Building an Open Source Classifier for the Neonatal EEG Background: A Systematic Feature-Based Approach From Expert Scoring to Clinical Visualization

• Published in: Frontiers in human neuroscience Vol. 15; p. 675154
• Main Authors: Montazeri, Saeed, Pinchefsky, Elana, Tse, Ilse, Marchi, Viviana, Kohonen, Jukka, Kauppila, Minna, Airaksinen, Manu, Tapani, Karoliina, Nevalainen, Päivi, Hahn, Cecil, Tam, Emily W. Y., Stevenson, Nathan J., Vanhatalo, Sampsa
• Format: Journal Article
• Language: English
• Published: Switzerland Frontiers Research Foundation 31.05.2021; Frontiers Media S.A
• Subjects: Algorithms; Annotations; artificial neural network; Asphyxia; background classifier; Classification; Computational neuroscience; Datasets; Design; EEG; EEG monitoring; Electroencephalography; Intensive care units; neonatal EEG; neonatal intensive care unit; Neonates; Neural networks; Neuroscience; Newborn babies; support vector machine; Support vector machines; Trends; Visualization; neonatal EEG; artificial neural network; support vector machine; EEG trend; EEG monitoring; neonatal intensive care unit; background classifier
• ISSN: 1662-5161; 1662-5161
• DOI: 10.3389/fnhum.2021.675154

Neonatal brain monitoring in the neonatal intensive care units (NICU) requires a continuous review of the spontaneous cortical activity, i.e., the electroencephalograph (EEG) background activity. This needs development of bedside methods for an automated assessment of the EEG background activity. In this paper, we present development of the key components of a neonatal EEG background classifier, starting from the visual background scoring to classifier design, and finally to possible bedside visualization of the classifier results. A dataset with 13,200 5-minute EEG epochs (8–16 channels) from 27 infants with birth asphyxia was used for classifier training after scoring by two independent experts. We tested three classifier designs based on 98 computational features, and their performance was assessed with respect to scoring system, pre- and post-processing of labels and outputs, choice of channels, and visualization in monitor displays. The optimal solution achieved an overall classification accuracy of 97% with a range across subjects of 81–100%. We identified a set of 23 features that make the classifier highly robust to the choice of channels and missing data due to artefact rejection. Our results showed that an automated bedside classifier of EEG background is achievable, and we publish the full classifier algorithm to allow further clinical replication and validation studies.