Subscribe to RSS
DOI: 10.1055/s-0043-1775816
Electroencephalography as a Biomarker of Prognosis in Acute Brain Injury
Abstract
Electroencephalography (EEG) is a noninvasive tool that allows the monitoring of cerebral brain function in critically ill patients, aiding with diagnosis, management, and prognostication. Specific EEG features have shown utility in the prediction of outcomes in critically ill patients with status epilepticus, acute brain injury (ischemic stroke, intracranial hemorrhage, subarachnoid hemorrhage, and traumatic brain injury), anoxic brain injury, and toxic-metabolic encephalopathy. Studies have also found an association between particular EEG patterns and long-term functional and cognitive outcomes as well as prediction of recovery of consciousness following acute brain injury. This review summarizes these findings and demonstrates the value of utilizing EEG findings in the determination of prognosis.
Publication History
Article published online:
13 October 2023
© 2023. Thieme. All rights reserved.
Thieme Medical Publishers, Inc.
333 Seventh Avenue, 18th Floor, New York, NY 10001, USA
-
References
- 1 Hill CE, Blank LJ, Thibault D. et al. Continuous EEG is associated with favorable hospitalization outcomes for critically ill patients. Neurology 2019; 92 (01) e9-e18
- 2 Scheuer ML. Continuous EEG monitoring in the intensive care unit. Epilepsia 2002; 43 (Suppl. 03) 114-127
- 3 Friedman D, Claassen J, Hirsch LJ. Continuous electroencephalogram monitoring in the intensive care unit. Anesth Analg 2009; 109 (02) 506-523
- 4 Trinka E, Cock H, Hesdorffer D. et al. A definition and classification of status epilepticus – report of the ILAE Task Force on Classification of Status Epilepticus. Epilepsia 2015; 56 (10) 1515-1523
- 5 Bleck TP. Refractory status epilepticus. Curr Opin Crit Care 2005; 11 (02) 117-120
- 6 Shorvon S, Ferlisi M. The treatment of super-refractory status epilepticus: a critical review of available therapies and a clinical treatment protocol. Brain 2011; 134 (Pt 10): 2802-2818
- 7 Rossetti AO, Logroscino G, Milligan TA, Michaelides C, Ruffieux C, Bromfield EB. Status Epilepticus Severity Score (STESS): a tool to orient early treatment strategy. J Neurol 2008; 255 (10) 1561-1566
- 8 Leitinger M, Höller Y, Kalss G. et al. Epidemiology-based mortality score in status epilepticus (EMSE). Neurocrit Care 2015; 22 (02) 273-282
- 9 Jaitly R, Sgro JA, Towne AR, Ko D, DeLorenzo RJ. Prognostic value of EEG monitoring after status epilepticus: a prospective adult study. J Clin Neurophysiol 1997; 14 (04) 326-334
- 10 Nei M, Lee JM, Shanker VL, Sperling MR. The EEG and prognosis in status epilepticus. Epilepsia 1999; 40 (02) 157-163
- 11 Neligan A, Shorvon SD. Frequency and prognosis of convulsive status epilepticus of different causes: a systematic review. Arch Neurol 2010; 67 (08) 931-940
- 12 Alvarez V, Drislane FW, Westover MB, Dworetzky BA, Lee JW. Characteristics and role in outcome prediction of continuous EEG after status epilepticus: a prospective observational cohort. Epilepsia 2015; 56 (06) 933-941
- 13 Giovannini G, Monti G, Tondelli M. et al. Mortality, morbidity and refractoriness prediction in status epilepticus: comparison of STESS and EMSE scores. Seizure 2017; 46: 31-37
- 14 Yuan F, Damien C, Gaspard N. Prognostic scores in status epilepticus: a systematic review and meta-analysis. Epilepsia 2023; 64 (01) 17-28
- 15 Baysal-Kirac L, Cakar MM, Altiokka-Uzun G, Guncan Z, Guldiken B. Electroclinical patterns in patients with nonconvulsive status epilepticus: etiology, treatment, and outcome. Epilepsy Behav 2021; 114 (Pt A): 107611
