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Liu G, Tian F, Zhu Y, Jiang M, Cui L, Zhang Y, Wang Y, Su Y. The predictive value of EEG reactivity by electrical stimulation and quantitative analysis in critically ill patients with large hemispheric infarction. J Crit Care 2023; 78:154358. [PMID: 37329762 DOI: 10.1016/j.jcrc.2023.154358] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2022] [Revised: 05/05/2023] [Accepted: 06/02/2023] [Indexed: 06/19/2023]
Abstract
PURPOSE The intensive care of critically ill patients with large hemispheric infarction improves the survival rate. However, established prognostic markers for neurological outcome show variable accuracy. We aimed to assess the value of electrical stimulation and quantitative analysis of EEG reactivity for early prognostication in this critically ill population. MATERIALS AND METHODS We prospectively enrolled consecutive patients between January 2018 and December 2021. EEG reactivity was randomly performed by pain or electrical stimulation via visual and quantitative analysis. Neurological outcome within 6-month was dichotomized as good (modified Rankin Scale, mRS 0-3) or poor (mRS 4-6). RESULTS Ninety-four patients were admitted, and 56 were included in the final analysis. EEG reactivity using electrical stimulation was superior to pain stimulation for good outcome prediction (visual analysis: AUC 0.825 vs. 0.763, P = 0.143; quantitative analysis: AUC 0.931 vs. 0.844, P = 0.058). The AUC of EEG reactivity by pain stimulation with visual analysis was 0.763, which increased to 0.931 by electrical stimulation with quantitative analysis (P = 0.006). When using quantitative analysis, the AUC of EEG reactivity increased (pain stimulation 0.763 vs. 0.844, P = 0.118; electrical stimulation 0.825 vs. 0.931, P = 0.041). CONCLUSION EEG reactivity by electrical stimulation and quantitative analysis seems a promising prognostic factor in these critical patients.
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Affiliation(s)
- Gang Liu
- Neurocritical Care Unit, Department of Neurology, Xuanwu Hospital, Capital Medical University, National Brain Injury Evaluation Quality Control Center, National Center for Neurological Disorders, National Clinical Research Center for Geriatric Diseases, Beijing 10053, China
| | - Fei Tian
- Neurocritical Care Unit, Department of Neurology, Xuanwu Hospital, Capital Medical University, National Brain Injury Evaluation Quality Control Center, National Center for Neurological Disorders, National Clinical Research Center for Geriatric Diseases, Beijing 10053, China
| | - Yu Zhu
- Neurocritical Care Unit, Department of Neurology, Xuanwu Hospital, Capital Medical University, National Brain Injury Evaluation Quality Control Center, National Center for Neurological Disorders, National Clinical Research Center for Geriatric Diseases, Beijing 10053, China
| | - Mengdi Jiang
- Neurocritical Care Unit, Department of Neurology, Xuanwu Hospital, Capital Medical University, National Brain Injury Evaluation Quality Control Center, National Center for Neurological Disorders, National Clinical Research Center for Geriatric Diseases, Beijing 10053, China
| | - Lili Cui
- Neurocritical Care Unit, Department of Neurology, Xuanwu Hospital, Capital Medical University, National Brain Injury Evaluation Quality Control Center, National Center for Neurological Disorders, National Clinical Research Center for Geriatric Diseases, Beijing 10053, China
| | - Yan Zhang
- Neurocritical Care Unit, Department of Neurology, Xuanwu Hospital, Capital Medical University, National Brain Injury Evaluation Quality Control Center, National Center for Neurological Disorders, National Clinical Research Center for Geriatric Diseases, Beijing 10053, China
| | - Yuan Wang
- Neurocritical Care Unit, Department of Neurology, Xuanwu Hospital, Capital Medical University, National Brain Injury Evaluation Quality Control Center, National Center for Neurological Disorders, National Clinical Research Center for Geriatric Diseases, Beijing 10053, China.
| | - Yingying Su
- Neurocritical Care Unit, Department of Neurology, Xuanwu Hospital, Capital Medical University, National Brain Injury Evaluation Quality Control Center, National Center for Neurological Disorders, National Clinical Research Center for Geriatric Diseases, Beijing 10053, China.
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Bouchereau E, Marchi A, Hermann B, Pruvost-Robieux E, Guinard E, Legouy C, Schimpf C, Mazeraud A, Baron JC, Ramdani C, Gavaret M, Sharshar T, Turc G. Quantitative analysis of early-stage EEG reactivity predicts awakening and recovery of consciousness in patients with severe brain injury. Br J Anaesth 2023; 130:e225-e232. [PMID: 36243578 DOI: 10.1016/j.bja.2022.09.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2022] [Revised: 09/06/2022] [Accepted: 09/09/2022] [Indexed: 01/25/2023] Open
Abstract
BACKGROUND Decisions of withdrawal of life-sustaining therapy for patients with severe brain injury are often based on prognostic evaluations such as analysis of electroencephalography (EEG) reactivity (EEG-R). However, EEG-R usually relies on visual assessment, which requires neurophysiological expertise and is prone to inter-rater variability. We hypothesised that quantitative analysis of EEG-R obtained 3 days after patient admission can identify new markers of subsequent awakening and consciousness recovery. METHODS In this prospective observational study of patients with severe brain injury requiring mechanical ventilation, quantitative EEG-R was assessed using standard 11-lead EEG with frequency-based (power spectral density) and functional connectivity-based (phase-lag index) analyses. Associations between awakening in the intensive care unit (ICU) and reactivity to auditory and nociceptive stimulations were assessed with logistic regression. Secondary outcomes included in-ICU mortality and 3-month Coma Recovery Scale-Revised (CRS-R) score. RESULTS Of 116 patients, 86 (74%) awoke in the ICU. Among quantitative EEG-R markers, variation in phase-lag index connectivity in the delta frequency band after noise stimulation was associated with awakening (adjusted odds ratio=0.89, 95% confidence interval: 0.81-0.97, P=0.02 corrected for multiple tests), independently of age, baseline severity, and sedation. This new marker was independently associated with improved 3-month CRS-R (adjusted β=-0.16, standard error 0.075, P=0.048), but not with mortality (adjusted odds ratio=1.08, 95% CI: 0.99-1.18, P=0.10). CONCLUSIONS An early-stage quantitative EEG-R marker was independently associated with awakening and 3-month level of consciousness in patients with severe brain injury. This promising marker based on functional connectivity will need external validation before potential integration into a multimodal prognostic model.
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Affiliation(s)
- Eléonore Bouchereau
- Anaesthesiology and ICU Department, Sainte Anne Hospital, Paris, France; Institute of Psychiatry and Neurosciences of Paris (IPNP), INSERM U1266, Paris, France.
