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Psychiatry Investig > Volume 20(2); 2023 > Article
Kim, Bong, Lee, Choi, Yoon, and Kim: The Usefulness of Quantitative Electroencephalography in Diagnosis and Severity Evaluation of Delirium: A Retrospective Study



Incontrovertible disease markers are absent in delirium. This study investigated the usefulness of quantitative electroencephalography (qEEG) in diagnosing delirium.


This retrospective case-control study reviewed medical records and qEEG data of 69 age/sex-matched patients (delirium group, n=30; control group, n=39). The first minute of artifact-free EEG data with eyes closed was selected. Nineteen electrodes’ sensitivity, specificity, and correlation with delirium rating scale-revised-98 were analyzed.


On comparing the means of absolute power by frontal, central, and posterior regions, the delta and theta powers showed significant differences (p<0.001) in all regions, and the magnitude of the absolute power was higher in the delirium group than in the control group; only the posterior region showed a significant (p<0.001) difference in beta power. The spectral power of theta at the frontal region (area under the curve [AUC]=0.84) and theta at the central and posterior regions (AUC=0.83) showed 90% sensitivity and 79% specificity, respectively, in differentiating delirious patients and controls. The beta power of the central region showed a significant negative correlation with delirium severity (R=-0.457, p=0.011).


Power spectrum analysis of qEEG showed high accuracy in screening delirium among patients. The study suggests qEEG as a potential aid in diagnosing delirium.


Delirium is a psychiatric disorder with disturbances in memory, learning, orientation, language, and perception, and is caused by medical conditions, drugs, and substance abuse, showing one or more symptoms among defective memory and learning, disorientation, language issues, and perceptual distortion [1]. In particular, it worsens at night with the accompanying decrease in external directional stimuli [1]. Though the pathophysiology of delirium is not well understood, the overall reduction of oxidative metabolic processes in the brain, enzymatic system, blood-brain barrier, cell membrane damage, acetylcholine synthesis, and consequent deficiency in choline, dopamine, and gamma-aminobutyric have been suggested as contributing factors. Acute brain failure is reportedly caused by a series of processes, such as an imbalance in neurotransmitters (gamma-aminobutyric acid, glutamate, serotonin, and norepinephrine), cortisol elevation due to acute stress response, and cytokine changes [2].
An estimated 70% of patients hospitalized for physical illness have delirium, and approximately 30% of elderly patients experience delirium during hospitalization [3,4]. The incidence of delirium increases with age and disease severity of the patients. Specifically, the risk of delirium was found to increase after surgical treatment in elderly patients and is closely related to complex surgical procedures, such as hip fracture and heart surgery [5-7]. Although delirium is reversible, in many cases, cognitive impairment persists, with an associated increase in medical costs and length of hospital stay [8-10]. Therefore, it is necessary to diagnose delirium at the earliest and intervene actively. Currently, delirium is screened based on history and physical examination performed by caregivers and medical staff; it solely depends on the skill and experience of the medical staff. As a result, only 28% of delirium patients admitted to the intensive care unit (ICU) were diagnosed by doctors and 34.8% by nurses; approximately 40% of patients who were believed to have depression and were referred by their physician to the Department of Psychiatry were diagnosed as having delirium [6,11]. To overcome this issue, scales, such as Confusion Assessment Method (CAM) and CAM-ICU, are used to diagnose delirium in clinical practice. However, a meta-analysis showed the sensitivity of CAM and CAM-ICU to be 82% and 81%, respectively; depending on the research environment and clinical situation, sensitivity decreased to 42.9% and 46.7%, respectively. Since these scales are used by clinicians, their accuracy inevitably varies according to factors, such as the aforementioned skill and experience [12-15]. As the diagnostic process is closely related to the clinical skill and experience of the medical staff, currently, it is difficult to intervene with just a preliminary diagnosis.
Recently, the US has focused on overcoming the drawbacks of individual approaches by developing objective disease markers, or biomarkers, in precision medical planning [16]. However, to date, no biomarker has been used to diagnose delirium, and a standardized approach has not been established [17,18]. Brain imaging techniques, such as computed tomography and magnetic resonance imaging, have limited use as biomarkers. Although a single nucleotide polymorphism of 16 genes has been detected, diagnosis involving genetic testing is still in its preliminary stage [19-23].
Meanwhile, electroencephalography (EEG) is one of the most promising tools as a diagnostic biomarker that can help to increase the diagnostic accuracy in delirium [24]. EEG provides data to support delirium diagnosis even when it is difficult to interview the patient due to symptoms, such as decreased attention span and decreased arousal [25]. A prospective cohort study among delirium patients by Kimchi et al. [26] demonstrated the usefulness of EEG delirium diagnosis and severity assessment. On performing EEG on 121 patients, those in the target group showed generalized theta or delta slowing. The degree of slowing was also correlated with the severity of the CAM scale. Recently, quantitative EEG (qEEG) has been in the spotlight as a useful tool for diagnosing delirium [24]. In qEEG, digitally measured EEG is processed using a computer and then converted into amounts of specific frequencies, resulting in power spectrum values across multiple bands [27-29]. In a recent qEEG study of 28 patients with delirium by van der Kooi et al. [24], the change in delta power of F8-Pz demonstrated superiority with sensitivity and specificity of 100% and 96%, respectively. To search for more objective indicators for diagnosing delirium, they investigated whether qEEG had diagnostic usefulness in delirium and the relationship between qEEG and the severity of delirium. The aforementioned studies were designed to compare a well-controlled group with a delirium group. However, in real-world situations, it is difficult to obtain a well-controlled group. As mentioned earlier, 40% of those who were referred to the Department of Psychiatry for depression had delirium; similarly, in real-world settings, patients with delirium have to be screened in a heterogeneous group [11]. We hypothesized that the present study would be more useful for diagnosing delirium and evaluating its severity than previous studies if the delirium and control groups were set up and compared using qEEG data collected from various patients in real-world clinical situations. Therefore, we conducted this study to explore whether qEEG had diagnostic usefulness in exploring the objective indicators of delirium in a heterogeneous group by reflecting on a real-world situation; further, the relationship between qEEG and the severity of delirium was also investigated.


