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ORIGINAL RESEARCH article

Front. Neurol.
Sec. Epilepsy
Volume 15 - 2024 | doi: 10.3389/fneur.2024.1390465

Automated sleep staging on reduced channels in children with epilepsy

Provisionally accepted

The final, formatted version of the article will be published soon.

    Objectives: To validate a sleep staging algorithm using in hospital video-electroencephalogram (EEG) in children without epilepsy, with well-controlled epilepsy (WCE) and with drug-resistant epilepsy (DRE). Methods: Overnight video-EEG, with addition of electro-oculogram (EOG) and chin electromyogram (EMG), was recorded in children between 4 and 18 years of age. Classical sleep staging was performed manually as a ground truth. An end-to-end hierarchical recurrent neural network for sequence-to-sequence automatic sleep staging (SeqSleepNet) was used to perform automated sleep staging using three channels: C4-A1, EOG and chin EMG. Results: In 176 children sleep stages were manually scored: 47 children without epilepsy, 74 with WCE and 55 with DRE. The 5-class sleep staging accuracy of the automatic sleep staging algorithm was 84.7% for the children without epilepsy, 83.5% for those with WCE, and 80.8% for those with DRE (Kappa of 0.79, 0.77 and 0.73 respectively). Performance per sleep stage was assessed with an F1-score of 0.91 for wake, 0.50 for N1, 0.83 for N2, 0.84 for N3 and 0.86 for rapid eye movement (REM) sleep. Conclusions: We conclude that the tested algorithm has a high accuracy in children without epilepsy and with WCE. Performance in children with DRE was still acceptable, but significantly lower, which could be explained by a tendency of more time spent in N1 and by abundant interictal epileptiform discharges and intellectual disability leading to less recognizable sleep stages. REM sleep time however, significantly affected in children with DRE, can be detected reliably by the algorithm.

    Keywords: Children, Epilepsy, REM sleep, Automated sleep staging, machine learning

    Received: 23 Feb 2024; Accepted: 15 Apr 2024.

    Copyright: © 2024 Proost, Heremans, Lagae, Van Paesschen, De Vos and Jansen. 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) or licensor 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: Renee Proost, KU Leuven, Leuven, Belgium

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