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Intraoperative Raw EEG Monitoring for Anesthetic Depth Assessment in Scoliosis Correction Surgery: A Case Report

Department of Anaesthesiology and Intensive Care, Udayana University, Denpasar, Indonesia, Indonesia

Received: 1 Mar 2026; Revised: 2 Jun 2026; Accepted: 9 Jun 2026; Available online: 13 Jun 2026.
Open Access Copyright 2021 JAI (Jurnal Anestesiologi Indonesia)
Creative Commons License This work is licensed under a Creative Commons Attribution 4.0 International License.

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Abstract

Background:
General anesthesia aims to provide adequate hypnosis, analgesia, amnesia, and muscle relaxation. Conventional intraoperative monitoring mainly relies on hemodynamic parameters, which do not directly reflect brain function—the primary target organ of anesthesia. This limitation may lead to excessively light or deep anesthesia, increasing the risk of intraoperative awareness, hemodynamic instability, and postoperative neurocognitive disorders. In prolonged and highly stimulating procedures such as scoliosis correction surgery, accurate assessment of anesthetic depth is crucial. Electroencephalography (EEG) offers real-time insight into cortical activity and may improve anesthetic depth monitoring.

Case:
We report a 17-year-old female with adolescent idiopathic scoliosis (Lenke 3AN, Risser stage 5) who underwent spinal deformity correction under intraoperative monitoring. General anesthesia was maintained with propofol and remifentanil. Raw EEG monitoring using dual channels (CP3–Fpz and CP4–Fpz) was applied throughout the procedure. During induction, incision, spinal rotation/translation, and closure, EEG consistently demonstrated symmetric, dominant frontal alpha activity, corresponding with stable anesthetic depth. Anesthetic titration was guided by EEG patterns without reliance on processed EEG indices. The surgery was completed uneventfully, and the patient recovered without neurological complications.

Discussion:
EEG waveforms change in a dose-dependent manner with anesthetic agents. Dominant frontal alpha activity (alpha anteriorization) is associated with adequate hypnotic depth under propofol anesthesia, whereas excessive slowing or burst suppression may indicate overly deep anesthesia. Raw EEG monitoring provides direct neurophysiological information and may be more sensitive than processed indices such as the Bispectral Index, particularly in surgeries requiring intraoperative neurophysiological monitoring.

Conclusion:
Intraoperative raw EEG monitoring is a valuable adjunct for assessing anesthetic depth in long-duration scoliosis correction surgery. Maintaining dominant alpha activity may help prevent anesthetic overdose while preserving neurological stability.

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Monitoring the Depth of Anesthesia Using EEG in Patients Undergoing Scoliosis Correction Surgery
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Keywords: anesthetic depth; electroencephalography; general anesthesia; intraoperative monitoring; scoliosis; spinal fusion

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  1. Gaviria García V, Loaiza López D, Serna Rojas C, Ríos Arismendy S, Montoya Guevara E, Mora Lesmes JD, et al. Assessment of changes in the electrical activity of the brain during general anesthesia using portable electroencephalography. Colombian Journal of Anesthesiology. 2020 Dec 3;49(2)
  2. Faras A, Utama SP. Anesthesia Management for Scoliosis Correction Surgery at the Level of Thoracic Vertebra 4 - Lumbar Vertebra 3 with Intraoperative Neurophysiological Monitoring. JAI (Jurnal Anestesiologi Indonesia). 2024 Jul 31;16(2):191–201
  3. Hudec J, Prokopová T, Kosinová M, Gál R. Anesthesia and Perioperative Management for Surgical Correction of Neuromuscular Scoliosis in Children: A Narrative Review. J Clin Med. 2023 May 24;12(11):3651
  4. Bouafif L. Monitoring of Anesthesia by Bispectral Analysis of EEG Signals. Comput Math Methods Med. 2021 Sep 20;2021:1–13
  5. Reysner M, Reysner T, Janusz P, Kowalski G, Geisler-Wojciechowska A, Grochowicka M, et al. The Influence of Anesthesia on Neuromonitoring During Scoliosis Surgery: A Systematic Review. NeuroSci. 2024 Dec 17;5(4):693–712
  6. Hagihira S. Changes in the electroencephalogram during anaesthesia and their physiological basis. Br J Anaesth. 2015 Jul;115:i27–31
  7. Aasheim A, Rosseland LA, Leonardsen AL, Romundstad L. Depth of anesthesia monitoring in Norway—A web‐based survey. Acta Anaesthesiol Scand. 2024 Jul 29;68(6):781–7
  8. Çeviker G, Pişkin Ö, Baytar Ç, Okyay RD, Bollucuoğlu K, Alkan Canıtez M, et al. Automatic Gas Control Mode Versus Manual Minimal-flow and Medium-flow Anaesthesia in Breast Surgery: A Comparative Study. Turk J Anaesthesiol Reanim. 2025 Oct 16;
  9. He X, Li T, Wang X. Research progress on the depth of anesthesia monitoring based on the electroencephalogram. Ibrain. 2025 Mar 6;11(1):32–43
  10. Lee KH, Egan TD, Johnson KB. Raw and processed electroencephalography in modern anesthesia practice: a brief primer on select clinical applications. Korean J Anesthesiol. 2021 Dec 1;74(6):465–77
  11. Sharma NK, Shahid S, Kumar S, Sharma S, Gupta RK, Kumar N. Predicting Depth of Anesthesia using EEG Signals and Deep Convolution Network. In: The Third International Conference on Artificial Intelligence and Machine Learning Systems. New York, NY, USA: ACM; 2023. p. 1–8
  12. Hagihira S. Brain Mechanisms during Course of Anesthesia: What We Know from EEG Changes during Induction and Recovery. Front Syst Neurosci. 2017 May 29;11
  13. Sun Y, Wei C, Cui V, Xiu M, Wu A. Electroencephalography: Clinical Applications During the Perioperative Period. Front Med (Lausanne). 2020 Jun 9;7
  14. Mumtaz W, Rasheed S, Irfan A. Review of challenges associated with the EEG artifact removal methods. Biomed Signal Process Control. 2021 Jul;68:102741
  15. Li T, Huang Y, Wen P, Li Y. Accurate depth of anesthesia monitoring based on EEG signal complexity and frequency features. Brain Inform. 2024 Dec 21;11(1):28

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