ArticleAIMS neuroscience2020
Delirium screening in the intensive care unit using emerging QEEG techniques: A pilot study.
Article in AIMS neuroscience, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Electroencephalography of mechanically ventilated patients at high risk of delirium.Acta neurologica Scandinavica · 2021Trial
- Bedside EEG for rapid diagnosis of delirium.Neurotherapeutics : the journal of the American Society for Experimental NeuroTherapeutics · 2026Review
- The Presence/Absence of an Awake-State Dominant EEG Rhythm in Delirious Patients Is Related to Different Symptoms of Delirium Evaluated by the Intensive Care Delirium Screening Checklist (ICDSC).Sensors (Basel, Switzerland) · 2024Article
- Increased A1 astrocyte activation-driven hippocampal neural network abnormality mediates delirium-like behavior in aged mice undergoing cardiac surgery.Aging cell · 2024Article
- A Dual-Camera Eye-Tracking Platform for Rapid Real-Time Diagnosis of Acute Delirium: A Pilot Study.IEEE journal of translational engineering in health and medicine · 2024Article
- Supervised deep learning with vision transformer predicts delirium using limited lead EEG.Scientific reports · 2023Article
- Processed Electroencephalogram-Based Monitoring to Guide Sedation in Critically Ill Adult Patients: Recommendations from an International Expert Panel-Based Consensus.Neurocritical care · 2023Article
- Electroencephalogram measured functional connectivity for delirium detection: a systematic review.Frontiers in neuroscience · 2023Review
- Delirium detection using GAMMA wave and machine learning: A pilot study.Research in nursing & health · 2022Article
- Recent applications of quantitative electroencephalography in adult intensive care units: a comprehensive review.Journal of neurology · 2022Review
- Electroencephalography in delirium assessment: a scoping review.BMC neurology · 2022Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
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