Delirium screening in the intensive care unit using emerging QEEG techniques: A pilot study

Andrew Hunter, Barry Crouch, Nigel Webster, Bettina Platt* (Corresponding Author)

*Corresponding author for this work

Research output: Contribution to journalArticle

6 Downloads (Pure)

Abstract

Delirium is an under-diagnosed yet frequently occurring clinical complication with potentially serious consequences for intensive care unit (ICU) patients. Diagnosis is currently reactive and based upon qualitative assessment of the patient’s cognitive status by ICU staff. Here, we conducted a preliminary investigation into whether emerging quantitative electroencephalography (QEEG) analysis techniques can accurately discriminate between delirious and non-delirious patients in an ICU setting. Resting EEG recordings from 5 ICU patients in a state of delirium and 5 age matched control patients were analyzed using autoregressive spectral estimation for quantification of EEG power and renormalized partial directed coherence for analysis of directed functional connectivity. Delirious subjects exhibited pronounced EEG slowing as well as severe general loss of directed functional connectivity between recording sites. Distinction between groups based on these parameters was surprisingly clear given the low sample size employed. Furthermore, by targeting the electrode positions where effects were most apparent it was possible to clearly segregate patients using only 3 scalp electrodes. These findings indicate that quantitative diagnosis and monitoring of delirium is not only possible using emerging QEEG methods but is also accomplishable using very low-density electrode systems.
Original languageEnglish
Pages (from-to)1-16
Number of pages16
JournalAIMS Neuroscience
Volume7
Issue number1
DOIs
Publication statusPublished - 13 Jan 2020

Keywords

  • delirium
  • ICU
  • QEEG
  • EEG
  • Granger causality
  • coherence
  • connectivity

Fingerprint Dive into the research topics of 'Delirium screening in the intensive care unit using emerging QEEG techniques: A pilot study'. Together they form a unique fingerprint.

  • Cite this