm EEG; Signal Processing, Computer-Assisted; automated detection; preterm EEG; EEG signal processing
• ISSN: 0967-3334; 1361-6579; 1361-6579
• DOI: 10.1088/1361-6579/ad7c05

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.