Automated sleep monitoring based on vital signs in critically ill children

Introduction: Critically ill children admitted to the Paediatric Intensive Care Unit (PICU) have a high risk of disruption of their normal sleep rhythm, which is associated with disturbances in physiology and negative effects on psychological and cognitive functioning. There is a need for real-time, automatic sleep monitoring to minimise disruptions in sleep patterns. The main objective of this thesis was to develop a machine learning model that can classify sleep based on vital signs in critically ill children. In addition, methods were investigated to optimise the decision criteria in multi-class problems. Methods: Three machine learning algorithms, logistic regression, random forest and extreme gradient boosting (XGBoost), were developed based on both the combination of features extracted from electrocardiogram (ECG) signal and pulse transit time (PTT) and on ECG features alone. To gain insight into the number of sleep stages that could be distinguished, the models were developed for two-class, three-class, four-class and five-class staging. The models were developed, trained and evaluated on a diagnostic dataset (n = 90) containing polysomnography (PSG) measurements of non-critically ill children. During model development, the decision criteria for the different classes were jointly optimised. To evaluate whether the models were generalisable to the PICU population, external validation was performed on a set of 8 of PICU patients. Results: For each number of sleep stages, the three models performed similarly. However, there was an increase in performance with a decrease in the number of sleep stages with balanced accuracies varying between 0.70 and 0.72 in two-class staging and between 0.41 and 0.42 in five-class staging. External validation on the PICU dataset showed a markedly worse performance for all three models with balanced accuracies varying between 0.56 and 0.62 in two-class staging and between 0.22 and 0.23 in five-class staging.Conclusion: Machine learning models for sleep classification in children based on vital signs have been developed and show promising results. Nonetheless, the developed models are not generalisable to the PICU population. Further research is recommended with a focus on improving the models such that they can be applied in the PICU. Combining information extracted from vital signs with EEG signal and developing the models directly on PICU data should be considered to improve the performance and thereby contribute to personalised care and minimising sleep disturbances. Technical Medicin