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Original Research

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Correlation of EEG-based brain resuscitation index and end-tidal carbon dioxide in porcine cardiac arrest model

  • Dong Sun Choi1,2,3,†
  • Heejin Kim4,†
  • Ki Jeong Hong2,5,6
  • Sang Do Shin2,6
  • Hee Chan Kim3,7
  • Yong Joo Park8
  • Tae Han Kim2,9
  • Young Sun Ro2
  • Kyoung Jun Song2,10
  • Ki Hong Kim2,10

1Department of Emergency Medicine, Uijeongbu Eulji Medical Center, 11759 Uijeongbu, Republic of Korea

2Laboratory of Emergency Medical Services, Biomedical Research Institute, Seoul National University Hospital, 03080 Seoul, Republic of Korea

3Department of Emergency Medicine, College of Medicine, Eulji University, 34824 Daejeon, Republic of Korea

4Clinical Trial Center, Seoul National University Hospital, 03080 Seoul, Republic of Korea

5Department of Emergency Medicine, Seoul National University Hospital, 03080 Seoul, Republic of Korea

6Department of Emergency Medicine, College of Medicine, Seoul National University, 03080 Seoul, Republic of Korea

7Department of Biomedical Engineering, College of Medicine, Seoul National University, 03080 Seoul, Republic of Korea

8Department of Emergency Medicine, Gyeongsang National University Changwon Hospital, 51472 Changwon, Republic of Korea

9Department of Emergency Medicine, Seoul National University Boramae Medical Center, 07061 Seoul, Republic of Korea

10Seoul National University Hospital Biomedical Research Institute, 03080 Seoul, Republic of Korea

DOI: 10.22514/sv.2021.226

Submitted: 29 June 2021 Accepted: 18 August 2021

Online publish date: 10 November 2021

*Corresponding Author(s): Ki Jeong Hong E-mail:

† These authors contributed equally.


Evaluation and monitoring perfusion of vital organs is important during resuscitation from cardiac arrest. We developed a non-invasive electroencephalogram (EEG) based brain resuscitation index (EBRI) as a physiologic indicator measuring organ perfusion during cardiopulmonary resuscitation (CPR) and evaluated the correlation of EBRI and end-tidal carbon dioxide (ETCO2). A randomized crossover experimental study using a porcine cardiac arrest model was designed. After 1 minute of untreated ventricular fibrillation, 10 periods of higher-quality CPR (compression depth 5 cm and compression rate 100/min) for 50 seconds and lower-quality CPR (compression depth 3 cm and compression rate 60/min) for 50 seconds were performed in alternation. EBRI was calculated from the single EEG channel with the lowest noise. Mixed-model analysis was conducted to compare the differences of hemodynamic parameters, ETCO2, and EBRI between higher-quality CPR periods and lower-quality CPR periods. Pearson’s correlation coefficient was calculated to assess correlation between EBRI and ETCO2. The experiment was performed on 5 female swine (44.6 ± 2.8 kg). Higher-quality CPR showed significantly higher delta EBRI (median [IQR] 0.1 [0.0–0.2]) than did lower-quality CPR (median [IQR] –0.1 [–0.2–0.0], p < 0.01). EBRI had a statistically moderate positive correlation with ETCO2 (r = 0.51). In this porcine cardiac arrest model, EBRI was successfully obtained during resuscitation and had a statistically moderate correlation with ETCO2.


Cardiopulmonary resuscitation; Electroencephalogram; End-tidal CO2

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Dong Sun Choi,Heejin Kim,Ki Jeong Hong,Sang Do Shin,Hee Chan Kim,Yong Joo Park,Tae Han Kim,Young Sun Ro,Kyoung Jun Song,Ki Hong Kim. Correlation of EEG-based brain resuscitation index and end-tidal carbon dioxide in porcine cardiac arrest model. Signa Vitae. 2021.doi:10.22514/sv.2021.226.