- 16 Hocker SE, Britton JW, Mandrekar JN, Wijdicks EFM, Rabinstein AA. Predictors of outcome in refractory status epilepticus. JAMA Neurol 2013; 70 (01) 72-77
- 17 Jose J, Keni RR, Hassan H. et al. Predictors of outcome in super refractory status epilepticus. Epilepsy Behav 2021; 118: 107929
- 18 Tian F, Su Y, Chen W. et al. RSE prediction by EEG patterns in adult GCSE patients. Epilepsy Res 2013; 105 (1-2): 174-182
- 19 Thompson SA, Hantus S. Highly epileptiform bursts are associated with seizure recurrence. J Clin Neurophysiol 2016; 33 (01) 66-71
- 20 Johnson EL, Martinez NC, Ritzl EK. EEG characteristics of successful burst suppression for refractory status epilepticus. Neurocrit Care 2016; 25 (03) 407-414
- 21 Rubin DB, Angelini B, Shoukat M. et al. Electrographic predictors of successful weaning from anaesthetics in refractory status epilepticus. Brain 2020; 143 (04) 1143-1157
- 22 Carrera E, Claassen J, Oddo M, Emerson RG, Mayer SA, Hirsch LJ. Continuous electroencephalographic monitoring in critically ill patients with central nervous system infections. Arch Neurol 2008; 65 (12) 1612-1618
- 23 Chatrian GE, Shaw CM, Leffman H. The significance of periodic lateralized epileptiform discharges in EEG: an electrographic, clinical and pathological study. Electroencephalogr Clin Neurophysiol 1964; 17 (17) 177-193
- 24 Sutter R, Kaplan PW, Cervenka MC. et al. Electroencephalography for diagnosis and prognosis of acute encephalitis. Clin Neurophysiol 2015; 126 (08) 1524-1531
- 25 Misra UK, Kalita J. Seizures in encephalitis: predictors and outcome. Seizure 2009; 18 (08) 583-587
- 26 Dhakar MB, Sheikh Z, Kumari P. et al. Epileptiform abnormalities in acute ischemic stroke: impact on clinical management and outcomes. J Clin Neurophysiol 2022; 39 (06) 446-452
- 27 Lima FO, Ricardo JAG, Coan AC, Soriano DC, Avelar WM, Min LL. Electroencephalography patterns and prognosis in acute ischemic stroke. Cerebrovasc Dis 2017; 44 (3-4): 128-134
- 28 Crepeau AZ, Kerrigan JF, Gerber P. et al. Rhythmical and periodic EEG patterns do not predict short-term outcome in critically ill patients with subarachnoid hemorrhage. J Clin Neurophysiol 2013; 30 (03) 247-254
- 29 Claassen J, Hirsch LJ, Frontera JA. et al. Prognostic significance of continuous EEG monitoring in patients with poor-grade subarachnoid hemorrhage. Neurocrit Care 2006; 4 (02) 103-112
- 30 De Marchis GM, Pugin D, Meyers E. et al. Seizure burden in subarachnoid hemorrhage associated with functional and cognitive outcome. Neurology 2016; 86 (03) 253-260
- 31 Claassen J, Jetté N, Chum F. et al. Electrographic seizures and periodic discharges after intracerebral hemorrhage. Neurology 2007; 69 (13) 1356-1365
- 32 Vespa PM, Nuwer MR, Nenov V. et al. Increased incidence and impact of nonconvulsive and convulsive seizures after traumatic brain injury as detected by continuous electroencephalographic monitoring. J Neurosurg 1999; 91 (05) 750-760
- 33 Vespa PM, Miller C, McArthur D. et al. Nonconvulsive electrographic seizures after traumatic brain injury result in a delayed, prolonged increase in intracranial pressure and metabolic crisis. Crit Care Med 2007; 35 (12) 2830-2836
- 34 Vespa P, Tubi M, Claassen J. et al. Metabolic crisis occurs with seizures and periodic discharges after brain trauma. Ann Neurol 2016; 79 (04) 579-590
- 35 Hirsch LJ, LaRoche SM, Gaspard N. et al. American Clinical Neurophysiology Society's Standardized Critical Care EEG Terminology: 2012 version. J Clin Neurophysiol 2013; 30 (01) 1-27
- 36 Lee H, Mizrahi MA, Hartings JA. et al. Continuous electroencephalography after moderate to severe traumatic brain injury. Criti Care Med 2019; 47 (04) 574-582
