title: Delirium detection using EEG: what and how to measure.
abstract: BACKGROUND
  Despite its frequency and impact, delirium is poorly recognized in postoperative and critically ill patients. EEG is highly sensitive to delirium but, as currently used, it is not diagnostic. To develop an EEG-based tool for delirium detection with a limited number of electrodes, we determined the optimal electrode derivation and EEG characteristic to discriminate delirium from nondelirium.
  
  
  METHODS
  Standard EEGs were recorded in 28 patients with delirium and 28 age- and sex-matched patients who had undergone cardiothoracic surgery and were not delirious, as classified by experts using Diagnostic and Statistical Manual of Mental Disorders, 4th edition, criteria. The first minute of artifact-free EEG data with eyes closed as well as with eyes open was selected. For each derivation, six EEG parameters were evaluated. Using Mann-Whitney U tests, all combinations of derivations and parameters were compared between patients with delirium and those without. Corresponding P values, corrected for multiple testing, were ranked.
  
  
  RESULTS
  The largest difference between patients with and without delirium and highest area under the receiver operating curve (0.99; 95% CI, 0.97-1.00) was found during the eyes-closed periods of the EEG, using electrode derivation F8-Pz (frontal-parietal) and relative δ power (median [interquartile range (IQR)] for delirium, 0.59 [IQR, 0.47-0.71] and for nondelirium, 0.20 [IQR, 0.17-0.26]; P = .0000000000018). With a cutoff value of 0.37, it resulted in a sensitivity of 100% (95% CI, 100%-100%) and specificity of 96% (95% CI, 88%-100%).
  
  
  CONCLUSIONS
  In a homogenous population of nonsedated patients who had undergone cardiothoracic surgery, we observed that relative δ power from an eyes-closed EEG recording with only two electrodes in a frontal-parietal derivation can distinguish among patients who have delirium and those who do not.
authors.affiliation: Department of Intensive Care Medicine, Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht, The Netherlands. Electronic address: a.w.vanderkooi@umcutrecht.nl.
authors.forename: Arendina W
authors.initials: AW
authors.lastname: van der Kooi
authors.affiliation: Department of Intensive Care Medicine, Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht, The Netherlands.
authors.forename: Irene J
authors.initials: IJ
authors.lastname: Zaal
authors.affiliation: Department of Psychiatry, Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht, The Netherlands.
authors.forename: Francina A
authors.initials: FA
authors.lastname: Klijn
authors.affiliation: Department of Geriatrics, Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht, The Netherlands.
authors.forename: Huiberdina L
authors.initials: HL
authors.lastname: Koek
authors.affiliation: Department of Cardiothoracic Surgery, Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht, The Netherlands.
authors.forename: Ronald C
authors.initials: RC
authors.lastname: Meijer
authors.affiliation: Department of Neurology and Neurosurgery, Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht, The Netherlands.
authors.forename: Frans S
authors.initials: FS
authors.lastname: Leijten
authors.affiliation: Department of Intensive Care Medicine, Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht, The Netherlands.
authors.forename: Arjen J
authors.initials: AJ
authors.lastname: Slooter
mesh_terms: D000368:Aged; D016638:Critical Illness; D003693:Delirium / Q000175:diagnosis*; D003937:Diagnosis, Differential; D004569:Electroencephalography / Q000379:methods*; D005260:Female; D005500:Follow-Up Studies; D006801:Humans; D008297:Male; D012372:ROC Curve; D015203:Reproducibility of Results