Data and participants

This study analyzed the characteristics of qEEG in patients with delirium. Sixty-nine participants were selected from patients who were referred to the Department of Psychiatry, Catholic University of Korea, Daegu, between January 01, 2018 and April 25, 2021, and a retrospective study was conducted using their medical records. Individuals referred to the Department of Psychiatry due to suspected delirium were included in the delirium group, and those who were referred due to other psychiatric disorders were included in the non-delirium group. The inclusion criteria for the delirium group were age ≥19 years and available records of EEG, clinical global impression-severity (CGI-S) rating scale, and delirium rating scale-revised-98 (DRS-R-98) rating scale. The groups were matched according to sex and age. Exclusion criteria for both groups included any history of neurological disorders, such as epilepsy, dementia or intracranial surgery, insertion of magnetic material into the head or eyeball, difficulty in communication (e.g., coma, intubation), or EEG data judged to be inappropriate for use in the study. In addition, drug history was evaluated before qEEG imaging, and only drug-naive subjects were included in the non-delirium control group. This study was approved by the Institutional Review Board of the Catholic University of Korea, Daegu (approval no: CR-21-090). The need for informed consent was waived due to the retrospective nature of the study.

Study design

From among the total number of participants, 30 patients diagnosed with delirium using the Diagnostic and Statistical Manual of Mental Disorders fifth edition (DSM-5) diagnostic criteria by a psychiatric specialist were classified as the delirium group, and 39 patients who were not diagnosed with delirium were classified as the non-delirium control group. Electronic medical records of all patients were analyzed, and data on sex, age, and delirium status were collected. In the delirium group, we analyzed the records of tests performed, such as the DRS-R-98 scale used to evaluate the symptoms and severity of delirium and the CGI-S. Finally, EEG data were retrieved and analyzed. This study followed the EQUATOR reporting guidelines for this type of study (RECORD-checklist).



The CGI-S is a widely used simple assessment tool in psychiatry. It is a clinician-administered scale and focuses on three aspects: the severity of disease, the pattern of improvement or change, and the evaluation of treatment responsiveness. It is a measure to evaluate the overall disease state, and clinicians can easily and quickly assess the patient and reflect it in treatment. In the present study, CGI-S was used to evaluate the severity of the disease [30].


The DRS-R-98 scale consists of 16 items, including three diagnostic items for initial evaluation and 13 severity items for evaluating severity concerning the differential diagnosis. Diagnostic items assess the time course of symptom onset, symptom variability, and the presence of physical diseases affecting symptoms. The severity items evaluate perceptual disturbance, delusion, affective variability, thought process, thought content, language, psychomotor delay or agitation, orientation, memory, attention, short-term or long-term memory, spatiotemporal ability, and degree of perceptual impairment. Severity items were scored 0-3 points, and diagnostic items were scored 0-3 or 0-2, with a total score of a maximum of 39 and 46 points, respectively [31]. The Cronbach’s alpha coefficient was 0.90 for the total scale and 0.87 for the severity scale [31]. In this study, a Korean-translated version of the scale (DRS-R-98-K) was used; Cronbach’s alpha coefficient was 0.91 for the total scale and 0.89 for the severity scale [32].


