ArticleResearch in nursing & health2022
Delirium detection using GAMMA wave and machine learning: A pilot study.
Article in Research in nursing & health, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Who cites it
4 citing papers in PubMed, 6 citations in OpenAlex.
- Predicting delirium in critically Ill COVID-19 patients using EEG-derived data: a machine learning approach.GeroScience · 2026Observational
- Bedside EEG for rapid diagnosis of delirium.Neurotherapeutics : the journal of the American Society for Experimental NeuroTherapeutics · 2026Review
- Intraoperative 40 Hz gamma frequency auditory stimulation for postoperative sleep disturbance in patients undergoing laparoscopic gynecological surgery: protocol for a randomized controlled trial.Frontiers in medicine · 2026Article
- Point-of-Care Electroencephalography in Acute Neurological Care: A Narrative Review.Neurology international · 2025Review
Corrections and comments
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Authors and funding
5 authors at 3 institutions in 1 country.
Funding
Abstract
Delirium occurs in as many as 80% of critically ill older adults and is associated with increased long-term cognitive impairment, institutionalization, and mortality. Less than half of delirium cases are identified using currently available subjective assessment tools. Electroencephalogram (EEG) has been identified as a reliable objective measure but has not been feasible. This study was a prospective pilot proof-of-concept study, to examine the use of machine learning methods evaluating the use of gamma band to predict delirium from EEG data derived from a limited lead rapid response handheld device. Data from 13 critically ill participants aged 50 or older requiring mechanical ventilation for more than 12 h were enrolled. Across the three models, accuracy of predicting delirium was 70 or greater. Stepwise discriminant analysis provided the best overall method. While additional research is needed to determine the best cut points and efficacy, use of a handheld limited lead rapid response EEG device capable of monitoring all five cerebral lobes of the brain for predicting delirium hold promise.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.