[1] Virani SS, Alonso A, Benjamin EJ, Bittencourt MS, Callaway CW, Carson AP, et al. Heart disease and stroke statistics-2020 update: a report from the American Heart Association. Circulation. 2020; 141: e139–e596.

[2] Benjamin EJ, Muntner P, Alonso A, Bittencourt MS, Callaway CW, Carson AP, et al. Heart disease and stroke Statistics-2019 update a report from the American Heart Association. Circulation. 2019; 139: e56–e528.

[3] Sawyer KN, Camp-Rogers TR, Kotini-Shah P, Del Rios M, Gossip MR, Moitra VK, et al. Sudden Cardiac Arrest Survivorship: a Scientific Statement from the American Heart Association. Circulation. 2020; 141: e654–e685.

[4] Daya MR, Schmicker RH, Zive DM, Rea TD, Nichol G, Buick JE, et al. Out-of-hospital cardiac arrest survival improving over time: Results from the Resuscitation Outcomes Consortium (ROC). Resuscitation. 2015; 91: 108–115.

[5] Jacobs I, Nadkarni V, Bahr J, Berg RA, Billi JE, Bossaert L, et al. Cardiac arrest and cardiopulmonary resuscitation outcome reports: update and simplification of the Utstein templates for resuscitation registries: a statement for healthcare professionals from a task force of the Interna-tional Liaison Committee on Resuscitation (American Heart Association, European Resuscitation Council, Australian Resuscitation Council, New Zealand Resuscitation Council, Heart and Stroke Foundation of Canada, InterAmerican Heart Foundation, Resuscitation Councils of Southern Africa). Circulation. 2004; 110: 3385–3397.

[6] Cummins RO, Ornato JP, Thies WH, Pepe PE. Improving survival from sudden cardiac arrest: the “chain of survival” concept. A statement for health professionals from the Advanced Cardiac Life Support Subcommittee and the Emergency Cardiac Care Committee, American Heart Association. Circulation. 1991; 83: 1832–1847.

[7] Iwami T, Nichol G, Hiraide A, Hayashi Y, Nishiuchi T, Kajino K, et al. Continuous Improvements in “Chain of Survival” Increased Survival after out-of-Hospital Cardiac Arrests. Circulation. 2009; 119: 728–734.

[8] Kleinman ME, Brennan EE, Goldberger ZD, Swor RA, Terry M, Bobrow BJ, et al. Part 5: Adult Basic Life Support and Cardiopulmonary Resus-citation Quality: 2015 American Heart Association Guidelines Update for Cardiopulmonary Resuscitation and Emergency Cardiovascular Care. Circulation. 2015; 132: S414–S435.

[9] Perkins GD, Handley AJ, Koster RW, Castrén M, Smyth MA, Olasveengen T, et al. European Resuscitation Council Guidelines for Resuscitation 2015: Section 2. Adult basic life support and automated external defibrillation. Resuscitation. 2015; 95: 81–99.

[10] Cheng A, Brown LL, Duff JP, Davidson J, Overly F, Tofil NM, et al. Improving cardiopulmonary resuscitation with a CPR feedback device and refresher simulations (CPR CARES Study): a randomized clinical trial. Journal of the American Medical Association pediatrics. 2015; 169: 137–144.

[11] Kirkbright S, Finn J, Tohira H, Bremner A, Jacobs I, Celenza A. Audiovisual feedback device use by health care professionals during CPR: a systematic review and meta-analysis of randomised and non-randomised trials. Resuscitation. 2014; 85: 460–471.

[12] Yeung J, Meeks R, Edelson D, Gao F, Soar J, Perkins GD. The use of CPR feedback/prompt devices during training and CPR performance: a systematic review. Resuscitation. 2009; 80: 743–751.

[13] Link MS, Berkow LC, Kudenchuk PJ, Halperin HR, Hess EP, Moitra VK, et al. Part 7: Adult Advanced Cardiovascular Life Support: 2015 American Heart Association Guidelines Update for Cardiopulmonary Resuscitation and Emergency Cardiovascular Care. Circulation. 2015; 132: S444–S464.

