Using Electroencephalogram Biosignal Changes for Delirium Detection in Intensive Care Units
- PMID: 35234185
- DOI: 10.1097/JNN.0000000000000639
Using Electroencephalogram Biosignal Changes for Delirium Detection in Intensive Care Units
Abstract
BACKGROUND: Biosignal data acquired during quantitative electroencephalography (QEEG) research may ultimately be used to develop algorithms for more accurate detection of delirium. This study investigates the biosignal changes during delirium states by using the QEEG data of patients in a medical intensive care unit. METHODS: This observational study was conducted between September 2018 and December 2019 at a tertiary hospital in South Korea. Delirium was measured using the Korean version of Confusion Assessment Method for the Intensive Care Unit in intensive care unit patients. Quantitative EEG measurements were recorded for 20 minutes in a natural state without external treatment or stimuli, and QEEG data measured in the centroparietal and parietal regions with eyes open were selected for analysis. Power spectrum analysis with a 5-minute epoch was conducted on the selected 65 cases. RESULTS: QEEG changes in the presence of delirium indicated that alpha, beta, gamma, and spectral edge frequency 50% waves showed significantly lower absolute power spectra than the corresponding findings in the absence of delirium. Brain-mapping results showed that these brain waves were inactivated in delirious states. CONCLUSION: QEEG assessments can potentially detect the changes in the centroparietal and parietal regions of delirium patients. QEEG changes, including lower power spectra of alpha, beta, and gamma waves, and spectral edge frequency 50%, can be successfully used to distinguish delirium from the absence of delirium.
Copyright © 2022 American Association of Neuroscience Nurses.
Conflict of interest statement
The authors declare no conflicts of interest
Comment in
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Acute Delirium and Transcranial Photobiomodulation.Photobiomodul Photomed Laser Surg. 2023 Dec;41(12):661-662. doi: 10.1089/photob.2023.0143. Epub 2023 Nov 28. Photobiomodul Photomed Laser Surg. 2023. PMID: 38016154 No abstract available.
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