Automated EEG-based sleep monitoring in critically ill children

Master thesis (2021)

Authors

F.W. Hiemstra Mechanical Engineering

Contributors

D.M.J. Tax Pattern Recognition and Bioinformatics - EEMCS (supervisor 1)
J.W. Kuiper Erasmus MC (supervisor 1)
R.C.J. de Jonge Erasmus MC (supervisor 1)
A.B.G. Cramer Erasmus MC (coach)
Faculty
Mechanical Engineering
Copyright: © 2021 Floor Hiemstra
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Published Date

13-09-2021

Language

English

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Faculty

Mechanical Engineering

Abstract

Introduction: Sleep deprivation is commonly encountered in
critically ill children admitted to the pediatric intensive care unit (PICU)
and is associated with poor clinical outcome. Automated electroencephalography
(EEG)-based depth of sleep monitoring enables real-time continuous study of
sleep in PICU patients without the need for visual assessment of the EEG
signals, the gold standard. This study aims to evaluate the classification
performance of various index measures and machine learning models for sleep
monitoring in critically ill children.   Method:
Two EEG-index-based approaches, calculated as the ratio gamma/delta and of
gamma/(theta+delta) spectral powers, as well as three machine learning models -
decision tree (DT), support vector machine (SVM) and extreme gradient boosting
(XGBoost) - were trained and evaluated. The classification into three as well
as four sleep states was evaluated. Polysomnography (PSG) recordings of 120
non-critically ill patients were used for model optimization, training and
internal validation. As a proof-of-concept, the models were tested on the PSG
data of 10 PICU patients.   Results:
Whereas the machine learning models outperformed the index-measures in both
three- as well as four-state classification in PSG recordings of non-critically
ill children, the opposite was true for the PICU PSG data. Best results for PSG
data of non-critically ill patients were obtained with the XGBoost model, with
a 5-fold cross-validation accuracy of 0.79 (± 0.01) for three-state
classification. Performances for PICU PSG data were remarkably worse for all
models. The best results for PICU data were obtained with the index-based
approach (accuracy = 0.60) and the gamma/delta and gamma/(theta+delta)
performed equally. The individual assessment of model performances per PICU
patient revealed large variation between them.   Conclusion: A simple index measure is a
promising method to monitor sleep in PICU patients. Machine learning models
developed in non-critically ill patients cannot easily be applied to PICU
patients in whom the sleep EEG is frequently deviant. Future efforts should
focus on further tuning, training and validating the classification models with
more PICU data.