[14] Callaway CW, Soar J, Aibiki M, Böttiger BW, Brooks SC, Deakin CD, et al. Part 4: Advanced Life Support: 2015 International Consensus on Cardiopulmonary Resuscitation and Emergency Cardiovascular Care Science With Treatment Recommendations. Circulation. 2015; 132: S84–S145.

[15] Callaway CW, Donnino MW, Fink EL, Geocadin RG, Golan E, Kern KB, et al. Part 8: Post-Cardiac Arrest Care: 2015 American Heart Association Guidelines Update for Cardiopulmonary Resuscitation and Emergency Cardiovascular Care. Circulation. 2015; 132: S465–S482.

[16] Sharbrough FW, Messick JM, Sundt TM. Correlation of Continuous Electroencephalograms with Cerebral Blood Flow Measurements during Carotid Endarterectomy. Stroke. 1973; 4: 674–683.

[17] Tiainen M, Kovala TT, Takkunen OS, Roine RO. Somatosensory and brainstem auditory evoked potentials in cardiac arrest patients treated with hypothermia. Critical Care Medicine. 2005; 33: 1736–1740.

[18] England M. The Changes in Bispectral Index during a Hypovolemic Cardiac Arrest. Anesthesiology. 1999; 91: 1947–1947.

[19] Moss J, Rockoff M. EEG monitoring during cardiac arrest and resuscitation. The Journal of the American Medical Association. 1980; 244: 2750–2751.

[20] Fatovich DM, Jacobs IG, Celenza A, Paech MJ. An observational study of bispectral index monitoring for out of hospital cardiac arrest. Resuscitation. 2006; 69: 207–212.

[21] Kim TH, Kim H, Hong KJ, Shin SD, Kim HC, Park YJ, et al. Prediction of cerebral perfusion pressure during CPR using electroencephalogram in a swine model of ventricular fibrillation. The American Journal of Emergency Medicine. 2021; 45: 137–143.

[22] Idris AH, Becker LB, Ornato JP, Hedges JR, Bircher NG, Chandra NC, et al. Utstein-style guidelines for uniform reporting of laboratory CPR research: a statement for healthcare professionals from a task force of the American Heart Association, the American College of Emergency Physicians, the American College of Cardiology, the European Resuscitation Council, the Heart and Stroke Foundation of Canada, the Institute of Critical Care Medicine, the Safar Center for Resuscitation Research, and the Society for Academic Emergency Medicine. Circulation. 1996; 94: 2324–2336.

[23] Momeni M, Gaudin A. Intraoperative cerebral hypoperfusion and elec-troencephalogram suppression resulting in neurological complications after cardiac surgery: the need for an in depth investigation. Acta Anæsthesiologica Belgica. 2016; 67: 73–79.

[24] Morimoto Y, Monden Y, Ohtake K, Sakabe T, Hagihira S. The Detection of Cerebral Hypoperfusion with Bispectral Index Monitoring during General Anesthesia. Anesthesia and Analgesia. 2005; 100: 158–161.

[25] Sivaraju A, Gilmore EJ, Wira CR, Stevens A, Rampal N, Moeller JJ, et al. Prognostication of post-cardiac arrest coma: early clinical and electroencephalographic predictors of outcome. Intensive Care Medicine. 2015; 41: 1264–1272.

[26] Hofmeijer J, Tjepkema-Cloostermans MC, van Putten MJ. Burst-suppression with identical bursts: a distinct EEG pattern with poor outcome in postanoxic coma. Clinical Neurophysiology. 2014; 125: 947–954.

[27] Rampil IJ. A primer for EEG signal processing in anesthesia. Anesthesi-ology. 1998; 89: 980–1002.

