Airaksinen, Manu

Hellström-Westas, Lena

Marchi, Viviana

Moghadam, Saeed Montazeri

Nevalainen, Päivi

Stevenson, Nathan J

Vanhatalo, Sampsa

English

Background Electroencephalogram (EEG) monitoring is recommended as routine in newborn neurocritical care to facilitate early therapeutic decisions and outcome predictions. EEG's larger-scale implementation is, however, hindered by the shortage of expertise needed for the interpretation of spontaneous cortical activity, the EEG background. We developed an automated algorithm that transforms EEG recordings to quantified interpretations of EEG background and provides simple intuitive visualisations in patient monitors. Methods In this method-development and proof-of-concept study, we collected visually classified EEGs from infants recovering from birth asphyxia or stroke. We used unsupervised learning methods to explore latent EEG characteristics, which guided the supervised training of a deep learning-based classifier. We assessed the classifier performance using cross-validation and an external validation dataset. We constructed a novel measure of cortical function, brain state of the newborn (BSN), from the novel EEG background classifier and a previously published sleep-state classifier. We estimated clinical utility of the BSN by identification of two key items in newborn brain monitoring, the onset of continuous cortical activity and sleep-wake cycling, compared with the visual interpretation of the raw EEG signal and the amplitude-integrated (aEEG) trend. Findings We collected 2561 h of EEG from 39 infants (gestational age 35 center dot 0-42 center dot 1 weeks; postnatal age 0-7 days). The external validation dataset included 105 h of EEG from 31 full-term infants. The overall accuracy of the EEG background classifier was 92% in the whole cohort (95% CI 91-96; range 85-100 for individual infants). BSN trend values were closely related to the onset of continuous EEG activity or sleep-wake cycling, and BSN levels showed robust difference between aEEG categories. The temporal evolution of the BSN trends showed early diverging trajectories in infants with severely abnormal outcomes. Interpretation The BSN trend can be implemented in bedside patient monitors as an EEG interpretation that is intuitive, transparent, and clinically explainable. A quantitative trend measure of brain function might harmonise practices across medical centres, enable wider use of brain monitoring in neurocritical care, and might facilitate clinical intervention trials. Copyright (c) 2022 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY-NC-ND 4.0 license

Nevalainen, Paivi

Stevenson, Nathan J.

Publikationer från Uppsala Universitet

An automated bedside measure for monitoring neonatal cortical activity : a supervised deep learning-based electroencephalogram classifier with external cohort validation

Helsingin yliopiston digitaalinen arkisto

An automated bedside measure for monitoring neonatal cortical activity: a supervised deep learning-based electroencephalogram classifier with external cohort validation

Digitala Vetenskapliga Arkivet - Academic Archive On-line

https://helda.helsinki.fi/bitstream/10138/355183/1/1_s2.0_S2589750022001960_main.pdf

An automated bedside measure for monitoring neonatal cortical activity: a supervised deep learning-based electroencephalogram classifier with external cohort validation

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Publikationer från Uppsala Universitet

Helsingin yliopiston digitaalinen arkisto

Digitala Vetenskapliga Arkivet - Academic Archive On-line