- 37 Tabaeizadeh M, Aboul Nour H, Shoukat M. et al. Burden of epileptiform activity predicts discharge neurologic outcomes in severe acute ischemic stroke. Neurocrit Care 2020; 32 (03) 697-706
- 38 Zafar SF, Postma EN, Biswal S. et al. Effect of epileptiform abnormality burden on neurologic outcome and antiepileptic drug management after subarachnoid hemorrhage. Clin Neurophysiol 2018; 129 (11) 2219-2227
- 39 Zafar SF, Rosenthal ES, Jing J. et al. Automated annotation of epileptiform burden and its association with outcomes. Ann Neurol 2021; 90 (02) 300-311
- 40 Payne ET, Zhao XY, Frndova H. et al. Seizure burden is independently associated with short term outcome in critically ill children. Brain 2014; 137 (Pt 5): 1429-1438
- 41 Witsch J, Frey HP, Schmidt JM. et al. Electroencephalographic periodic discharges and frequency-dependent brain tissue hypoxia in acute brain injury. JAMA Neurol 2017; 74 (03) 301-309
- 42 Song JL, Kim JA, Struck AF, Zhang R, Westover MB. A model of metabolic supply-demand mismatch leading to secondary brain injury. J Neurophysiol 2021; 126 (02) 653-667
- 43 Jabbarli R, Pierscianek D, Rölz R. et al. Endovascular treatment of cerebral vasospasm after subarachnoid hemorrhage: more is more. Neurology 2019; 93 (05) e458-e466
- 44 Sharbrough FW, Messick Jr JM, Sundt Jr TM. Correlation of continuous electroencephalograms with cerebral blood flow measurements during carotid endarterectomy. Stroke 1973; 4 (04) 674-683
- 45 Vespa PM, Nuwer MR, Juhász C. et al. Early detection of vasospasm after acute subarachnoid hemorrhage using continuous EEG ICU monitoring. Electroencephalogr Clin Neurophysiol 1997; 103 (06) 607-615
- 46 Rots ML, van Putten MJAM, Hoedemaekers CWE, Horn J. Continuous EEG monitoring for early detection of delayed cerebral ischemia in subarachnoid hemorrhage: a pilot study. Neurocrit Care 2016; 24 (02) 207-216
- 47 Rosenthal ES, Biswal S, Zafar SF. et al. Continuous electroencephalography predicts delayed cerebral ischemia after subarachnoid hemorrhage: a prospective study of diagnostic accuracy. Ann Neurol 2018; 83 (05) 958-969
- 48 Claassen J, Hirsch LJ, Kreiter KT. et al. Quantitative continuous EEG for detecting delayed cerebral ischemia in patients with poor-grade subarachnoid hemorrhage. Clin Neurophysiol 2004; 115 (12) 2699-2710
- 49 Gollwitzer S, Groemer T, Rampp S. et al. Early prediction of delayed cerebral ischemia in subarachnoid hemorrhage based on quantitative EEG: a prospective study in adults. Clin Neurophysiol 2015; 126 (08) 1514-1523
- 50 Yu Z, Wen D, Zheng J. et al. Predictive accuracy of alpha-delta ratio on quantitative electroencephalography for delayed cerebral ischemia in patients with aneurysmal subarachnoid hemorrhage: meta-analysis. World Neurosurg 2019; 126: e510-e516
- 51 Balança B, Dailler F, Boulogne S. et al. Diagnostic accuracy of quantitative EEG to detect delayed cerebral ischemia after subarachnoid hemorrhage: a preliminary study. Clin Neurophysiol 2018; 129 (09) 1926-1936
- 52 Kim JA, Rosenthal ES, Biswal S. et al. Epileptiform abnormalities predict delayed cerebral ischemia in subarachnoid hemorrhage. Clin Neurophysiol 2017; 128 (06) 1091-1099
- 53 Struck AF, Westover MB, Hall LT, Deck GM, Cole AJ, Rosenthal ES. Metabolic correlates of the ictal-interictal continuum: FDG-PET during continuous EEG. Neurocrit Care 2016; 24 (03) 324-331
- 54 Connolly ES, Rabinstein AA, Carhuapoma RJ. et al; On Behalf of the American Heart Association Stroke Council, Council on Cardiovascular Radiology and Intervention, Council on Cardiovascular Nursing, Council on Cardiovascular Surgery and Anesthesia, and Council on Clinical Cardiology. Guidelines for the management of aneurysmal subarachnoid hemorrhage: a guideline for healthcare professionals from the American Heart Association/American Stroke Association. Stroke 2012; 43: 1711-1737
