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Saeed Montazeri1*
Elana Pinchefsky2
Ilse Tse1
Viviana Marchi1,3Jukka Kohonen4Minna Kauppila1
Manu Airaksinen1,5Karoliina Tapani1
Päivi Nevalainen1Cecil Hahn6
Emily W. Y. Tam6
Nathan J. Stevenson7
Sampsa Vanhatalo1,8- 1BABA Center, Pediatric Research Centre, Department of Clinical Neurophysiology, Children's Hospital and HUS Diagnostic Center, Helsinki University Hospital and University of Helsinki, Helsinki, Finland
- 2Division of Neurology, Department of Paediatrics, Sainte-Justine University Hospital Centre, University of Montreal, Montreal, QC, Canada
- 3Department of Developmental Neuroscience, Stella Maris Scientific Institute, IRCCS Fondazione Stella Maris Foundation, Pisa, Italy
- 4Department of Computer Science, Aalto University, Espoo, Finland
- 5Department of Signal Processing and Acoustics, Aalto University, Espoo, Finland
- 6Department of Paediatrics (Neurology), The Hospital for Sick Children and University of Toronto, Toronto, ON, Canada
- 7Brain Modelling Group, QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
- 8Neuroscience Center, Helsinki Institute of Life Science, University of Helsinki, Helsinki, Finland
A corrigendum on
Building an open source classifier for the neonatal EEG background: a systematic feature-based approach from expert scoring to clinical visualization
by Moghadam, S., Pinchefsky, E., Tse, I., Marchi, V., Kohonen, J., Kauppila, M., Airaksinen, M., Tapani, K., Nevalainen, P., Hahn, C., Tam, E. W. Y., Stevenson, N. J., and Vanhatalo, S. (2021). Front. Hum. Neurosci. 15:675154. doi: 10.3389/fnhum.2021.675154
In the published article, an author name was incorrectly written as “Saeed Montazeri Moghadam.” The correct spelling is “Saeed Montazeri.”
The authors apologize for this error and state that this does not change the scientific conclusions of the article in any way. The original article has been updated.
Publisher's note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
Keywords: neonatal EEG, EEG monitoring, neonatal intensive care unit, background classifier, support vector machine, artificial neural network, EEG trend
Citation: Montazeri S, Pinchefsky E, Tse I, Marchi V, Kohonen J, Kauppila M, Airaksinen M, Tapani K, Nevalainen P, Hahn C, Tam EWY, Stevenson NJ and Vanhatalo S (2024) Corrigendum: Building an open source classifier for the neonatal EEG background: a systematic feature-based approach from expert scoring to clinical visualization. Front. Hum. Neurosci. 18:1417744. doi: 10.3389/fnhum.2024.1417744
Received: 15 April 2024; Accepted: 17 April 2024;
Published: 29 April 2024.
Approved by:
Frontiers Editorial Office, Frontiers Media SA, SwitzerlandCopyright © 2024 Montazeri, Pinchefsky, Tse, Marchi, Kohonen, Kauppila, Airaksinen, Tapani, Nevalainen, Hahn, Tam, Stevenson and Vanhatalo. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Saeed Montazeri, c2FlZWQubW9udGF6ZXJpQGhlbHNpbmtpLmZp