EEG recording and pre processing

The EEG was performed with 19 channels of the international 10-20 system (Fp1, Fp2, F7, F3, Fz, F4, F8, T7, C3, Cz, C4, T8, P7, P3, Pz, P4, P8, O1, and O2), using a 64-channel Comet digital EEG (Grass; Natus neurology, Middleton, WI, USA), with a recording frequency of 800 Hz with reference to the ear electrode. The progress of the qEEG was measured for 5 minutes with the eyes open while sitting in a comfortable chair and immediately after that for 5 minutes with the eyes closed. The patient was instructed to focus on the “+” sign in front in the eyes-open condition, avoid movement, remain silent, and not fall asleep when the eyes were closed.

Method of EEG analysis

For the analysis of EEG, MATLAB 7.0.1 (MathWorks, Natick, MA, USA) and EEGLAB toolbox ( were used. First, down-sampling of EEG data to 250 Hz, detrending, and mean-subtracting were performed to remove the direct current component. Subsequently, frequencies less than 1 Hz and 60 Hz affected by eye movements and electrical noise were removed through the filter, and artifacts caused by blinking, muscle movement, and heart noise were removed through independent component analysis. Power spectrum analysis was performed on the EEG data for at least 2 minutes without artifacts. Finally, we performed EEG analysis using the fast Fourier transforms algorithm for each frequency band for each selected epoch: delta wave (1-4 Hz), theta wave (4-8 Hz), alpha wave (8-12 Hz), beta wave (12-30 Hz), gamma wave (>30 Hz). All EEG results were reconfirmed by psychiatric clinicians and EEG experts for error and noise.

Statistical analysis

The χ2 test was used to analyze the categorical sociodemographic variables between the two groups (delirium and non-delirium control groups), and the Student’s t-test was used to compare continuous variables. For analyzing EEG data in the eyes-closed condition, frontal (FP1, F3, F7, Fz, FP2, F4, and F8), central (T3, C3, Cz, T4, and C4), and posterior (T5, P3, O1, Pz, T6, P4, and O2) channels were classified into three regions, and then the differences between groups were analyzed using the Student’s t-test [33]. The diagnostic performance and accuracy of qEEG were analyzed using the receiver operating characteristic (ROC) curve for the sub-groups that showed a significant difference between the groups in the EEG analysis; the effectiveness of qEEG was evaluated by analyzing the sensitivity and specificity between the two groups. Accuracy was measured in the lower area under the curve (AUC) of the ROC curve; the AUC value is considered an excellent tool for diagnostic accuracy evaluation when the ROC curve is close to 1 [34]. Finally, Pearson’s correlation analysis was performed to examine the correlation between the severity of delirium and EEG data in the delirium group. Bonferroni correction was performed to control false positives in multiple comparisons, and statistical significance was set at p<0.017 (0.05/3). All analyzes were performed using the statistical program SPSS Version 25.0 for Windows (IBM Corp., Armonk, NY, USA).


Demographic and clinical characteristics

There were 30 participants in the delirium group and 39 in the control group. The diseases in the control group included depressive disorders (n=16), anxiety-related disorders (n=11), sleep-related disorders (n=6), somatic symptoms and related disorders (n=4), and trauma and stress-related disorders (n=2). There were no statistically significant differences in age (p=0.066) and sex (p=0.387) between the delirium and the control groups. In addition, in the inclusion criteria of this study, the minimum age was 19 years, but the actual age of the included participants was ≥49 years; therefore, there was no statistical error due to the young age (Table 1 and Figure 1).

Comparison of scores on clinical scales between delirium and control groups

The CGI-S (p<0.001) scores showed a significant difference between the two groups, and the overall clinical severity was higher in the delirium group (mean 5.30, standard deviation [SD] ±0.70) than in the control group (mean 2.92, SD ±0.74). The DRS-R-98 was administered only when delirium was clinically suspected; therefore, between-group analysis with the control group was not possible. The mean score on the severity scale of the DRS-R-98 in the delirium group was 20.90 points (SD ±9.92), and the mean total score in DRS-R-98 was 26.73 points (SD ±11.02) (Table 1).

