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

Andrew Hunter; Barry Crouch; Nigel Webster; Bettina Platt; Andrew Hunter; Barry Crouch; Nigel Webster; Bettina Platt

doi:10.3934/Neuroscience.2020001

AIMS Neuroscience

2020, Volume 7, Issue 1: 1-16. doi: 10.3934/Neuroscience.2020001

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

Institute of Medical Sciences, The University of Aberdeen, Aberdeen, UK

Received: 15 October 2019 Accepted: 05 January 2020 Published: 13 January 2020

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.
- delirium,
- ICU,
- QEEG,
- EEG,
- Granger causality,
- coherence,
- connectivity
Citation: Andrew Hunter, Barry Crouch, Nigel Webster, Bettina Platt. Delirium screening in the intensive care unit using emerging QEEG techniques: A pilot study[J]. AIMS Neuroscience, 2020, 7(1): 1-16. doi: 10.3934/Neuroscience.2020001

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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.

Abbreviation ARI: Aberdeen Royal Infirmary; ANOVA: Analysis of Variance; AR: Auto-regression/Auto-regressive; CAM-ICU: Confusion Assessment Method for the Intensive Care Unit; dPLI: Directed Phase Lag Index; DSM-V: Diagnostic and Statistical Manual of Mental Disorders–Version 5; EEG: Electroencephalography; ICU: Intensive Care Unit; NHS: National Health Service; NICE: National Institute for Health and Care Excellence; PTE: Phase Transfer Entropy; QEEG: Quantitative Electroencephalography; RASS: Richmond Agitation-Sedation Score; rPDC: Renormalized Partial Directed Coherence; REC: Research Ethics Committee; SEM: Standard Error of Mean; SFPR: Slow to Fast Frequency Power Ratio;
Acknowledgments

This work was supported by an Alzheimer's Society grant (project grant number AS-PG-14-039 to BP) and a British Journal of Anaesthesia / Royal College of Anaesthetists funded John Snow Anaesthesia iBSc Award (to AH).

Conflicts of interest

All authors declare no conflicts of interest in this paper.

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