- 55 Nolan JP, Sandroni C, Böttiger BW. et al. European Resuscitation Council and European Society of Intensive Care Medicine Guidelines 2021: post-resuscitation care. Resuscitation 2021; 161 (04) 220-269
- 56 Jøogensen EO, Malchow-Møller A. Natural history of global and critical brain ischaemia. Part III: cerebral prognostic signs after cardiopulmonary resuscitation. Cerebral recovery course and rate during the first year after global and critical ischaemia monitored and predicted by EEG and neurological signs. Resuscitation 1981; 9 (02) 175-188
- 57 Sandroni C, D'Arrigo S, Cacciola S. et al. Prediction of good neurological outcome in comatose survivors of cardiac arrest: a systematic review. Intensive Care Med 2022; 48 (04) 389-413
- 58 Westhall E, Rosén I, Rossetti AO. et al. Electroencephalography (EEG) for neurological prognostication after cardiac arrest and targeted temperature management; rationale and study design. BMC Neurol 2014; 14: 159
- 59 Hofmeijer J, Beernink TM, Bosch FH, Beishuizen A, Tjepkema-Cloostermans MC, van Putten MJ. Early EEG contributes to multimodal outcome prediction of postanoxic coma. Neurology 2015; 85 (02) 137-143
- 60 Sandroni C, D'Arrigo S, Cacciola S. et al. Prediction of poor neurological outcome in comatose survivors of cardiac arrest: a systematic review. Intensive Care Med 2020; 46 (10) 1803-1851
- 61 Hofmeijer J, Tjepkema-Cloostermans MC, van Putten MJAM. Burst-suppression with identical bursts: a distinct EEG pattern with poor outcome in postanoxic coma. Clin Neurophysiol 2014; 125 (05) 947-954
- 62 Hirsch LJ, Fong MWK, Leitinger M. et al. American Clinical Neurophysiology Society's Standardized Critical Care EEG Terminology: 2021 Version. J Clin Neurophysiol 2021; 38 (01) 1-29
- 63 Amorim E, Gilmore EJ, Abend NS. et al. EEG reactivity evaluation practices for adult and pediatric hypoxic-ischemic coma prognostication in North America. J Clin Neurophysiol 2018; 35 (06) 510-514
- 64 Dragancea I, Backman S, Westhall E, Rundgren M, Friberg H, Cronberg T. Outcome following postanoxic status epilepticus in patients with targeted temperature management after cardiac arrest. Epilepsy Behav 2015; 49: 173-177
- 65 Ruijter BJ, Keijzer HM, Tjepkema-Cloostermans MC. et al; TELSTAR Investigators. Treating rhythmic and periodic EEG patterns in comatose survivors of cardiac arrest. N Engl J Med 2022; 386 (08) 724-734
- 66 Rossetti AO, Logroscino G, Liaudet L. et al. Status epilepticus: an independent outcome predictor after cerebral anoxia. Neurology 2007; 69 (03) 255-260
- 67 Kaplan PW. The EEG in metabolic encephalopathy and coma. J Clin Neurophysiol 2004; 21 (05) 307-318
- 68 Young GB, Leung LS, Campbell V, DeMelo J, Schieven J, Tilsworth R. The Electroencephalogram in Metabolic/Toxic Coma. American Journal of EEG Technology 1992; 32 (04) 243-259
- 69 Kimchi EY, Neelagiri A, Whitt W. et al. Clinical EEG slowing correlates with delirium severity and predicts poor clinical outcomes. Neurology 2019; 93 (13) e1260-e1271
- 70 Tesh RA, Sun H, Jing J. et al. VE-CAM-S: visual EEG-based grading of delirium severity and associations with clinical outcomes. Crit Care Explor 2022; 4 (01) e0611
- 71 Kaplan PW, Rossetti AO. EEG patterns and imaging correlations in encephalopathy: encephalopathy part II. J Clin Neurophysiol 2011; 28 (03) 233-251
- 72 Bersagliere A, Raduazzo ID, Schiff S. et al. Ammonia-related changes in cerebral electrogenesis in healthy subjects and patients with cirrhosis. Clin Neurophysiol 2013; 124 (03) 492-496