Comparison of qEEG between the delirium and control groups

On comparing the average absolute power for the frontal, central, and posterior regions, a significant difference was found in delta and theta frequencies in all regions, and the absolute power was increased in the delirium group than in the control group. In contrast, beta power was decreased in all areas, especially the greatest decrease was observed in the posterior region. There was no significant difference in the alpha and gamma frequencies (Table 2).

Sensitivity and specificity of qEEG data using ROC curves

When the cut-off was set to 1.0 SD on dividing according to frontal, central, and posterior regions, the theta power of the frontal region showed the highest AUC (0.84) value, and the sensitivity and specificity were 77% and 79%, respectively. Additionally, the theta power in the central and posterior regions also showed an AUC of ≥0.8. In particular, the sensitivity of the theta power was highest in the posterior region (90%), while the delta power in the central region had the highest specificity (95%) (Figure 2).

Correlation of delirium severity with qEEG

Pearson’s correlation analysis was performed on the EEG data of the delirium group and the scores on the DRS-R-98 scale. At p<0.017 (0.05/3), defined by Bonferroni correction, the only significant correlation with delirium severity was the central beta power (R=-0.457, p=0.011) (Figure 3).


In this study, qEEG was found to have a high diagnostic value in identifying delirium among patients referred to the department of psychiatry. We recorded the patient’s EEG in the eyes-closed resting state for 5 minutes and acquired the absolute power through power spectrum analysis. In the delirium group, it was confirmed that the absolute power increased in slow waves, such as theta and delta waves, while it decreased in the beta wave. Moreover, as a channel parameter of the regions, the theta wave in the frontal region showed the highest accuracy (sensitivity, 77%; specificity, 79%).
Many studies have compared differences in EEG characteristics between patients with delirium and normal controls. Generalized slowing of theta and delta waves [26,35], an increase in absolute power [24,36], and a decrease in functional connectivity in the frontal region [7] have been reported. Excessive slowing on EEG in delirium is a phenomenon occurring not only when the level of consciousness is lowered but also at a normal level of consciousness, in relation to poor cognitive function, poor progress, and prognosis [26,35]. Furthermore, in investigating the usefulness of EEG-based diagnosis of delirium, van der Kooi et al. [24] reported that the relative delta power had high accuracy, with an AUC of 0.99 in the F8-Pz channel. Fleischmann et al. [37] reported 100% sensitivity and 99% specificity by combining 2 Hz (delta wave region) of F3-P4 and 19 Hz (beta wave region) of C3-O1 based on a large-scale EEG database.
Among the patients with delirium referred to the department of psychiatry, we observed an increase in slow wave and a decrease in fast wave (beta wave), which is consistent with previous studies [35,38]. Theta and delta waves appear as slow waves across a large area of the brain and have oscillators in structures below the cortex [39], such as the brainstem [40], thalamus [41], and hippocampus [42]. This increase in slow wave increase is commonly seen with lowered consciousness or encephalopathy [43], known to cause hypoarousal of the brain, and is a frequent phenomenon in neurodegenerative diseases, such as Alzheimer’s and Parkinson’s diseases [44]. In contrast, beta waves are classified as fast on EEG, with oscillators primarily in the cerebral cortex [45]. A decrease in beta waves may reflect hypoarousal of the cerebral cortex and is common in traumatic brain injury along with symptoms, such as cognitive decline and concentration loss [46]. Therefore, the results of this study suggest that the decrease in cerebral cortex activity in patients with delirium causes thalamocortical dysrhythmia [47], with an increase in slow waves, such as theta and delta waves.
Notably, the sensitivity and specificity of the absolute theta power in the frontal and occipital regions, which may be potent biological markers of delirium, were relatively low (frontal: sensitivity 77%, specificity 79%; posterior: sensitivity 90%, specificity 67%) in the present study than in previous studies. van der Kooi et al. [24] reported 100% sensitivity and 96% specificity using the relative delta power of the F8-Pz (frontal-parietal) electrode, and Fleischmann et al. [37] reported that the F3-C4 electrode had 100% sensitivity and 90.98% specificity using relative delta power. The present results may partially depend on the selection method and heterogeneity of the control group; previous studies were conducted using the data of patients without delirium from among patients diagnosed with the same disease or undergoing the same type of surgery, or the general population [24,37]. However, in the present study, patients diagnosed with disorders other than delirium by a psychiatrist were included in the control group. The patients enrolled had various medical conditions, including depressive disorder (41.0%), anxiety disorder (28.2%), sleep disorders (15.4%), somatic symptoms and related disorders (10.3%), and trauma and stress-related disorders (5.1%). In previous studies with heterogeneous control groups, sensitivity and specificity were slightly low. For example, Kimchi et al. [26] reported that the generalized slowing of EEG had a sensitivity of 83.5% and a specificity of 67.1% in patients with delirium having various internal and external disorders. However, this should be considered from a more realistic perspective; EEG data used in this study were obtained for clinical use in a real-world setting. In clinical settings, delirium has to be screened in significantly heterogeneous groups, not in well-controlled groups. Many of the patients referred to the department of psychiatry had delirium [6]. As such, it is very difficult to distinguish delirium in patients with co-existing psychiatric symptoms in a clinical setting. The significance of this study is that the method demonstrated can secure a fairly high level of accuracy with the assistance of qEEG.
Conversely, it was reported that the severity of delirium had a significant negative correlation with the absolute beta power of the central region. According to previous studies, the severity of delirium correlated with changes in EEG, which may be interpreted as decreased alpha power and increased theta power, and generalized slowing with increased delirium severity [26,48]. In this study, the beta power belonging to the fast wave decreased, indicating hypoarousal of the cerebral cortex, similar to the aforementioned studies wherein eventually aggravation in the severity of delirium occurred. Moreover, the alpha and theta power corresponding to the slow wave did not represent the severity, which is thought to be because it reflected the real-world situation, unlike previous studies.
This study has a few limitations. The first and greatest limitation is the retrospective study design of reviewing medical records. Therefore, in the control group, the evaluation of delirium diagnosis and severity, such as CAM and DRS-R-98, was not performed. In future studies, it is necessary to prospectively administer the measures of delirium in the control group as well, thereby making between-group comparisons possible. Second, the number of participants included in the study was 69, which was relatively small compared to that in previous studies. This, along with the heterogeneity of the sample, makes it difficult to generalize the results of the study; a prospective follow-up study with a larger sample is required in this regard. Finally, delirium was characterized as varying attention and consciousness levels over a day, and the increase in the absolute power of the theta and gamma waves was similar to that observed in a vegetative state or a minimally conscious state patient [49]. The patient’s condition at the time of EEG measurement may be largely reflected in the study results. Therefore, it is expected that more characteristic and accurate results for delirium can be obtained if future studies are conducted by considering pattern changes in intraday EEG through repeated EEG imaging.
In conclusion, power spectrum analysis of qEEG using single-channel parameters had high accuracy in screening patients with delirium from among patients referred to the department of psychiatry and correlated with the severity of delirium. The diagnosis and differentiation of delirium require a lot of effort, skill, and training, and early screening and diagnosis are important for a better prognosis; therefore, this study is relevant in its suggestion that in the future, it may be possible to support the diagnosis of delirium using qEEG. Large-scale, well-controlled, prospective studies are required in the future.


Availability of Data and Material

Due to the sensitive nature of this study, participants were assured that raw data would remain confidential and would not be shared.

Conflicts of Interest

The authors have no potential conflicts of interest to disclose.

Author Contributions

Conceptualization: Seung Bhin Kim, Su Hyun Bong, Jun Won Kim. Data Curation: Seung Bhin Kim. Formal analysis: Seung Bhin Kim, Su Hyun Bong. Investigation: Seung Bhin Kim. Methodology: Seung Bhin Kim, Su Hyun Bong, Jun Won Kim. Project administration: Jun Won Kim. Supervision: Jun Won Kim. Visualization: Seung Bhin Kim, Su Hyun Bong. Writing—original draft: Seung Bhin Kim. Writing—review & editing: Su Hyun Bong, Jong Hun Lee, Tae Young Choi, Seo Young Yoon, Jun Won Kim.

Funding Statement

This study was supported by the Choi Shin-Hai Neuropsychiatric Research Fund of the Korean Neuropsychiatry Research Foundation in 2021.

Figure 1.
Topographical representation of regional quantitative electroencephalography topographic representation of differences between the delirium and non-delirium groups using absolute power. The dark area indicates an increase in absolute power. In delta and theta waves, a significant difference was found in almost all regions. Beta power was decreased in all areas. The greatest decrease was seen in the posterior region.
Figure 2.
ROC curves of regional qEEG. The ROC curves for delta, theta, and beta waves in delirium. The diagonal line represents a classification level equivalent to a 50% chance of identifying diagnosed cases. Frontal (FP1, F3, F7, Fz, FP2, F4, and F8), Central (T3, C3, Cz, T4 and C4), and Posterior (T5, P3, O1, Pz, T6, P4, O2). All p-values were <0.017. ROC, receiver operating characteristic; qEEG, quantitative electroencephalography.
Figure 3.
Correlation between severity of delirium and central region absolute beta power. Pearson’s correlation analysis between severity of delirium and central region absolute beta power. Frontal (FP1, F3, F7, Fz, FP2, F4, and F8), Central (T3, C3, Cz, T4, and C4), and Posterior (T5, P3, O1, Pz, T6, P4, and O2). DRS-R-98, delirium rating scale-revised-98.
Table 1.
Demographic and clinical characteristics
Delirium group (N=30) Control group (N=39) p
Sex 0.387
 Male 15 17
 Female 15 22
Age (yr) 72.27±12.76 67.97±6.83 0.066
 Severity 20.90±9.92 NA NA
 Total 26.73±11.02 NA NA
CGI-S 5.30±0.70 2.92±0.74 <0.001*

Values are presented as mean±standard deviation or only numbers.

* p<0.05.

DRS-R-98, delirium rating scale-revised-98; CGI-S, clinical global impression-severity; NA, not available

Table 2.
Regional quantitative electroencephalography results of the participants
Absolute power (μV2)
Delirium group (N=30) Control group (N=39)
 Frontal 16.75±6.86 10.51±4.26 <0.001*
 Central 14.64±7.04 8.37±3.68 <0.001*
 Posterior 15.09±6.66 9.04±5.84 <0.001*
 Frontal 18.19±12.15 7.72±6.15 <0.001*
 Central 20.55±14.65 8.09±8.00 <0.001*
 Posterior 22.13±16.54 9.37±11.71 <0.001*
 Frontal 12.36±11.61 12.21±11.46 0.594
 Central 13.45±10.58 13.29±12.71 0.45
 Posterior 15.89±12.67 17.90±20.50 0.569
 Frontal 6.48±3.92 9.21±6.32 0.009*
 Central 7.38±5.07 10.86±6.47 0.004*
 Posterior 6.30±4.35 10.80±6.06 <0.001*
 Frontal 1.52±1.09 2.80±4.68 0.032
 Central 1.29±0.88 1.89±1.28 0.018
 Posterior 1.01±0.70 1.36±0.85 0.019

Values are presented as mean±standard deviation. Frontal (FP1, F3, F7, Fz, FP2, F4, and F8), Central (T3, C3, Cz, T4, and C4), and Posterior (T5, P3, O1, Pz, T6, P4, and O2).

* p<0.017


1. Guha M. Diagnostic and statistical manual of mental disorders: DSM-5 (5th edition). Reference Reviews 2014;28:36-37.
2. Maldonado JR. Delirium pathophysiology: an updated hypothesis of the etiology of acute brain failure. Int J Geriatr Psychiatry 2018;33:1428-1457.
crossref pmid pdf
3. Elie M, Rousseau F, Cole M, Primeau F, McCusker J, Bellavance F. Prevalence and detection of delirium in elderly emergency department patients. CMAJ 2000;163:977-981.
pmid pmc
4. McNicoll L, Pisani MA, Zhang Y, Ely EW, Siegel MD, Inouye SK. Delirium in the intensive care unit: occurrence and clinical course in older patients. J Am Geriatr Soc 2003;51:591-598.
crossref pmid
5. Francis J. Delirium in older patients. J Am Geriatr Soc 1992;40:829-838.
crossref pmid
6. Marcantonio E, Ta T, Duthie E, Resnick NM. Delirium severity and psychomotor types: their relationship with outcomes after hip fracture repair. J Am Geriatr Soc 2002;50:850-857.
crossref pmid
7. van Dellen E, van der Kooi AW, Numan T, Koek HL, Klijn FA, Buijsrogge MP, et al. Decreased functional connectivity and disturbed directionality of information flow in the electroencephalography of intensive care unit patients with delirium after cardiac surgery. Anesthesiology 2014;121:328-335.
crossref pmid pdf
8. Fong TG, Tulebaev SR, Inouye SK. Delirium in elderly adults: diagnosis, prevention and treatment. Nat Rev Neurol 2009;5:210-220.
crossref pmid pmc pdf
9. Leslie DL, Marcantonio ER, Zhang Y, Leo-Summers L, Inouye SK. One-year health care costs associated with delirium in the elderly population. Arch Intern Med 2008;168:27-32.
crossref pmid pmc
10. Leslie DL, Inouye SK. The importance of delirium: economic and societal costs. J Am Geriatr Soc 2011;59(Suppl 2):S241-S243.
crossref pmid pmc
11. Spronk PE, Riekerk B, Hofhuis J, Rommes JH. Occurrence of delirium is severely underestimated in the ICU during daily care. Intensive Care Med 2009;35:1276-1280.
crossref pmid pmc
12. Gusmao-Flores D, Salluh JI, Chalhub RÁ, Quarantini LC. The Confusion Assessment Method for the Intensive Care Unit (CAM-ICU) and Intensive Care Delirium Screening Checklist (ICDSC) for the diagnosis of delirium: a systematic review and meta-analysis of clinical studies. Crit Care 2012;16:R115
crossref pmid pmc
13. Reade MC, Finfer S. Sedation and delirium in the intensive care unit. N Engl J Med 2014;370:444-454.
crossref pmid
14. Shi Q, Warren L, Saposnik G, Macdermid JC. Confusion assessment method: a systematic review and meta-analysis of diagnostic accuracy. Neuropsychiatr Dis Treat 2013;9:1359-1370.
crossref pmid pmc
15. van Eijk MM, van den Boogaard M, van Marum RJ, Benner P, Eikelenboom P, Honing ML, et al. Routine use of the confusion assessment method for the intensive care unit: a multicenter study. Am J Respir Crit Care Med 2011;184:340-344.
crossref pmid
16. Ashley EA. The precision medicine initiative: a new national effort. JAMA 2015;313:2119-2120.
crossref pmid
17. Luetz A, Balzer F, Radtke FM, Jones C, Citerio G, Walder B, et al. Delirium, sedation and analgesia in the intensive care unit: a multinational, two-part survey among intensivists. PLoS One 2014;9:e110935.
crossref pmid pmc
18. Pandharipande P, Shintani A, Peterson J, Pun BT, Wilkinson GR, Dittus RS, et al. Lorazepam is an independent risk factor for transitioning to delirium in intensive care unit patients. Anesthesiology 2006;104:21-26.
crossref pmid pdf
19. Alsop DC, Fearing MA, Johnson K, Sperling R, Fong TG, Inouye SK. The role of neuroimaging in elucidating delirium pathophysiology. J Gerontol A Biol Sci Med Sci 2006;61:1287-1293.
20. Brenner RP. Utility of EEG in delirium: past views and current practice. Int Psychogeriatr 1991;3:211-229.
crossref pmid
21. Soiza RL, Sharma V, Ferguson K, Shenkin SD, Seymour DG, Maclullich AM. Neuroimaging studies of delirium: a systematic review. J Psychosom Res 2008;65:239-248.
crossref pmid
22. Stoicea N, McVicker S, Quinones A, Agbenyefia P, Bergese SD. Delirium-biomarkers and genetic variance. Front Pharmacol 2014;5:75
crossref pmid pmc
23. van Munster BC, de Rooij SE, Korevaar JC. The role of genetics in delirium in the elderly patient. Dement Geriatr Cogn Disord 2009;28:187-195.
crossref pmid pdf
24. van der Kooi AW, Zaal IJ, Klijn FA, Koek HL, Meijer RC, Leijten FS, et al. Delirium detection using EEG: what and how to measure. Chest 2015;147:94-101.
crossref pmid
25. Sidhu KS, Balon R, Ajluni V, Boutros NN. Standard EEG and the difficult-to-assess mental status. Ann Clin Psychiatry 2009;21:103-108.
26. Kimchi EY, Neelagiri A, Whitt W, Sagi AR, Ryan SL, Gadbois G, et al. Clinical EEG slowing correlates with delirium severity and predicts poor clinical outcomes. Neurology 2019;93:e1260-e1271.
crossref pmid pmc
27. John ER, Ahn H, Prichep L, Trepetin M, Brown D, Kaye H. Developmental equations for the electroencephalogram. Science 1980;210:1255-1258.
crossref pmid
28. John ER, Prichep LS, Fridman J, Easton P. Neurometrics: computer-assisted differential diagnosis of brain dysfunctions. Science 1988;239:162-169.
crossref pmid
29. Senf GM. Neurometric BrainMapping in the diagnosis and rehabilitation of cognitive dysfunction. Cognitive Rehabilitation 1988;6:20-37.

30. Busner J, Targum SD. The clinical global impressions scale: applying a research tool in clinical practice. Psychiatry (Edgmont) 2007;4:28-37.