| | - Angela Marchi
- Epileptology and Cerebral Rhythmology Department, APHM, Timone Hospital, Marseille, France
| | - Bertrand Hermann
- ICU Department, Hôpital Européen Georges Pompidou, Paris, France; Institut du Cerveau et de la Moelle épinière - ICM, Paris, France; Université Paris Cité, Paris, France
| | - Estelle Pruvost-Robieux
- Institute of Psychiatry and Neurosciences of Paris (IPNP), INSERM U1266, Paris, France; Université Paris Cité, Paris, France; Neurophysiology Department, Sainte Anne Hospital, Paris, France
| | - Eléonore Guinard
- Institute of Psychiatry and Neurosciences of Paris (IPNP), INSERM U1266, Paris, France; Université Paris Cité, Paris, France; Neurophysiology Department, Sainte Anne Hospital, Paris, France
| | - Camille Legouy
- Anaesthesiology and ICU Department, Sainte Anne Hospital, Paris, France
| | - Caroline Schimpf
- Anaesthesiology and ICU Department, Sainte Anne Hospital, Paris, France
| | - Aurélien Mazeraud
- Anaesthesiology and ICU Department, Sainte Anne Hospital, Paris, France; Université Paris Cité, Paris, France
| | - Jean-Claude Baron
- Institute of Psychiatry and Neurosciences of Paris (IPNP), INSERM U1266, Paris, France; Université Paris Cité, Paris, France; Neurology Department, GHU Paris Psychiatry and Neurosciences, Sainte Anne Hospital, Paris, France; FHU NeuroVasc, Paris, France
| | - Céline Ramdani
- Institut de Recherche Biomédicale des Armées (IRBA), Brétigny-sur-Orge, France
| | - Martine Gavaret
- Institute of Psychiatry and Neurosciences of Paris (IPNP), INSERM U1266, Paris, France; Université Paris Cité, Paris, France; Neurophysiology Department, Sainte Anne Hospital, Paris, France; FHU NeuroVasc, Paris, France
| | - Tarek Sharshar
- Anaesthesiology and ICU Department, Sainte Anne Hospital, Paris, France; Institute of Psychiatry and Neurosciences of Paris (IPNP), INSERM U1266, Paris, France; Université Paris Cité, Paris, France; FHU NeuroVasc, Paris, France
| | - Guillaume Turc
- Institute of Psychiatry and Neurosciences of Paris (IPNP), INSERM U1266, Paris, France; Université Paris Cité, Paris, France; Neurology Department, GHU Paris Psychiatry and Neurosciences, Sainte Anne Hospital, Paris, France; FHU NeuroVasc, Paris, France
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Abstract
OBJECTIVE This study aimed to explore the effectiveness of quantitative electroencephalogram (EEG) and EEG reactivity (EEG-R) to predict the prognosis of patients with severe traumatic brain injury. METHODS This was a prospective observational study on severe traumatic brain injury. Quantitative EEG monitoring was performed for 8 to 12 hours within 14 days of onset. The EEG-R was tested during the monitoring period. We then followed patients for 3 months to determine their level of consciousness. The Glasgow Outcome Scale (GOS) score was used. The score 3, 4, 5 of GOS were defined good prognosis, and score 1 and 2 as poor prognosis. Univariate and multivariate analyses were employed to assess the association of predictors with poor prognosis. RESULTS A total of 56 patients were included in the study. Thirty-two patients (57.1%) awoke (good prognosis) in 3 months after the onset. Twenty-four patients (42.9%) did not awake (poor prognosis), including 11 cases deaths. Univariate analysis showed that Glasgow coma scale (GCS) score, the amplitude-integrated EEG (aEEG), the relative band power (RBP), the relative alpha variability (RAV), the spectral entropy (SE), and EEG-R reached significant difference between the poor-prognosis and good-prognosis groups. However, age, gender, and pupillary light reflex did not correlate significantly with poor prognosis. Furthermore, multivariate logistic regression analysis showed that only RAV and EEG-R were significant independent predictors of poor prognosis, and the prognostic model containing these 2 variables yielded a predictive performance with an area under the curve of 0.882. CONCLUSIONS Quantitative EEG and EEG-R may be used to assess the prognosis of patients with severe traumatic brain injury early. RAV and EEG-R were the good predictive indicators of poor prognosis.
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Affiliation(s)
- Jian Wang
- Neurosurgery ICU, Xiangya Hospital, Central South University, Changsha, China
| | - Li Huang
- General ICU/Department of Critical Care Medicine, Xiangya Hospital, Central South University, Changsha, China
| | - Xinhua Ma
- General ICU/Department of Critical Care Medicine, Xiangya Hospital, Central South University, Changsha, China
| | - Chunguang Zhao
- General ICU/Department of Critical Care Medicine, Xiangya Hospital, Central South University, Changsha, China
| | - Jinfang Liu
- Neurosurgery ICU, Xiangya Hospital, Central South University, Changsha, China
| | - Daomiao Xu
- General ICU/Department of Critical Care Medicine, Xiangya Hospital, Central South University, Changsha, China
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Johnsen B, Jeppesen J, Duez CHV. Common patterns of EEG reactivity in post-anoxic coma identified by quantitative analyses. Clin Neurophysiol 2022; 142:143-153. [PMID: 36041343 DOI: 10.1016/j.clinph.2022.07.507] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2022] [Revised: 06/23/2022] [Accepted: 07/28/2022] [Indexed: 11/03/2022]
Abstract
OBJECTIVE Description of typical kinds of EEG reactivity (EEG-R) in post-anoxic coma using a quantitative method. METHODS Study of 101 out-of-hospital cardiac arrest patients, 71 with good outcome (cerebral performance category scale ≤ 2). EEG was recorded 12-24 hours after cardiac arrest and four noxious, one auditory, and one visual stimulation were applied for 30 seconds each. Individual reference intervals for the power in the delta, theta, alpha, and beta bands were calculated based on six 2-second resting epochs just prior to stimulations. EEG-R in consecutive 2-second epochs after stimulation was expressed in Z-scores. RESULTS EEG-R occurred roughly equally frequent as an increase or as a decrease in EEG activity. Sternal rub and sound stimulation were most provocative with the most pronounced changes as an increase in delta activity 4.5-8.5 seconds after stimulation and a decrease in theta activity 0.5-4.5 seconds after stimulation. These parameters predicted good outcome with an AUC of 0.852 (95 % CI: 0.771-0.932). CONCLUSIONS Quantitative EEG-R is a feasible method for identification of common types of reactivity, for evaluation of stimulation methods, and for prognostication. SIGNIFICANCE This method provides an objective measure of EEG-R revealing knowledge about the nature of EEG-R and its use as a diagnostic tool.
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Affiliation(s)
- Birger Johnsen
- Department of Clinical Neurophysiology, Aarhus University Hospital, Aarhus, Denmark; Department of Clinical Medicine, Aarhus University, Denmark.
| | - Jesper Jeppesen
- Department of Clinical Neurophysiology, Aarhus University Hospital, Aarhus, Denmark; Department of Clinical Medicine, Aarhus University, Denmark
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5
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Williams A, Zeng Y, Li Z, Thakor N, Geocadin RG, Bronder J, Martinez NC, Ritzl EK, Cho SM. Quantitative Assessment of Electroencephalogram Reactivity in Comatose Patients on Extracorporeal Membrane Oxygenation. Int J Neural Syst 2022; 32:2250025. [PMID: 35443895 DOI: 10.1142/s0129065722500253] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Objective assessment of the brain's responsiveness in comatose patients on Extracorporeal Membrane Oxygenation (ECMO) support is essential to clinical care, but current approaches are limited by subjective methodology and inter-rater disagreement. Quantitative electroencephalogram (EEG) algorithms could potentially assist clinicians, improving diagnostic accuracy. We developed a quantitative, stimulus-based algorithm to assess EEG reactivity features in comatose patients on ECMO support. Patients underwent a stimulation protocol of increasing intensity (auditory, peripheral, and nostril stimulation). A total of 129 20-s EEG epochs were collected from 24 patients (age [Formula: see text], 10 females, 14 males) on ECMO support with a Glasgow Coma Scale[Formula: see text]8. EEG reactivity scores ([Formula: see text]-scores) were calculated using aggregated spectral power and permutation entropy for each of five frequency bands ([Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text]. Parameter estimation techniques were applied to [Formula: see text]-scores to identify properties that replicate the decision process of experienced clinicians performing visual analysis. Spectral power changes from audio stimulation were concentrated in the [Formula: see text] band, whereas peripheral stimulation elicited an increase in spectral power across multiple bands, and nostril stimulation changed the entropy of the [Formula: see text] band. The findings of this pilot study on [Formula: see text]-score lay a foundation for a future prediction tool with clinical applications.
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Affiliation(s)
- Autumn Williams
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Yinuo Zeng
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Ziwei Li
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Nitish Thakor
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Romergryko G Geocadin
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Jay Bronder
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | | | - Eva K Ritzl
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Sung-Min Cho
- Department of Neurology, Johns Hopkins University School of Medicine, 600 N. Wolfe Street, Phipps 455, Baltimore, MD, USA
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6
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Serban CA, Barborica A, Roceanu AM, Mindruta I, Ciurea J, Pâslaru AC, Zăgrean AM, Zăgrean L, Moldovan M. A method to assess the default EEG macrostate and its reactivity to stimulation. Clin Neurophysiol 2021; 134:50-64. [PMID: 34973517 DOI: 10.1016/j.clinph.2021.12.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2021] [Revised: 08/23/2021] [Accepted: 12/04/2021] [Indexed: 11/16/2022]
Abstract
OBJECTIVE The default mode network (DMN) is deactivated by stimulation. We aimed to assess the DMN reactivity impairment by routine EEG recordings in stroke patients with impaired consciousness. METHODS Binocular light flashes were delivered at 1 Hz in 1-minute epochs, following a 1-minute baseline (PRE). The EEG was decomposed in a series of binary oscillatory macrostates by topographic spectral clustering. The most deactivated macrostate was labeled the default EEG macrostate (DEM). Its reactivity (DER) was quantified as the decrease in DEM occurrence probability during stimulation. A normalized DER index (DERI) was calculated as DER/PRE. The measures were compared between 14 healthy controls and 32 comatose patients under EEG monitoring following an acute stroke. RESULTS The DEM was mapped to the posterior DMN hubs. In the patients, these DEM source dipoles were 3-4 times less frequent and were associated with an increased theta activity. Even in a reduced 6-channel montage, a DER below 6.26% corresponding to a DERI below 0.25 could discriminate the patients with sensitivity and specificity well above 80%. CONCLUSION The method detected the DMN impairment in post-stroke coma patients. SIGNIFICANCE The DEM and its reactivity to stimulation could be useful to monitor the DMN function at bedside.
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Affiliation(s)
- Cosmin-Andrei Serban
- Physics Department, University of Bucharest, Romania; Termobit Prod SRL, Bucharest, Romania; FHC Inc, Bowdoin, ME, USA.
| | - Andrei Barborica
- Physics Department, University of Bucharest, Romania; Termobit Prod SRL, Bucharest, Romania; FHC Inc, Bowdoin, ME, USA.
| | | | - Ioana Mindruta
- Neurology Department, University Emergency Hospital, Bucharest, Romania.
| | - Jan Ciurea
- Department of Neurosurgery, Bagdasar-Arseni Emergency Hospital, Bucharest, Romania.
| | - Alexandru C Pâslaru
- Division of Physiology and Neuroscience, "Carol Davila" University of Medicine and Pharmacy, Bucharest, Romania
| | - Ana-Maria Zăgrean
- Division of Physiology and Neuroscience, "Carol Davila" University of Medicine and Pharmacy, Bucharest, Romania
| | - Leon Zăgrean
- Division of Physiology and Neuroscience, "Carol Davila" University of Medicine and Pharmacy, Bucharest, Romania
| | - Mihai Moldovan
- Termobit Prod SRL, Bucharest, Romania; Division of Physiology and Neuroscience, "Carol Davila" University of Medicine and Pharmacy, Bucharest, Romania; Neuroscience, University of Copenhagen, Copenhagen, Denmark; Department of Clinical Neurophysiology, Rigshospitalet, Copenhagen, Denmark.
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7
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Fröhlich S, Kutz DF, Müller K, Voelcker-Rehage C. Characteristics of Resting State EEG Power in 80+-Year-Olds of Different Cognitive Status. Front Aging Neurosci 2021; 13:675689. [PMID: 34456708 PMCID: PMC8387136 DOI: 10.3389/fnagi.2021.675689] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2021] [Accepted: 07/07/2021] [Indexed: 11/28/2022] Open
Abstract
Compared with healthy older adults, patients with Alzheimer's disease show decreased alpha and beta power as well as increased delta and theta power during resting state electroencephalography (rsEEG). Findings for mild cognitive impairment (MCI), a stage of increased risk of conversion to dementia, are less conclusive. Cognitive status of 213 non-demented high-agers (mean age, 82.5 years) was classified according to a neuropsychological screening and a cognitive test battery. RsEEG was measured with eyes closed and open, and absolute power in delta, theta, alpha, and beta bands were calculated for nine regions. Results indicate no rsEEG power differences between healthy individuals and those with MCI. There were also no differences present between groups in EEG reactivity, the change in power from eyes closed to eyes open, or the topographical pattern of each frequency band. Overall, EEG reactivity was preserved in 80+-year-olds without dementia, and topographical patterns were described for each frequency band. The application of rsEEG power as a marker for the early detection of dementia might be less conclusive for high-agers.
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Affiliation(s)
- Stephanie Fröhlich
- Department of Neuromotor Behavior and Exercise, Institute of Sport and Exercise Sciences, Faculty of Psychology and Sport Sciences, University of Münster, Münster, Germany.,Department of Sports Psychology (With Focus on Prevention and Rehabilitation), Institute of Human Movement Science and Health, Faculty of Behavioural and Social Sciences, Chemnitz University of Technology, Chemnitz, Germany
| | - Dieter F Kutz
- Department of Neuromotor Behavior and Exercise, Institute of Sport and Exercise Sciences, Faculty of Psychology and Sport Sciences, University of Münster, Münster, Germany.,Institute of Human Movement Science and Health, Faculty of Behavioural and Social Sciences, Chemnitz University of Technology, Chemnitz, Germany
| | - Katrin Müller
- Department of Sports Psychology (With Focus on Prevention and Rehabilitation), Institute of Human Movement Science and Health, Faculty of Behavioural and Social Sciences, Chemnitz University of Technology, Chemnitz, Germany.,Department of Social Science of Physical Activity and Health, Institute of Human Movement Science and Health, Faculty of Behavioural and Social Sciences, Chemnitz University of Technology, Chemnitz, Germany
| | - Claudia Voelcker-Rehage
- Department of Neuromotor Behavior and Exercise, Institute of Sport and Exercise Sciences, Faculty of Psychology and Sport Sciences, University of Münster, Münster, Germany.,Department of Sports Psychology (With Focus on Prevention and Rehabilitation), Institute of Human Movement Science and Health, Faculty of Behavioural and Social Sciences, Chemnitz University of Technology, Chemnitz, Germany
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8
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Admiraal MM, Ramos LA, Delgado Olabarriaga S, Marquering HA, Horn J, van Rootselaar AF. Quantitative analysis of EEG reactivity for neurological prognostication after cardiac arrest. Clin Neurophysiol 2021; 132:2240-2247. [PMID: 34315065 DOI: 10.1016/j.clinph.2021.07.004] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2019] [Revised: 04/06/2021] [Accepted: 07/03/2021] [Indexed: 11/28/2022]
Abstract
OBJECTIVE To test whether 1) quantitative analysis of EEG reactivity (EEG-R) using machine learning (ML) is superior to visual analysis, and 2) combining quantitative analyses of EEG-R and EEG background pattern increases prognostic value for prediction of poor outcome after cardiac arrest (CA). METHODS Several types of ML models were trained with twelve quantitative features derived from EEG-R and EEG background data of 134 adult CA patients. Poor outcome was a Cerebral Performance Category score of 3-5 within 6 months. RESULTS The Random Forest (RF) trained on EEG-R showed the highest AUC of 83% (95-CI 80-86) of tested ML classifiers, predicting poor outcome with 46% sensitivity (95%-CI 40-51) and 89% specificity (95%-CI 86-92). Visual analysis of EEG-R had 80% sensitivity and 65% specificity. The RF was also the best classifier for EEG background (AUC 85%, 95%-CI 83-88) at 24 h after CA, with 62% sensitivity (95%-CI 57-67) and 84% specificity (95%-CI 79-88). Combining EEG-R and EEG background RF classifiers reduced the number of false positives. CONCLUSIONS Quantitative EEG-R using ML predicts poor outcome with higher specificity, but lower sensitivity compared to visual analysis of EEG-R, and is of some additional value to ML on EEG background data. SIGNIFICANCE Quantitative EEG-R using ML is a promising alternative to visual analysis and of some added value to ML on EEG background data.
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Affiliation(s)
- M M Admiraal
- Amsterdam UMC, University of Amsterdam, Department of Neurology/Clinical Neurophysiology, Amsterdam Neuroscience, Amsterdam, the Netherlands.
| | - L A Ramos
- Amsterdam UMC, University of Amsterdam, Department Biomedical Engineering & Physics, Amsterdam Neuroscience, Amsterdam, the Netherlands; Amsterdam UMC, University of Amsterdam, Department of Clinical Epidemiology, Biostatistics and Bioinformatics, Amsterdam, the Netherlands
| | - S Delgado Olabarriaga
- Amsterdam UMC, University of Amsterdam, Department of Clinical Epidemiology, Biostatistics and Bioinformatics, Amsterdam, the Netherlands
| | - H A Marquering
- Amsterdam UMC, University of Amsterdam, Department Biomedical Engineering & Physics, Amsterdam Neuroscience, Amsterdam, the Netherlands; Amsterdam UMC, University of Amsterdam, Department of Radiology and Nuclear Medicine, Amsterdam, the Netherlands
| | - J Horn
- Amsterdam UMC, University of Amsterdam, Laboratory for Experimental Intensive Care and Anesthesiology, Amsterdam, the Netherlands; Amsterdam UMC, University of Amsterdam, Department of Intensive Care, Amsterdam Neuroscience, Amsterdam, the Netherlands
| | - A F van Rootselaar
- Amsterdam UMC, University of Amsterdam, Department of Neurology/Clinical Neurophysiology, Amsterdam Neuroscience, Amsterdam, the Netherlands
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Korit Áková E, Doležalová I, Chládek J, Jurková T, Chrastina J, Plešinger F, Roman R, Pail M, Jurák P, Shaw DJ, Brázdil M. A Novel Statistical Model for Predicting the Efficacy of Vagal Nerve Stimulation in Patients With Epilepsy (Pre-X-Stim) Is Applicable to Different EEG Systems. Front Neurosci 2021; 15:635787. [PMID: 34045942 PMCID: PMC8144700 DOI: 10.3389/fnins.2021.635787] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2020] [Accepted: 03/22/2021] [Indexed: 11/13/2022] Open
Abstract
Background: Identifying patients with intractable epilepsy who would benefit from therapeutic chronic vagal nerve stimulation (VNS) preoperatively remains a major clinical challenge. We have developed a statistical model for predicting VNS efficacy using only routine preimplantation electroencephalogram (EEG) recorded with the TruScan EEG device (Brazdil et al., 2019). It remains to be seen, however, if this model can be applied in different clinical settings. Objective: To validate our model using EEG data acquired with a different recording system. Methods: We identified a validation cohort of eight patients implanted with VNS, whose preimplantation EEG was recorded on the BrainScope device and who underwent the EEG recording according to the protocol. The classifier developed in our earlier work, named Pre-X-Stim, was then employed to classify these patients as predicted responders or non-responders based on the dynamics in EEG power spectra. Predicted and real-world outcomes were compared to establish the applicability of this classifier. In total, two validation experiments were performed using two different validation approaches (single classifier or classifier voting). Results: The classifier achieved 75% accuracy, 67% sensitivity, and 100% specificity. Only two patients, both real-life responders, were classified incorrectly in both validation experiments. Conclusion: We have validated the Pre-X-Stim model on EEGs from a different recording system, which indicates its application under different technical conditions. Our approach, based on preoperative EEG, is easily applied and financially undemanding and presents great potential for real-world clinical use.
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Affiliation(s)
- Eva Korit Áková
- Institute of Biostatistics and Analyses, Faculty of Medicine, Masaryk University, Brno, Czechia
| | - Irena Doležalová
- Brno Epilepsy Center, Department of Neurology and Neurosurgery, St. Anne's University Hospital and Medical Faculty of Masaryk University, Brno, Czechia
| | - Jan Chládek
- Behavioral and Social Neuroscience Research Group, Central European Institute of Technology, Masaryk University, Brno, Czechia.,Institute of Scientific Instruments of the Czech Academy of Sciences, Brno, Czechia
| | - Tereza Jurková
- Brno Epilepsy Center, Department of Neurology and Neurosurgery, St. Anne's University Hospital and Medical Faculty of Masaryk University, Brno, Czechia
| | - Jan Chrastina
- Brno Epilepsy Center, Department of Neurology and Neurosurgery, St. Anne's University Hospital and Medical Faculty of Masaryk University, Brno, Czechia
| | - Filip Plešinger
- Institute of Scientific Instruments of the Czech Academy of Sciences, Brno, Czechia
| | - Robert Roman
- Behavioral and Social Neuroscience Research Group, Central European Institute of Technology, Masaryk University, Brno, Czechia
| | - Martin Pail
- Brno Epilepsy Center, Department of Neurology and Neurosurgery, St. Anne's University Hospital and Medical Faculty of Masaryk University, Brno, Czechia
| | - Pavel Jurák
- Institute of Scientific Instruments of the Czech Academy of Sciences, Brno, Czechia
| | - Daniel J Shaw
- Behavioral and Social Neuroscience Research Group, Central European Institute of Technology, Masaryk University, Brno, Czechia
| | - Milan Brázdil
- Brno Epilepsy Center, Department of Neurology and Neurosurgery, St. Anne's University Hospital and Medical Faculty of Masaryk University, Brno, Czechia.,Behavioral and Social Neuroscience Research Group, Central European Institute of Technology, Masaryk University, Brno, Czechia
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Chen W, Liu G, Su Y, Zhang Y, Lin Y, Jiang M, Huang H, Ren G, Yan J. EEG signal varies with different outcomes in comatose patients: A quantitative method of electroencephalography reactivity. J Neurosci Methods 2020; 342:108812. [PMID: 32565224 DOI: 10.1016/j.jneumeth.2020.108812] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2019] [Revised: 06/05/2020] [Accepted: 06/15/2020] [Indexed: 10/24/2022]
Abstract
BACKGROUND Electroencephalographic reactivity (EEG-R) is a major predictor of outcome in comatose patients; however, the inter-rater reliability is limited due to the lack of homogeneous stimuli and quantitative interpretation. NEW METHODS EEG-R testing was employed in comatose patients by quantifiable electrical stimulation. Reactivity at different frequency bands was computed as the difference between pre- and post-stimulations in power spectra and connectivity function (including magnitude squared coherence and transfer entropy). The clinical outcomes were dichotomized as good and poor according to the recovery of consciousness. Signal discrimination of EEG-R was compared between the two groups. RESULTS A total of 18 patients (43%) regained consciousness at a 3-month follow-up. In the patients who regained consciousness, the EEG power increased significantly (P < 0.05) at the Alpha and Beta frequency bands after stimulation as compared to those with no behavioral awakening. Also, connectivity enhancement (including linear and nonlinear analysis) in the Beta and Gamma bands and connectivity decrease (nonlinear transfer entropy analysis) in the Delta band after stimulus were observed in the good outcome group. COMPARISON WITH EXISTING METHOD(S) In this study, the combined use of quantifiable stimulation and quantitative analysis shed new light on differentiating brain responses in comatose patients with good and poor outcomes as well as exploring the nature of EEG changes concerning the recovery of consciousness. CONCLUSIONS The combination of quantifiable electrical stimulation and quantitative analysis with spectral power and connectivity for the EEG-R may be a promising method to predict the outcome of comatose patients.
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Affiliation(s)
- Weibi Chen
- Department of Neurology, Xuanwu Hospital, Capital Medical University, Beijing, China
| | - Gang Liu
- Department of Neurology, Xuanwu Hospital, Capital Medical University, Beijing, China
| | - Yingying Su
- Department of Neurology, Xuanwu Hospital, Capital Medical University, Beijing, China.
| | - Yan Zhang
- Department of Neurology, Xuanwu Hospital, Capital Medical University, Beijing, China
| | - Yicong Lin
- Department of Neurology, Xuanwu Hospital, Capital Medical University, Beijing, China
| | - Mengdi Jiang
- Department of Neurology, Xuanwu Hospital, Capital Medical University, Beijing, China
| | - Huijin Huang
- Department of Neurology, Xuanwu Hospital, Capital Medical University, Beijing, China
| | - Guoping Ren
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Jiaqing Yan
- College of Electrical and Control Engineering, North China University of Technology, Beijing, China.
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11
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Amorim E, van der Stoel M, Nagaraj SB, Ghassemi MM, Jing J, O'Reilly UM, Scirica BM, Lee JW, Cash SS, Westover MB. Quantitative EEG reactivity and machine learning for prognostication in hypoxic-ischemic brain injury. Clin Neurophysiol 2019; 130:1908-1916. [PMID: 31419742 PMCID: PMC6751020 DOI: 10.1016/j.clinph.2019.07.014] [Citation(s) in RCA: 44] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2018] [Revised: 05/27/2019] [Accepted: 07/05/2019] [Indexed: 01/17/2023]
Abstract
OBJECTIVE Electroencephalogram (EEG) reactivity is a robust predictor of neurological recovery after cardiac arrest, however interrater-agreement among electroencephalographers is limited. We sought to evaluate the performance of machine learning methods using EEG reactivity data to predict good long-term outcomes in hypoxic-ischemic brain injury. METHODS We retrospectively reviewed clinical and EEG data of comatose cardiac arrest subjects. Electroencephalogram reactivity was tested within 72 h from cardiac arrest using sound and pain stimuli. A Quantitative EEG (QEEG) reactivity method evaluated changes in QEEG features (EEG spectra, entropy, and frequency features) during the 10 s before and after each stimulation. Good outcome was defined as Cerebral Performance Category of 1-2 at six months. Performance of a random forest classifier was compared against a penalized general linear model (GLM) and expert electroencephalographer review. RESULTS Fifty subjects were included and sixteen (32%) had good outcome. Both QEEG reactivity methods had comparable performance to expert EEG reactivity assessment for good outcome prediction (mean AUC 0.8 for random forest vs. 0.69 for GLM vs. 0.69 for expert review, respectively; p non-significant). CONCLUSIONS Machine-learning models utilizing quantitative EEG reactivity data can predict long-term outcome after cardiac arrest. SIGNIFICANCE A quantitative approach to EEG reactivity assessment may support prognostication in cardiac arrest.
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Affiliation(s)
- Edilberto Amorim
- Department of Neurology, Massachusetts General Hospital, Boston, MA, USA; Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, USA.
| | | | | | - Mohammad M Ghassemi
- Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Jin Jing
- Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
| | - Una-May O'Reilly
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, USA
| | | | - Jong Woo Lee
- Department of Neurology, Brigham and Women's Hospital, Boston, MA, USA
| | - Sydney S Cash
- Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
| | - M Brandon Westover
- Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
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12
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Brázdil M, Doležalová I, Koritáková E, Chládek J, Roman R, Pail M, Jurák P, Shaw DJ, Chrastina J. EEG Reactivity Predicts Individual Efficacy of Vagal Nerve Stimulation in Intractable Epileptics. Front Neurol 2019; 10:392. [PMID: 31118916 PMCID: PMC6507513 DOI: 10.3389/fneur.2019.00392] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2018] [Accepted: 04/01/2019] [Indexed: 01/20/2023] Open
Abstract
Background: Chronic vagal nerve stimulation (VNS) is a well-established non-pharmacological treatment option for drug-resistant epilepsy. This study sought to develop a statistical model for prediction of VNS efficacy. We hypothesized that reactivity of the electroencephalogram (EEG) to external stimuli measured during routine preoperative evaluation differs between VNS responders and non-responders. Materials and Methods: Power spectral analyses were computed retrospectively on pre-operative EEG recordings from 60 epileptic patients with VNS. Thirty five responders and 25 non-responders were compared on the relative power values in four standard frequency bands and eight conditions of clinical assessment-eyes opening/closing, photic stimulation, and hyperventilation. Using logistic regression, groups of electrodes within anatomical areas identified as maximally discriminative by n leave-one-out iterations were used to classify patients. The reliability of the predictive model was verified with an independent data-set from 22 additional patients. Results: Power spectral analyses revealed significant differences in EEG reactivity between responders and non-responders; specifically, the dynamics of alpha and gamma activity strongly reflected VNS efficacy. Using individual EEG reactivity to develop and validate a predictive model, we discriminated between responders and non-responders with 86% accuracy, 83% sensitivity, and 90% specificity. Conclusion: We present a new statistical model with which EEG reactivity to external stimuli during routine presurgical evaluation can be seen as a promising avenue for the identification of patients with favorable VNS outcome. This novel method for the prediction of VNS efficacy might represent a breakthrough in the management of drug-resistant epilepsy, with wide-reaching medical and economic implications.
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Affiliation(s)
- Milan Brázdil
- Departments of Neurology and Neurosurgery, Medical Faculty of Masaryk University, Brno Epilepsy Center, St. Anne's University Hospital, Brno, Czechia.,Behavioral and Social Neuroscience Research Group, CEITEC-Central European Institute of Technology, Masaryk University, Brno, Czechia
| | - Irena Doležalová
- Departments of Neurology and Neurosurgery, Medical Faculty of Masaryk University, Brno Epilepsy Center, St. Anne's University Hospital, Brno, Czechia
| | - Eva Koritáková
- Institute of Biostatistics and Analyses, Faculty of Medicine, Masaryk University, Brno, Czechia
| | - Jan Chládek
- Behavioral and Social Neuroscience Research Group, CEITEC-Central European Institute of Technology, Masaryk University, Brno, Czechia.,Institute of Scientific Instruments, The Czech Academy of Sciences, Brno, Czechia
| | - Robert Roman
- Behavioral and Social Neuroscience Research Group, CEITEC-Central European Institute of Technology, Masaryk University, Brno, Czechia
| | - Martin Pail
- Departments of Neurology and Neurosurgery, Medical Faculty of Masaryk University, Brno Epilepsy Center, St. Anne's University Hospital, Brno, Czechia
| | - Pavel Jurák
- Institute of Scientific Instruments, The Czech Academy of Sciences, Brno, Czechia
| | - Daniel J Shaw
- Behavioral and Social Neuroscience Research Group, CEITEC-Central European Institute of Technology, Masaryk University, Brno, Czechia
| | - Jan Chrastina
- Departments of Neurology and Neurosurgery, Medical Faculty of Masaryk University, Brno Epilepsy Center, St. Anne's University Hospital, Brno, Czechia
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Azabou E, Navarro V, Kubis N, Gavaret M, Heming N, Cariou A, Annane D, Lofaso F, Naccache L, Sharshar T. Value and mechanisms of EEG reactivity in the prognosis of patients with impaired consciousness: a systematic review. Crit Care 2018; 22:184. [PMID: 30071861 PMCID: PMC6091014 DOI: 10.1186/s13054-018-2104-z] [Citation(s) in RCA: 54] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/13/2017] [Accepted: 06/22/2018] [Indexed: 12/21/2022]
Abstract
Background Electroencephalography (EEG) is a well-established tool for assessing brain function that is available at the bedside in the intensive care unit (ICU). This review aims to discuss the relevance of electroencephalographic reactivity (EEG-R) in patients with impaired consciousness and to describe the neurophysiological mechanisms involved. Methods We conducted a systematic search of the term “EEG reactivity and coma” using the PubMed database. The search encompassed articles published from inception to March 2018 and produced 202 articles, of which 42 were deemed relevant, assessing the importance of EEG-R in relationship to outcomes in patients with impaired consciousness, and were therefore included in this review. Results Although definitions, characteristics and methods used to assess EEG-R are heterogeneous, several studies underline that a lack of EEG-R is associated with mortality and unfavorable outcome in patients with impaired consciousness. However, preserved EEG-R is linked to better odds of survival. Exploring EEG-R to nociceptive, auditory, and visual stimuli enables a noninvasive trimodal functional assessment of peripheral and central sensory ascending pathways that project to the brainstem, the thalamus and the cerebral cortex. A lack of EEG-R in patients with impaired consciousness may result from altered modulation of thalamocortical loop activity by afferent sensory input due to neural impairment. Assessing EEG-R is a valuable tool for the diagnosis and outcome prediction of severe brain dysfunction in critically ill patients. Conclusions This review emphasizes that whatever the etiology, patients with impaired consciousness featuring a reactive electroencephalogram are more likely to have a favorable outcome, whereas those with a nonreactive electroencephalogram are prone to having an unfavorable outcome. EEG-R is therefore a valuable prognostic parameter and warrants a rigorous assessment. However, current assessment methods are heterogeneous, and no consensus exists. Standardization of stimulation and interpretation methods is needed.
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Affiliation(s)
- Eric Azabou
- Department of Physiology and Department of Critical Care Medicine, Raymond Poincaré Hospital, Assistance Publique - Hôpitaux de Paris (AP-HP), Inserm UMR 1173 Infection and Inflammation, University of Versailles Saint Quentin (UVSQ), University Paris-Saclay, Garches, Paris, France. .,Clinical Neurophysiology Unit, Raymond Poincaré Hospital - Assistance - Publique Hôpitaux de Paris, INSERM U1173, University of Versailles-Saint Quentin (UVSQ), 104 Boulevard Raymond Poincaré, Garches, 92380, Paris, France.
| | - Vincent Navarro
- Department of Clinical Neurophysiology, Pitié-Salpêtrière Hospital, AP-HP, Inserm UMRS 1127, CNRS UMR 7225, Sorbonne Universities, Université Pierre et Marie Curie - UPMC Université Paris 06, Paris, France
| | - Nathalie Kubis
- Department of Clinical Physiology, Lariboisière Hospital, AP-HP, Inserm U965, University of Paris Diderot, Sorbonne Paris Cité, Paris, France
| | - Martine Gavaret
- Department of Clinical Neurophysiology, Sainte-Anne Hospital, Inserm U894, University Paris-Descartes, Paris, France
| | - Nicholas Heming
- Department of Physiology and Department of Critical Care Medicine, Raymond Poincaré Hospital, Assistance Publique - Hôpitaux de Paris (AP-HP), Inserm UMR 1173 Infection and Inflammation, University of Versailles Saint Quentin (UVSQ), University Paris-Saclay, Garches, Paris, France
| | - Alain Cariou
- Medical ICU, Cochin Hospital, AP-HP, Paris Cardiovascular Research Center, INSERM U970, Université Paris Descartes Sorbonne Paris Cité, Paris, France
| | - Djillali Annane
- Department of Physiology and Department of Critical Care Medicine, Raymond Poincaré Hospital, Assistance Publique - Hôpitaux de Paris (AP-HP), Inserm UMR 1173 Infection and Inflammation, University of Versailles Saint Quentin (UVSQ), University Paris-Saclay, Garches, Paris, France
| | - Fréderic Lofaso
- Department of Physiology and Department of Critical Care Medicine, Raymond Poincaré Hospital, Assistance Publique - Hôpitaux de Paris (AP-HP), Inserm UMR 1173 Infection and Inflammation, University of Versailles Saint Quentin (UVSQ), University Paris-Saclay, Garches, Paris, France
| | - Lionel Naccache
- Department of Clinical Neurophysiology, Pitié-Salpêtrière Hospital, AP-HP, Inserm UMRS 1127, CNRS UMR 7225, Sorbonne Universities, Université Pierre et Marie Curie - UPMC Université Paris 06, Paris, France
| | - Tarek Sharshar
- Department of Neuro-Intensive Care Medicine, Sainte-Anne Hospital, Paris-Descartes University, Paris, France
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14
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Johnsen B, Nøhr KB, Duez CHV, Ebbesen MQ. The Nature of EEG Reactivity to Light, Sound, and Pain Stimulation in Neurosurgical Comatose Patients Evaluated by a Quantitative Method. Clin EEG Neurosci 2017; 48:428-437. [PMID: 28844160 DOI: 10.1177/1550059417726475] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
EEG reactivity (EEG-R) is regarded as an important parameter in coma prognosis but knowledge is sparse on the nature of EEG changes due to different kinds of stimulation and their prognostic significance. EEG-R was quantified in a study of 39 comatose neurosurgical patients. Six 30-second standardized visual, auditory, and painful stimulations were applied. EEG-R in the delta, theta, alpha, and beta band was normalized in z-scores as the power of a stimulation epoch relative to average power of 6 resting epochs. Outcome measure was 3 months Glasgow Outcome Scale. Increase in EEG activity was related to poor outcome, was more common (13.4% of tests), and grew continuously during the 30-second stimulation epoch. Decrease in EEG activity was related to good outcome, was rarer (2.5%), and peaked around 15 seconds. Pain was the most provocative stimulation (20.4%) followed by sound (8.7%) and eye-opening (6.7%). Discrimination between good (n = 6) and poor (n = 33) outcome was best in the theta and alpha bands for pain stimulation in the first 10-20 seconds and for sound stimulation in the first 5 to 10 seconds, eye-opening did not discriminate. Increase in activity predicted poor outcome with a high specificity 100% (CI = 52%-100%) and a modest sensitivity of 39% (CI = 23%-58%). Decrease in activity predicted good outcome with a high specificity of 100% (CI = 87%-100%) and a modest sensitivity of 33% (CI = 6%-76%). This quantitative study reveals new knowledge about the nature of EEG-R, which contribute to the development of more reliable and objective clinical procedures for outcome prediction.
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Affiliation(s)
- Birger Johnsen
- 1 Department of Clinical Neurophysiology, Aarhus University Hospital, Aarhus, Denmark
| | - Kristoffer B Nøhr
- 1 Department of Clinical Neurophysiology, Aarhus University Hospital, Aarhus, Denmark
| | - Christophe H V Duez
- 1 Department of Clinical Neurophysiology, Aarhus University Hospital, Aarhus, Denmark.,2 Research Centre for Emergency Medicine, Aarhus University, Aarhus, Denmark
| | - Mads Q Ebbesen
- 1 Department of Clinical Neurophysiology, Aarhus University Hospital, Aarhus, Denmark
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15
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Fantaneanu TA, Tolchin B, Alvarez V, Friolet R, Avery K, Scirica BM, O'Brien M, Henderson GV, Lee JW. Effect of stimulus type and temperature on EEG reactivity in cardiac arrest. Clin Neurophysiol 2016; 127:3412-3417. [PMID: 27693940 DOI: 10.1016/j.clinph.2016.09.002] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2016] [Revised: 08/26/2016] [Accepted: 09/04/2016] [Indexed: 11/29/2022]
Abstract
OBJECTIVE Electroencephalogram (EEG) background reactivity is a reliable outcome predictor in cardiac arrest patients post therapeutic hypothermia. However, there is no consensus on modality testing and prior studies reveal only fair to moderate agreement rates. The aim of this study was to explore different stimulus modalities and report interrater agreements. METHODS We studied a multicenter, prospectively collected cohort of cardiac arrest patients who underwent therapeutic hypothermia between September 2014 and December 2015. We identified patients with reactivity data and evaluated interrater agreements of different stimulus modalities tested in hypothermia and normothermia. RESULTS Of the 60 patients studied, agreement rates were moderate to substantial during hypothermia and fair to moderate during normothermia. Bilateral nipple pressure is more sensitive (80%) when compared to other modalities in eliciting a reactive background in hypothermia. Auditory, nasal tickle, nailbed pressure and nipple pressure reactivity were associated with good outcomes in both hypothermia and normothermia. CONCLUSIONS EEG reactivity varies depending on the stimulus testing modality as well as the temperature during which stimulation is performed, with nipple pressure emerging as the most sensitive during hypothermia for reactivity and outcome determination. SIGNIFICANCE This highlights the importance of multiple stimulus testing modalities in EEG reactivity determination to reduce false negatives and optimize prognostication.
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Affiliation(s)
| | | | - Vincent Alvarez
- Department of Neurology, Hopital du Valais, Sion, Switzerland; Department of Neurology, Brigham and Women's Hospital, USA.
| | - Raymond Friolet
- Intensive Care Medicine Department - Hôpital de Sion, Switzerland.
| | | | | | - Molly O'Brien
- Department of Cardiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
| | - Galen V Henderson
- Critical Care & Emergency Neurology, Department of Neurology, Brigham and Women's Hospital, USA.
| | - Jong Woo Lee
- Department of Neurology, Brigham and Women's Hospital, USA.
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16
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Estraneo A, Loreto V, Guarino I, Boemia V, Paone G, Moretta P, Trojano L. Standard EEG in diagnostic process of prolonged disorders of consciousness. Clin Neurophysiol 2016; 127:2379-85. [PMID: 27178856 DOI: 10.1016/j.clinph.2016.03.021] [Citation(s) in RCA: 51] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2016] [Revised: 03/14/2016] [Accepted: 03/22/2016] [Indexed: 12/21/2022]
Abstract
OBJECTIVE This cross-sectional study assessed the ability of standard EEG in distinguishing vegetative state (VS) from minimally conscious state plus (MCS+) or MCS minus (MCS-), and to correlate EEG features with aetiology and level of responsiveness assessed by Coma Recovery Scale-Revised (CRS-R). METHODS We analyzed background EEG activity and EEG reactivity to eye opening and closing and to tactile, acoustic, nociceptive stimuli and Intermittent Photic Stimulation (IPS) in 73 inpatients (VS=37, MCS-=11, MCS+=25), with traumatic (n=21), vascular (n=25) or anoxic (n=27) aetiology. RESULTS All patients, but one, showed abnormal background activity. EEG abnormalities were more severe in VS than in MCS+ or MCS-, and in anoxic than other aetiologies. MCS+ patients with normal or Mildly Abnormal background activity showed higher scores on CRS-R than patients with moderate to severe EEG abnormalities. Reactivity to IPS, and acoustic stimuli was significantly more frequent in MCS+ and MCS- than in VS patients. CONCLUSIONS EEG features differ between VS and MCS- or MCS+ patients and can provide evidence of relative sparing of thalamocortical connections in MCS+ patients. In anoxic patients EEG organization is more severely impaired and provides less discriminative diagnostic information. SIGNIFICANCE Conventional EEG can help clinicians to disentangle VS from MCS patients.
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Affiliation(s)
- Anna Estraneo
- Salvatore Maugeri Foundation, IRCCS, Scientific Institute of Telese Terme (BN), Via Bagni Vecchi 1, 82037 Telese Terme (BN), Italy.
| | - Vincenzo Loreto
- Salvatore Maugeri Foundation, IRCCS, Scientific Institute of Telese Terme (BN), Via Bagni Vecchi 1, 82037 Telese Terme (BN), Italy
| | - Ivan Guarino
- Salvatore Maugeri Foundation, IRCCS, Scientific Institute of Telese Terme (BN), Via Bagni Vecchi 1, 82037 Telese Terme (BN), Italy
| | - Virginia Boemia
- Salvatore Maugeri Foundation, IRCCS, Scientific Institute of Telese Terme (BN), Via Bagni Vecchi 1, 82037 Telese Terme (BN), Italy
| | - Giuseppe Paone
- Salvatore Maugeri Foundation, IRCCS, Scientific Institute of Telese Terme (BN), Via Bagni Vecchi 1, 82037 Telese Terme (BN), Italy
| | - Pasquale Moretta
- Salvatore Maugeri Foundation, IRCCS, Scientific Institute of Telese Terme (BN), Via Bagni Vecchi 1, 82037 Telese Terme (BN), Italy
| | - Luigi Trojano
- Neuropsychology Lab., Dept. of Psychology, Second University of Naples, Viale Ellittico 31, 81100 Caserta, Italy
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Azabou E, Fischer C, Mauguiere F, Vaugier I, Annane D, Sharshar T, Lofaso F. Prospective Cohort Study Evaluating the Prognostic Value of Simple EEG Parameters in Postanoxic Coma. Clin EEG Neurosci 2016; 47:75-82. [PMID: 26545818 DOI: 10.1177/1550059415612375] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/21/2015] [Accepted: 09/20/2015] [Indexed: 11/16/2022]
Abstract
We prospectively studied early bedside standard EEG characteristics in 61 acute postanoxic coma patients. Five simple EEG features, namely, isoelectric, discontinuous, nonreactive to intense auditory and nociceptive stimuli, dominant delta frequency, and occurrence of paroxysms were classified yes or no. Sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and area under the receiver operating characteristic curve (AUC) of each of these variables for predicting an unfavorable outcome, defined as death, persistent vegetative state, minimally conscious state, or severe neurological disability, as assessed 1 year after coma onset were computed as well as Synek's score. The outcome was unfavorable in 56 (91.8%) patients. Sensitivity, specificity, PPV, NPV, and AUC of nonreactive EEG for predicting an unfavorable outcome were 84%, 80%, 98%, 31%, and 0.82, respectively; and were all very close to the ones of Synek score>3, which were 82%, 80%, 98%, 29%, and 0.81, respectively. Specificities for predicting an unfavorable outcome were 100% for isoelectric, discontinuous, or dominant delta activity EEG. These 3 last features were constantly associated to unfavorable outcome. Absent EEG reactivity strongly predicted an unfavorable outcome in postanoxic coma, and performed as accurate as a Synek score>3. Analyzing characteristics of some simple EEG features may easily help nonneurophysiologist physicians to investigate prognostic issue of postanoxic coma patient. In this study (a) discontinuous, isoelectric, or delta-dominant EEG were constantly associated with unfavorable outcome and (b) nonreactive EEG performed prognostic as accurate as a Synek score>3.
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Affiliation(s)
- Eric Azabou
- Department of Physiology and Functional Explorations and Department of Critical Care Medicine, Assistance Publique- Hôpitaux de Paris (AP-HP), Raymond Poincaré Hospital, INSERM U1173, University of Versailles St Quentin (UVSQ), Garches, France Department of Clinical Neurophysiology, Hospices Civils de Lyon, Neurological Hospital of Lyon, Lyon Neuroscience Research Center (CRNL), Brain Dynamics and Cognition Team (Dycog), INSERM U1028, CNRS UMR5292, Université Lyon 1, Lyon, France
| | - Catherine Fischer
- Department of Clinical Neurophysiology, Hospices Civils de Lyon, Neurological Hospital of Lyon, Lyon Neuroscience Research Center (CRNL), Brain Dynamics and Cognition Team (Dycog), INSERM U1028, CNRS UMR5292, Université Lyon 1, Lyon, France
| | - François Mauguiere
- Department of Clinical Neurophysiology, Hospices Civils de Lyon, Neurological Hospital of Lyon, Lyon Neuroscience Research Center (CRNL), Brain Dynamics and Cognition Team (Dycog), INSERM U1028, CNRS UMR5292, Université Lyon 1, Lyon, France
| | - Isabelle Vaugier
- Department of Physiology and Functional Explorations and Department of Critical Care Medicine, Assistance Publique- Hôpitaux de Paris (AP-HP), Raymond Poincaré Hospital, INSERM U1173, University of Versailles St Quentin (UVSQ), Garches, France
| | - Djillali Annane
- Department of Physiology and Functional Explorations and Department of Critical Care Medicine, Assistance Publique- Hôpitaux de Paris (AP-HP), Raymond Poincaré Hospital, INSERM U1173, University of Versailles St Quentin (UVSQ), Garches, France
| | - Tarek Sharshar
- Department of Physiology and Functional Explorations and Department of Critical Care Medicine, Assistance Publique- Hôpitaux de Paris (AP-HP), Raymond Poincaré Hospital, INSERM U1173, University of Versailles St Quentin (UVSQ), Garches, France
| | - Fréderic Lofaso
- Department of Physiology and Functional Explorations and Department of Critical Care Medicine, Assistance Publique- Hôpitaux de Paris (AP-HP), Raymond Poincaré Hospital, INSERM U1173, University of Versailles St Quentin (UVSQ), Garches, France
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Ribeiro A, Singh R, Brunnhuber F. Clinical outcome of generalized periodic epileptiform discharges on first EEG in patients with hypoxic encephalopathy postcardiac arrest. Epilepsy Behav 2015. [PMID: 26210063 DOI: 10.1016/j.yebeh.2015.06.010] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
Abstract
INTRODUCTION The EEG, alongside clinical examination, imaging studies, and SSEPs, is used to determine the prognosis following hypoxic encephalopathy postcardiac arrest. Generalized periodic epileptiform discharges (GPEDs) are recognized as a "malignant" EEG pattern associated with very poor outcome with previous studies reporting no or few survivors. We looked at our database of cardiac arrest patients who subsequently developed GPEDs to determine clinical outcome and profile any survivors. METHODOLOGY We identified all cardiac arrest patients treated at King's College Hospital between 2011-2014 who developed hypoxic encephalopathy associated with GPEDs, BiPLEDs (bilateral periodic lateralized epileptiform discharges), and periodic discharges on first EEG. We collected clinical data including age, gender, downtime, EEG reactivity, presence of seizures or myoclonus, and outcome. Survivors were defined as patients who were discharged from the hospital to home or a neurorehabilitation unit. RESULTS Thirty-six postcardiac arrest patients with hypoxic encephalopathy were identified, 24/36 with GPEDs, and 12/36 with BiPLEDs on first EEG. The mean age of patients was 62.8 ± 14.5 years old, with 27 males (75%) and 9 females (25%). Ten of thirty-six patients survived, which is slightly higher than previously reported. Statistical tests to compare clinical characteristics between survivors and nonsurvivors demonstrated no significant differences except for trend to significance for the presence of reactivity on first EEG (p = 0.0794). On discharge, one survivor had good functional outcome (and subsequently became independent), but all others were dependent for all ADLs (activities of daily living). CONCLUSION Generalized periodic epileptiform discharges carry a grave clinical prognosis following cardiac arrest. This study did identify a higher number of survivors compared to previous studies, but most were severely disabled at hospital discharge. Reactivity of the first EEG might predict better prognosis and merit further evaluation. This article is part of a Special Issue entitled "Status Epilepticus".
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Affiliation(s)
- A Ribeiro
- Department of Clinical Neurophysiology, King's College Hospital, London, UK.
| | - R Singh
- Department of Clinical Neurophysiology, King's College Hospital, London, UK
| | - F Brunnhuber
- Department of Clinical Neurophysiology, King's College Hospital, London, UK
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Hermans MC, Westover MB, van Putten MJAM, Hirsch LJ, Gaspard N. Quantification of EEG reactivity in comatose patients. Clin Neurophysiol 2015; 127:571-580. [PMID: 26183757 DOI: 10.1016/j.clinph.2015.06.024] [Citation(s) in RCA: 44] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2015] [Revised: 06/02/2015] [Accepted: 06/05/2015] [Indexed: 12/01/2022]
Abstract
OBJECTIVE EEG reactivity is an important predictor of outcome in comatose patients. However, visual analysis of reactivity is prone to subjectivity and may benefit from quantitative approaches. METHODS In EEG segments recorded during reactivity testing in 59 comatose patients, 13 quantitative EEG parameters were used to compare the spectral characteristics of 1-minute segments before and after the onset of stimulation (spectral temporal symmetry). Reactivity was quantified with probability values estimated using combinations of these parameters. The accuracy of probability values as a reactivity classifier was evaluated against the consensus assessment of three expert clinical electroencephalographers using visual analysis. RESULTS The binary classifier assessing spectral temporal symmetry in four frequency bands (delta, theta, alpha and beta) showed best accuracy (Median AUC: 0.95) and was accompanied by substantial agreement with the individual opinion of experts (Gwet's AC1: 65-70%), at least as good as inter-expert agreement (AC1: 55%). Probability values also reflected the degree of reactivity, as measured by the inter-experts' agreement regarding reactivity for each individual case. CONCLUSION Automated quantitative EEG approaches based on probabilistic description of spectral temporal symmetry reliably quantify EEG reactivity. SIGNIFICANCE Quantitative EEG may be useful for evaluating reactivity in comatose patients, offering increased objectivity.
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Affiliation(s)
- Mathilde C Hermans
- Department of Technical Medicine, University of Twente, PO Box 217, 7500 AE Enschede, The Netherlands; Department of Neurology, Comprehensive Epilepsy Center, Yale University School of Medicine, PO Box 208018, New Haven, CT 06520-8018, USA
| | - M Brandon Westover
- Department of Neurology, Massachusetts General Hospital, Boston, MA 02114-2622, USA
| | - Michel J A M van Putten
- Department of Neurology and Clinical Neurophysiology, Medisch Spectrum Twente and Clinical Neurophysiology Group, University of Twente, PO Box 217, 7500 AE Enschede, The Netherlands
| | - Lawrence J Hirsch
- Department of Neurology, Comprehensive Epilepsy Center, Yale University School of Medicine, PO Box 208018, New Haven, CT 06520-8018, USA
| | - Nicolas Gaspard
- Department of Neurology, Comprehensive Epilepsy Center, Yale University School of Medicine, PO Box 208018, New Haven, CT 06520-8018, USA; Department of Neurology, Comprehensive Epilepsy Center, Université Libre de Bruxelles - Hôpital Erasme, Route de Lennik, 808, 1070 Bruxelles, Belgium
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Buyck I, Wiersema JR. State-related electroencephalographic deviances in attention deficit hyperactivity disorder. Res Dev Disabil 2014; 35:3217-3225. [PMID: 25178704 DOI: 10.1016/j.ridd.2014.08.003] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/12/2014] [Revised: 08/04/2014] [Accepted: 08/04/2014] [Indexed: 06/03/2023]
Abstract
This study investigated the stability and state-related characteristics of electroencephalographic (EEG) deviances in attention-deficit/hyperactivity disorder (ADHD). Three minutes resting EEG with eyes closed and eyes open were compared between 21 children with ADHD and 29 typically developing children. Across resting conditions, children with ADHD exhibited divergent topographic distribution for theta, alpha and beta power compared to typically developing children. In addition, less alpha and theta suppression to eye opening was found in children with ADHD, but only in those without comorbid ODD/CD. Findings of the present study refer to a consistent divergence in topographic distribution in ADHD across resting state conditions, yet demonstrate that state-related factors and comorbidity may also contribute to resting EEG deviances in ADHD. The state-related findings are in accord with several theoretical accounts emphasizing the role of contextual and state factors defining deficits in ADHD.
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Affiliation(s)
- Inez Buyck
- Ghent University, Department of Experimental-Clinical and Health Psychology, Henri Dunantlaan 2, 9000 Gent, Belgium.
| | - Jan R Wiersema
- Ghent University, Department of Experimental-Clinical and Health Psychology, Henri Dunantlaan 2, 9000 Gent, Belgium.
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Bagnato S, Boccagni C, Sant'Angelo A, Prestandrea C, Mazzilli R, Galardi G. EEG predictors of outcome in patients with disorders of consciousness admitted for intensive rehabilitation. Clin Neurophysiol 2014; 126:959-66. [PMID: 25238957 DOI: 10.1016/j.clinph.2014.08.005] [Citation(s) in RCA: 40] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2014] [Revised: 07/17/2014] [Accepted: 08/05/2014] [Indexed: 12/31/2022]
Abstract
OBJECTIVE This study examined the prognostic value of standard EEG in patients with unresponsive wakefulness syndrome (UWS) or in a minimally conscious state (MCS). METHODS EEGs recorded at admission in 106 patients with UWS or in a MCS were analyzed retrospectively. EEG amplitude, dominant frequency, and reactivity to stimuli were correlated to patient outcomes according to the Coma Recovery Scale Revised (CRS-R). In 101 patients, data were integrated to generate a novel Amplitude-Frequency-Reactivity (AFR) scale, with scores ranging from 3 to 7. RESULTS Patients with reduced amplitudes showed less improvement in CRS-R scores at 3 months compared to patients with normal amplitudes. Delta, theta, and alpha frequencies were associated with the least, intermediate, and the greatest improvement in CRS-R scores, respectively. Patients with EEG reactivity showed greater improvements in CRS-R scores than patients without reactivity. The AFR scores for these patients were correlated with outcomes. CONCLUSIONS Reduced EEG amplitudes and delta frequencies correlated with worse clinical outcomes, while alpha frequencies and reactivity correlated with better outcomes. AFR scores allowed more delineated descriptions of outcomes in patients with normal amplitude, theta frequency, and no reactivity. SIGNIFICANCE Standard EEG descriptors are related to the 3-month outcomes in patients with disorders of consciousness.
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Affiliation(s)
- Sergio Bagnato
- Unit of Neurophysiology and Unit for Severe Acquired Brain Injury, Rehabilitation Department, Fondazione Istituto San Raffaele G. Giglio, Cefalù (PA), Italy.
| | - Cristina Boccagni
- Unit of Neurophysiology and Unit for Severe Acquired Brain Injury, Rehabilitation Department, Fondazione Istituto San Raffaele G. Giglio, Cefalù (PA), Italy
| | - Antonino Sant'Angelo
- Unit of Neurophysiology and Unit for Severe Acquired Brain Injury, Rehabilitation Department, Fondazione Istituto San Raffaele G. Giglio, Cefalù (PA), Italy
| | - Caterina Prestandrea
- Unit of Neurophysiology and Unit for Severe Acquired Brain Injury, Rehabilitation Department, Fondazione Istituto San Raffaele G. Giglio, Cefalù (PA), Italy
| | - Roberta Mazzilli
- Unit of Neurophysiology and Unit for Severe Acquired Brain Injury, Rehabilitation Department, Fondazione Istituto San Raffaele G. Giglio, Cefalù (PA), Italy
| | - Giuseppe Galardi
- Unit of Neurophysiology and Unit for Severe Acquired Brain Injury, Rehabilitation Department, Fondazione Istituto San Raffaele G. Giglio, Cefalù (PA), Italy
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