Medical University of Vienna, Waehringer Guertel 18-20, Vienna 1090, Austria.
Center for Health & Bioresources, AIT (Austrian Institute of Technology), Vienna, Austria.
Physiol Meas. 2024 Oct 1;45(9). doi: 10.1088/1361-6579/ad7c05.
. 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.. 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.. 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.. 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.
. 本研究提供了一种用于早产儿脑电图（EEG）中爆发检测的自适应阈值算法，并使用临床实际 EEG 数据评估其性能。. 我们开发了一种用于早产儿 EEG 记录中爆发检测的自适应阈值算法。为了评估其在实际应用中的适用性，我们在一个由 30 个临床 EEG 记录组成的数据集上测试了该算法，这些记录没有预先选择高质量的记录，以确保真实场景。. 组内一致性很高，kappa 值为 0.73（0.68-0.79 四分位间距）。该算法的性能与一位临床专家的评估结果相似，kappa 值为 0.73（0.67-0.76），灵敏度和特异性分别为 0.90（0.82-0.94）和 0.95（0.93-0.97）。. 自适应阈值算法在检测早产儿临床 EEG 数据中的爆发模式方面表现出稳健的性能，突出了其实用价值。经过微调的算法达到了与人类评估者相似的性能。该算法被证明是早产儿 EEG 中自动爆发检测的有价值工具。