- 73 Marchetti P, D'Avanzo C, Orsato R. et al. Electroencephalography in patients with cirrhosis. Gastroenterology 2011; 141 (05) 1680-9.e1 , 2
- 74 Amodio P, Del Piccolo F, Pettenò E. et al. Prevalence and prognostic value of quantified electroencephalogram (EEG) alterations in cirrhotic patients. J Hepatol 2001; 35 (01) 37-45
- 75 Montagnese S, De Rui M, Schiff S. et al. Prognostic benefit of the addition of a quantitative index of hepatic encephalopathy to the MELD score: the MELD-EEG. Liver Int 2015; 35 (01) 58-64
- 76 Young GB, Bolton CF, Archibald YM, Austin TW, Wells GA. The electroencephalogram in sepsis-associated encephalopathy. J Clin Neurophysiol 1992; 9 (01) 145-152
- 77 Synek VM. Prognostically important EEG coma patterns in diffuse anoxic and traumatic encephalopathies in adults. J Clin Neurophysiol 1988; 5 (02) 161-174
- 78 Azabou E, Magalhaes E, Braconnier A. et al; Groupe d'Explorations Neurologiques en Réanimation (GENER). Early standard electroencephalogram abnormalities predict mortality in septic intensive care unit patients. PLoS One 2015; 10 (10) e0139969
- 79 Gilmore EJ, Gaspard N, Choi HA. et al. Acute brain failure in severe sepsis: a prospective study in the medical intensive care unit utilizing continuous EEG monitoring. Intensive Care Med 2015; 41 (04) 686-694
- 80 Beuchat I, Danish HH, Rubin DB. et al. EEG findings in CAR T-cell-associated neurotoxicity: clinical and radiological correlations. Neuro-oncol 2022; 24 (02) 313-325
- 81 Gust J, Annesley CE, Gardner RA, Bozarth X. EEG correlates of delirium in children and young adults with CD19-directed CAR T cell treatment-related neurotoxicity. J Clin Neurophysiol 2021; 38 (02) 135-142
- 82 Payne LE, Gagnon DJ, Riker RR. et al. Cefepime-induced neurotoxicity: a systematic review. Crit Care 2017; 21 (01) 276
- 83 Li HT, Lee CH, Wu T. et al. Clinical, electroencephalographic features and prognostic factors of cefepime-induced neurotoxicity: a retrospective study. Neurocrit Care 2019; 31 (02) 329-337
- 84 Galovic M, Döhler N, Erdélyi-Canavese B. et al. Prediction of late seizures after ischaemic stroke with a novel prognostic model (the SeLECT score): a multivariable prediction model development and validation study. Lancet Neurol 2018; 17 (02) 143-152
- 85 Haapaniemi E, Strbian D, Rossi C. et al. The CAVE score for predicting late seizures after intracerebral hemorrhage. Stroke 2014; 45 (07) 1971-1976
- 86 Rubinos C, Waters B, Hirsch LJ. Predicting and Treating Post-traumatic Epilepsy. Curr Treat Options Neurol 2022; 24 (09) 365-381
- 87 De Reuck J, Goethals M, Claeys I, Van Maele G, De Clerck M. EEG findings after a cerebral territorial infarct in patients who develop early- and late-onset seizures. Eur Neurol 2006; 55 (04) 209-213
- 88 Roseman E, Woodhall B. The electro-encephalogram in war wounds of the brain; with particular reference to post-traumatic epilepsy. Res. Publs. Assoc. Res. Nerv. Ment. Dis. Proc 1946; 25: 201-219
- 89 Kim JA, Boyle EJ, Wu AC. et al. Epileptiform activity in traumatic brain injury predicts post-traumatic epilepsy. Ann Neurol 2018; 83 (04) 858-862
- 90 Kong THJ, Abdul Azeem M, Naeem A, Allen S, Kim JA, Struck AF. Epileptiform activity predicts epileptogenesis in cerebral hemorrhage. Ann Clin Transl Neurol 2022; 9 (09) 1475-1480
- 91 Chen Y, Li S, Ge W. et al. Quantitative epileptiform burden and electroencephalography background features predict post-traumatic epilepsy. J Neurol Neurosurg Psychiatry 2023; 94 (03) 245-249
- 92 Bentes C, Martins H, Peralta AR. et al. Early EEG predicts poststroke epilepsy. Epilepsia Open 2018; 3 (02) 203-212
- 93 Punia V, Fitzgerald Z, Zhang X. et al. Electroencephalographic biomarkers of epilepsy development in patients with acute brain injury: a matched, parallel cohort study. Ann Clin Transl Neurol 2019; 6 (11) 2230-2239
