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Automatic continuous EEG signal analysis for diagnosis of delirium in patients with sepsis by Olalla Urdanibia-Centelles; Rikke M. Nielsen; Egill Rostrup; Esben Vedel-Larsen; Kirsten Thomsen; Miki Nikolic; Birger Johnsen; Kirsten Møller; Martin Lauritzen; Krisztina Benedek is a Neuroscience article available to read on EtoBox.
What is Automatic continuous EEG signal analysis for diagnosis of delirium in patients with sepsis about?
## Objective: In critical care, continuous eeg (ceeg) monitoring is useful for delirium diagnosis. although visual ceeg analysis is most commonly used, automatic ceeg analysis has shown promising results in small samples. here we aimed to compare visual versus automatic ceeg analysis for delirium diagnosis in septic patients. ## Methods: We obtained ceeg recordings from 102 septic patients who were scored for delirium six times daily. a total of 1252 ceeg blocks were visually analyzed, of which 805 blocks were also automatically analyzed. ## Results: Automatic ceeg analyses revealed that delirium was associated with 1) high mean global field power (p < 0.005), mainly driven by delta activity; 2) low average coherence across all electrode pairs and all frequencies (p < 0.01); 3) lack of intrahemispheric (fronto-temporal and temporo-occipital regions) and interhemispheric coherence (p < 0.05); and 4) lack of ceeg reactivity (p < 0.005). classification accuracy was assessed by receiver operating characteristic (roc) curve analysis, revealing a slightly higher area under the curve for visual analysis (0.88) than automatic analysis (0.74) (p < 0.05). ## Conclusions: Automatic ceeg analy
Who reads Automatic continuous EEG signal analysis for diagnosis of delirium in patients with sepsis?
It is typically read by researchers, students, and practitioners in Neuroscience.
- Author
- Olalla Urdanibia-Centelles; Rikke M. Nielsen; Egill Rostrup; Esben Vedel-Larsen; Kirsten Thomsen; Miki Nikolic; Birger Johnsen; Kirsten Møller; Martin Lauritzen; Krisztina Benedek
- Publisher
- Elsevier BV
- Published
- 2021
- Language
- EN
- Field
- Neuroscience (Life Sciences)