[28] Bruhn J, Bouillon TW, Shafer SL. Bispectral index (BIS) and burst suppression: revealing a part of the BIS algorithm. Journal of clinical monitoring and computing. 2000; 16: 593–596.

[29] Thenayan EA, Savard M, Sharpe MD, Norton L, Young B. Electroen-cephalogram for prognosis after cardiac arrest. Journal of Critical Care. 2010; 25: 300–304.

[30] Sigl JC, Chamoun NG. An introduction to bispectral analysis for the electroencephalogram. Journal of clinical monitoring. 1994; 10: 392–404.

[31] Sheth BR, Sandkühler S, Bhattacharya J. Posterior beta and anterior gamma oscillations predict cognitive insight. Journal of Cognitive Neuroscience. 2009; 21: 1269–1279.

[32] Azim N, Wang C. The use of bispectral index during a cardiopulmonary arrest: a potential predictor of cerebral perfusion. Anaesthesia. 2004; 59: 610–612.

[33] Gudipati CV, Weil MH, Bisera J, Deshmukh HG, Rackow EC. Expired carbon dioxide: a noninvasive monitor of cardiopulmonary resuscitation. Circulation. 1988; 77: 234–239.

[34] Lewis LM, Stothert J, Standeven J, Chandel B, Kurtz M, Fortney J. Correlation of end-tidal CO_2 to cerebral perfusion during CPR. Annals of Emergency Medicine. 1992; 21: 1131–1134.

[35] Cereceda-Sánchez FJ, Molina-Mula J. Systematic Review of Capnog-raphy with Mask Ventilation during Cardiopulmonary Resuscitation Maneuvers. Journal of Clinical Medicine. 2019; 8: 358.

[36] Kodali BS, Urman RD. Capnography during cardiopulmonary resusci-tation: Current evidence and future directions. Journal of Emergencies, Trauma, and Shock. 2014; 7: 332–340.

[37] Lee HJ, Shin J, Hong KJ, Jung JH, Lee SJ, Jung E, et al. A feasibility study for the continuous measurement of pupillary response using the pupillography during CPR in out-of-hospital cardiac arrest patients. Resuscitation. 2019; 135: 80–87.

[38] Cournoyer A, Iseppon M, Chauny J, Denault A, Cossette S, Notebaert Ė. Near-infrared Spectroscopy Monitoring during Cardiac Arrest: a Systematic Review and Meta-analysis. Academic Emergency Medicine. 2016; 23: 851–862.

[39] Yagi T, Kawamorita T, Kuronuma K, Tachibana E, Watanabe K, Chiba N, et al. Usefulness of a New Device to Monitor Cerebral Blood Oxygenation Using NIRS during Cardiopulmonary Resuscitation in Patients with Cardiac Arrest: a Pilot Study. Advances in Experimental Medicine and Biology. 2020; 128: 323–329.

[40] Berka C, Levendowski DJ, Cvetinovic MM, Petrovic MM, Davis G, Lumicao MN, et al. Real-Time Analysis of EEG Indexes of Alertness, Cognition, and Memory Acquired with a Wireless EEG Headset. International Journal of Human-Computer Interaction. 2004; 17: 151–170.

[41] Gervais HW, Schleien CL, Koehler RC, Berkowitz ID, Shaffner DH, Traystman RJ. Effect of adrenergic drugs on cerebral blood flow, metabolism, and evoked potentials after delayed cardiopulmonary resuscitation in dogs. Stroke. 1991; 22: 1554–1561.

[42] Jang HS, Kwon YS, Lee MG, Jang KH. The Effect of Tile-tamine/Zolazepam (Zoletile) Combination with Xylazine or Medetomi-dine on Electroencephalograms in Dogs. Journal of Veterinary Medical Science. 2004; 66: 501–507.

[43] Martín-Cancho MF, Lima JR, Luis L, Crisóstomo V, Ezquerra LJ, Carrasco MS, et al. Bispectral index, spectral edge frequency 95

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