- 94 Punia V, Ellison L, Bena J. et al. Acute epileptiform abnormalities are the primary predictors of post-stroke epilepsy: a matched, case-control study. Ann Clin Transl Neurol 2022; 9 (04) 558-563
- 95 Angeleri F, Majkowski J, Cacchiò G. et al. Posttraumatic epilepsy risk factors: one-year prospective study after head injury. Epilepsia 1999; 40 (09) 1222-1230
- 96 Punia V, Bena J, Krishnan B, Newey C, Hantus S. New onset epilepsy among patients with periodic discharges on continuous electroencephalographic monitoring. Epilepsia 2018; 59 (08) 1612-1620
- 97 Struck AF, Ustun B, Ruiz AR. et al. Association of an electroencephalography-based risk score with seizure probability in hospitalized patients. JAMA Neurol 2017; 74 (12) 1419-1424
- 98 Sheorajpanday RVA, Nagels G, Weeren AJTM, De Deyn PP. Quantitative EEG in ischemic stroke: correlation with infarct volume and functional status in posterior circulation and lacunar syndromes. Clin Neurophysiol 2011; 122 (05) 884-890
- 99 Bentes C, Peralta AR, Martins H. et al. Seizures, electroencephalographic abnormalities, and outcome of ischemic stroke patients. Epilepsia Open 2017; 2 (04) 441-452
- 100 Cuspineda E, Machado C, Galán L. et al. QEEG prognostic value in acute stroke. Clin EEG Neurosci 2007; 38 (03) 155-160
- 101 Gur AY, Neufeld MY, Treves TA, Aronovich BD, Bornstein NM, Korczyn AD. EEG as predictor of dementia following first ischemic stroke. Acta Neurol Scand 1994; 90 (04) 263-265
- 102 Song Y, Zang DW, Jin YY. et al. Background rhythm frequency and theta power of quantitative EEG analysis: predictive biomarkers for cognitive impairment post-cerebral infarcts. Clin EEG Neurosci 2015; 46 (02) 142-146
- 103 Schleiger E, Sheikh N, Rowland T, Wong A, Read S, Finnigan S. Frontal EEG delta/alpha ratio and screening for post-stroke cognitive deficits: the power of four electrodes. Int J Psychophysiol 2014; 94 (01) 19-24
- 104 Schleiger E, Wong A, Read S, Rowland T, Finnigan S. Poststroke QEEG informs early prognostication of cognitive impairment. Psychophysiology 2017; 54 (02) 301-309
- 105 Purandare M, Ehlert AN, Vaitkevicius H, Dworetzky BA, Lee JW. The role of cEEG as a predictor of patient outcome and survival in patients with intraparenchymal hemorrhages. Seizure 2018; 61: 122-127
- 106 Steyerberg EW, Mushkudiani N, Perel P. et al. Predicting outcome after traumatic brain injury: development and international validation of prognostic scores based on admission characteristics. PLoS Med 2008; 5 (08) e165 , discussion e165
- 107 Haveman ME, Van Putten MJAM, Hom HW, Eertman-Meyer CJ, Beishuizen A, Tjepkema-Cloostermans MC. Predicting outcome in patients with moderate to severe traumatic brain injury using electroencephalography. Crit Care 2019; 23 (01) 401
- 108 Vespa PM, Boscardin WJ, Hovda DA. et al. Early and persistent impaired percent alpha variability on continuous electroencephalography monitoring as predictive of poor outcome after traumatic brain injury. J Neurosurg 2002; 97 (01) 84-92
- 109 Hebb MO, McArthur DL, Alger J. et al. Impaired percent alpha variability on continuous electroencephalography is associated with thalamic injury and predicts poor long-term outcome after human traumatic brain injury. J Neurotrauma 2007; 24 (04) 579-590
- 110 Tolonen A, Särkelä MOK, Takala RSK. et al. Quantitative EEG parameters for prediction of outcome in severe traumatic brain injury: development study. Clin EEG Neurosci 2018; 49 (04) 248-257
- 111 Kane NM, Moss TH, Curry SH, Butler SR. Quantitative electroencephalographic evaluation of non-fatal and fatal traumatic coma. Electroencephalogr Clin Neurophysiol 1998; 106 (03) 244-250
