Assessment of changes in the electrical activity of the brain during general anesthesia using portable electroencephalography
Abstract
Introduction: The analysis of the electrical activity of the brain using scalp electrodes with electroencephalography (EEG) could reveal the depth of anesthesia of a patient during surgery. However, conventional EEG equipment, due to its price and size, are not a practical option for the operating room and the commercial units used in surgery do not provide access to the electrical activity. The availability of low-cost portable technologies could provide for further research on the brain activity under general anesthesia and facilitate our quest for new markers of depth of anesthesia.
Objective: To assess the capabilities of a portable EEG technology to capture brain rhythms associated with the state of consciousness and the general anesthesia status of surgical patients anesthetized with propofol.
Methods: Observational, cross-sectional trial that reviewed 10 EEG recordings captured using OpenBCI portable low-cost technology, in female patients undergoing general anesthesia with propofol. The signal from the frontal electrodes was analyzed with spectral analysis and the results were compared against the reports in the literature.
Results: The signal captured with frontal electrodes, particularly α rhythm, enabled the distinction between resting with eyes closed and with eyes opened in a conscious state, and sustained anesthesia during surgery.
Conclusions: It is possible to differentiate a resting state from sustained anesthesia, replicating previous findings with conventional technologies. These results pave the way to the use of portable technologies such as the OpenBCI tool, to explore the brain dynamics during anesthesia.
References
Al-Kadi MI, Reaz MBI, Mohd Ali MA. Evolution of electroencephalogram signal analysis techniques during anesthesia. Sensors (Switzerland). 2013;13(5):6605-35. doi: https://doi.org/10.3390/s130506605.
David Whythe S, Driscoll Booker P. Monitoring depth of anaesthesia by EEG. BJA CEPD Reviews. 2003;3(4):106-10. doi: https://doi.org/10.1093/bjacepd/mkg106
García-Colmenero DIG, Zorrilla-Mendoza DJG. Electroencefalografía para el anestesiólogo, consideraciones clínicas. Rev Mexi Anest. 2018;41:39-43.
Chan MTV, Hedrick TL, Egan TD, García PS, Koch S, Purdon PL, et al. American Society for Enhanced Recovery and Perioperative quality initiative joint consensus statement on the role of neuromonitoring in perioperative outcomes: Electroencephalography. Anesth Analg. 2020;130(5):1278-91. doi: https://doi.org/10.1213/ANE.0000000000004502.
Marchant N, Sanders R, Sleigh J, Vanhaudenhuyse A, Bruno MA, Brichant JF, et al. How electroencephalography serves the anesthesiologist. Clin EEG Neurosci. 2014;45(1):22-32. doi: https://doi.org/10.1177/1550059413509801.
Buzsáki G, Anastassiou CA, Koch C. The origin of extracellular fields and currents-EEG, ECoG, LFP and spikes. Nat Rev Neurosci. 2012;13(6):407-20. doi: https://doi.org/10.1038/nrn3241.
Purdon PL, Sampson A, Pavone KJ, Brown EN. Clinical Electroencephalography for Anesthesiologists: Part I: Background and Basic Signatures. Anesthesiology. 2015;123(4):937-60. doi: https://doi.org/10.1097/ALN.0000000000000841.
Cascella M. Mechanisms underlying brain monitoring during anesthesia: Limitations, possible improvements, and perspectives. Korean Journal of Anesthesiology 2016;69:113-20. doi: https://doi.org/10.4097/kjae.2016.69.2.113.
Hagihira S. Changes in the electroencephalogram during anaesthesia and their physiological basis. British J Anaesthesia. 2015;115:27-31. doi: https://doi.org/10.1093/bja/aev212.
Gugino LD, Chabot RJ, Prichep LS, John ER, Formanek V. Quantitative EEG changes associated with loss and return of consciousness in healthy adult volunteers anaesthetized with propofol or sevourane. Br J Anaesth. 2001;87(3):421-8. doi: https://doi.org/10.1093/bja/87.3.421.
Kreuzer M. EEG based monitoring of general anesthesia: Taking the next steps. Front Comput Neurosci. 2017;11:1-7. doi: https://doi.org/10.3389/fncom.2017.00056.
Rampill IJ. A primer for EEG signal processing in anesthesia. Anesthesiology. 1998;89(4):980-1002. doi: https://doi.org/10.1097/00000542-199810000-00023.
Purdon PL, Sampson A, Pavone KJ, