Forskning
Udskriv Udskriv
Switch language
Region Hovedstaden - en del af Københavns Universitetshospital
Udgivet

Automatic continuous EEG signal analysis for diagnosis of delirium in patients with sepsis

Publikation: Bidrag til tidsskriftTidsskriftartikelForskningpeer review

  1. Myelin protein zero gene dose dependent axonal ion-channel dysfunction in a family with Charcot-Marie-Tooth disease

    Publikation: Bidrag til tidsskriftTidsskriftartikelForskningpeer review

  2. Automatic detection of cortical arousals in sleep and their contribution to daytime sleepiness

    Publikation: Bidrag til tidsskriftTidsskriftartikelForskningpeer review

  3. Steady-state visual evoked potential temporal dynamics reveal correlates of cognitive decline

    Publikation: Bidrag til tidsskriftTidsskriftartikelForskningpeer review

  1. Prediction of survival in amyotrophic lateral sclerosis: a nationwide, Danish cohort study

    Publikation: Bidrag til tidsskriftTidsskriftartikelForskningpeer review

  2. Cognitive training for prevention of cognitive impairment in adult intensive care unit (ICU) patients

    Publikation: Bidrag til tidsskriftTidsskriftartikelForskningpeer review

Vis graf over relationer

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 analysis is a useful supplement to visual analysis, and provides additional cEEG diagnostic classifiers.

SIGNIFICANCE: Automatic cEEG analysis provides useful information in septic patients.

OriginalsprogEngelsk
TidsskriftClinical Neurophysiology
Vol/bind132
Udgave nummer9
Sider (fra-til)2075-2082
Antal sider8
ISSN1388-2457
DOI
StatusUdgivet - sep. 2021

ID: 66411548