- 112 Thatcher RW, Walker RA, Gerson I, Geisler FH. EEG discriminant analyses of mild head trauma. Electroencephalogr Clin Neurophysiol 1989; 73 (02) 94-106
- 113 Sandsmark DK, Kumar MA, Woodward CS, Schmitt SE, Park S, Lim MM. Sleep features on continuous electroencephalography predict rehabilitation outcomes after severe traumatic brain injury. J Head Trauma Rehabil 2016; 31 (02) 101-107
- 114 Schiff ND, Plum F. The role of arousal and “gating” systems in the neurology of impaired consciousness. J Clin Neurophysiol 2000; 17 (05) 438-452
- 115 Giacino JT, Kalmar K, Whyte J. The JFK Coma Recovery Scale-Revised: measurement characteristics and diagnostic utility. Arch Phys Med Rehabil 2004; 85 (12) 2020-2029
- 116 Zieleniewska M, Duszyk A, Różański P, Pietrzak M, Bogotko M, Durka P. Parametric description of EEG profiles for assessment of sleep architecture in disorders of consciousness. Int J Neural Syst 2019; 29 (03) 1850049
- 117 Rossi Sebastiano D, Visani E, Panzica F. et al. Sleep patterns associated with the severity of impairment in a large cohort of patients with chronic disorders of consciousness. Clin Neurophysiol 2018; 129 (03) 687-693
- 118 Malinowska U, Chatelle C, Bruno MA, Noirhomme Q, Laureys S, Durka PJ. Electroencephalographic profiles for differentiation of disorders of consciousness. Biomed Eng Online 2013; 12 (01) 109
- 119 Landsness E, Bruno MA, Noirhomme Q. et al. Electrophysiological correlates of behavioural changes in vigilance in vegetative state and minimally conscious state. Brain 2011; 134 (Pt 8): 2222-2232
- 120 Cologan V, Drouot X, Parapatics S. et al. Sleep in the unresponsive wakefulness syndrome and minimally conscious state. J Neurotrauma 2013; 30 (05) 339-346
- 121 Schiff ND. Recovery of consciousness after brain injury: a mesocircuit hypothesis. Trends Neurosci 2010; 33 (01) 1-9
- 122 Claassen J, Doyle K, Matory A. et al. Detection of brain activation in unresponsive patients with acute brain injury. N Engl J Med 2019; 380 (26) 2497-2505
- 123 Rossi Sebastiano D, Panzica F, Visani E. et al. Significance of multiple neurophysiological measures in patients with chronic disorders of consciousness. Clin Neurophysiol 2015; 126 (03) 558-564
- 124 Laureys S, Celesia GG, Cohadon F. et al; European Task Force on Disorders of Consciousness. Unresponsive wakefulness syndrome: a new name for the vegetative state or apallic syndrome. BMC Med 2010; 8 (01) 68
- 125 Naro A, Bramanti P, Leo A. et al. Towards a method to differentiate chronic disorder of consciousness patients' awareness: the low-resolution brain electromagnetic tomography analysis. J Neurol Sci 2016; 368: 178-183
- 126 Babiloni C, Sarà M, Vecchio F. et al. Cortical sources of resting-state alpha rhythms are abnormal in persistent vegetative state patients. Clin Neurophysiol 2009; 120 (04) 719-729
- 127 Lechinger J, Bothe K, Pichler G. et al. CRS-R score in disorders of consciousness is strongly related to spectral EEG at rest. J Neurol 2013; 260 (09) 2348-2356
- 128 Hirsch LJ, Brenner RP, Drislane FW. et al. The ACNS subcommittee on research terminology for continuous EEG monitoring: proposed standardized terminology for rhythmic and periodic EEG patterns encountered in critically ill patients. J Clin Neurophysiol 2005; 22 (02) 128-135
- 129 Hirsch LJ, Fong MW, Leitinger M. et al. American Clinical Neurophysiology Society's Standardized Critical Care EEG Terminology: 2021 Version. J Clin Neurophysiol 2021; 38 (01) 1-29
- 130 Dhakar M, Sheikh Z, Desai M. et al. Developing a standardized approach to grading the level of brain dysfunction on EEG. J Clin Neurophysiol 2023; 40 (06) 553-561