31. Trzepacz PT, Mittal D, Torres R, Kanary K, Norton J, Jimerson N. Validation of the delirium rating scale-revised-98: comparison with the delirium rating scale and the cognitive test for delirium. J Neuropsychiatry Clin Neurosci 2001;13:229-242.
crossref pmid
32. Lee Y, Ryu J, Lee J, Kim HJ, Shin IH, Kim JL, et al. Korean version of the delirium rating scale-revised-98: reliability and validity. Psychiatry Investig 2011;8:30-38.
crossref pmid pmc
33. Son KL, Choi JS, Lee J, Park SM, Lim JA, Lee JY, et al. Neurophysiological features of Internet gaming disorder and alcohol use disorder: a resting-state EEG study. Transl Psychiatry 2015;5:e628.
crossref pmid pmc pdf
34. Metz CE. Basic principles of ROC analysis. Semin Nucl Med 1978;8:283-298.
crossref pmid
35. van der Kooi AW, Slooter AJ, van Het Klooster MA, Leijten FS. EEG in delirium: increased spectral variability and decreased complexity. Clin Neurophysiol 2014;125:2137-2139.
crossref pmid
36. Shafi MM, Santarnecchi E, Fong TG, Jones RN, Marcantonio ER, Pascual-Leone A, et al. Advancing the neurophysiological understanding of delirium. J Am Geriatr Soc 2017;65:1114-1118.
crossref pmid pmc pdf
37. Fleischmann R, Tränkner S, Bathe-Peters R, Rönnefarth M, Schmidt S, Schreiber SJ, et al. Diagnostic performance and utility of quantitative EEG analyses in delirium: confirmatory results from a large retrospective case-control study. Clin EEG Neurosci 2019;50:111-120.
crossref pmid pdf
38. Plaschke K, Hill H, Engelhardt R, Thomas C, von Haken R, Scholz M, et al. EEG changes and serum anticholinergic activity measured in patients with delirium in the intensive care unit. Anaesthesia 2007;62:1217-1223.
crossref pmid
39. Nikulin VV, Brismar T. Long-range temporal correlations in electroencephalographic oscillations: relation to topography, frequency band, age and gender. Neuroscience 2005;130:549-558.
crossref pmid
40. Steriade M, Dossi RC, Nuñez A. Network modulation of a slow intrinsic oscillation of cat thalamocortical neurons implicated in sleep delta waves: cortically induced synchronization and brainstem cholinergic suppression. J Neurosci 1991;11:3200-3217.
crossref pmid pmc
41. Roy A, Svensson FP, Mazeh A, Kocsis B. Prefrontal-hippocampal coupling by theta rhythm and by 2-5 Hz oscillation in the delta band: the role of the nucleus reuniens of the thalamus. Brain Struct Funct 2017;222:2819-2830.
crossref pmid pmc pdf
42. Mormann F, Osterhage H, Andrzejak RG, Weber B, Fernández G, Fell J, et al. Independent delta/theta rhythms in the human hippocampus and entorhinal cortex. Front Hum Neurosci 2008;2:3
crossref pmid pmc
43. Smith SJ. EEG in neurological conditions other than epilepsy: when does it help, what does it add? J Neurol Neurosurg Psychiatry 2005;76:ii8-ii12.
crossref pmid pmc
44. Benz N, Hatz F, Bousleiman H, Ehrensperger MM, Gschwandtner U, Hardmeier M, et al. Slowing of EEG background activity in Parkinson’s and Alzheimer’s disease with early cognitive dysfunction. Front Aging Neurosci 2014;6:314
crossref pmid pmc
45. Roopun AK, Middleton SJ, Cunningham MO, LeBeau FE, Bibbig A, Whittington MA, et al. A beta2-frequency (20-30 Hz) oscillation in nonsynaptic networks of somatosensory cortex. Proc Natl Acad Sci U S A 2006;103:15646-15650.
crossref pmid pmc
46. Munia TTK, Haider A, Schneider C, Romanick M, Fazel-Rezai R. A novel EEG based spectral analysis of persistent brain function alteration in athletes with concussion history. Sci Rep 2017;7:17221
crossref pmid pmc pdf
47. Schulman JJ, Cancro R, Lowe S, Lu F, Walton KD, Llinás RR. Imaging of thalamocortical dysrhythmia in neuropsychiatry. Front Hum Neurosci 2011;5:69
crossref pmid pmc
48. Thomas C, Hestermann U, Kopitz J, Plaschke K, Oster P, Driessen M, et al. Serum anticholinergic activity and cerebral cholinergic dysfunction: an EEG study in frail elderly with and without delirium. BMC Neurosci 2008;9:86
crossref pmid pmc pdf
49. Lehembre R, Marie-Aurélie B, Vanhaudenhuyse A, Chatelle C, Cologan V, Leclercq Y, et al. Resting-state EEG study of comatose patients: a connectivity and frequency analysis to find differences between vegetative and minimally conscious states. Funct Neurol 2012;27:41-47.
pmid pmc
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