ArticleDimensions of critical care nursing : DCCN
Rapid Handheld Continuous Electroencephalogram (EEG) Has the Potential to Detect Delirium in Older Adults.
Article in Dimensions of critical care nursing : DCCN. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed, 8 citations in OpenAlex.
- Predicting delirium in critically Ill COVID-19 patients using EEG-derived data: a machine learning approach.GeroScience · 2026Observational
- Point-of-care electroencephalography for prediction of postoperative delirium in older adults undergoing elective surgery: protocol for a prospective cohort study.Biology methods & protocols · 2026Review
- Bedside EEG for rapid diagnosis of delirium.Neurotherapeutics : the journal of the American Society for Experimental NeuroTherapeutics · 2026Review
- Supervised deep learning with vision transformer predicts delirium using limited lead EEG.Scientific reports · 2023Article
- Delirium detection using GAMMA wave and machine learning: A pilot study.Research in nursing & health · 2022Article
Corrections and comments
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Authors and funding
10 authors at 2 institutions in 2 countries.
Funding
Abstract
backgroundDelirium-related biochemical derangements lead to electrical changes that can be detected in electroencephalographic (EEG) patterns followed by behavioral signs and symptoms. Studies using limited lead EEG show a large difference between patients with and without delirium while discriminating delirium from other causes. Handheld rapid EEG devices may be capable of detecting delirium before symptom onset, thus providing an objective physiological method to detect delirium when it is most amenable to interventions.
objectiveThe aim of this study was to explore the potential for rapid EEG to detect waveform pattern changes consistent with delirium status.
methodsThis prospective exploratory pilot study used a correlational design and mixed models to explore the relationships between handheld portable EEG data and delirium status.
resultsWhile being under powered minimized opportunities to detect statistical differences in EEG-derived ratios using spectral density analysis, sleep-to-wake ratios tended to be higher in patients with delirium.
conclusionsLimited lead EEG may be useful in predicting adverse outcomes and risk for delirium in older critically ill patients. Although this population is at the highest risk for mortality, delirium is not easily identified by current clinical assessments. Therefore, further investigation of limited lead EEG for delirium detection is warranted.
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Registered trials
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