University of Twente Student Theses
Amplitude integrated electroencephalography analysis of sleep architecture in mechanically ventilated children
Steen, Douwe van der (2021) Amplitude integrated electroencephalography analysis of sleep architecture in mechanically ventilated children.
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| Full Text Status: | Access to this publication is restricted |
| Embargo date: | 12 October 2024 |
| Abstract: | Abstract Objective First, characterise aEEG background patterns and sleep-wake cycling during mechanical ventilation in a heterogeneous group of critically ill children. Second, to create a simple detection algorithm for the presence of sleep-wake cycles in a three hour period. Design Secondary analysis of physiology data. Setting Tertiary paediatric intensive care unit in a university hospital. Patients Mechanically ventilated children < 18 years of age in whom the bedside team indicated aEEG monitoring, i.e., established or suspected epileptic activity, depressed level of consciousness, hypoxic-ischemic encephalopathy, (postoperative) congenital heart disease, extracorporeal membrane oxygenation, significant neurological malformations, meningitis, and use of continuous neuromuscular blockade. Measurements and Main Results Three-hour sections of aEEG during the night were used for analysis. A total of 115 patients were included, this resulted in 617 aEEG sections. The sections were classified into different background patterns and on the presence of sleep-wake cycling. The results showed a mix of CNV and DNV in the 0-1 month group, but predominantly CNV in the older age groups. The amplitude of the CNV upper and lower bands showed an increasing trend with increasing age. An increase in ventilation days was correlated with lower aEEG amplitudes. SWC were seen in all age groups, however, after two years of age, a sudden drop in SWC occurrence was observed. The detection algorithm, which used two outcome variables (no SWC or some form of SWC), had an accuracy of 87.7%. Conclusion The amplitude of the aEEG bands increases with age, and SWC are commonly seen until two years. The automated detection of SWC is promising and could be helpful in further research. |
| Item Type: | Essay (Master) |
| Faculty: | TNW: Science and Technology |
| Subject: | 44 medicine |
| Programme: | Technical Medicine MSc (60033) |
| Link to this item: | https://purl.utwente.nl/essays/89247 |
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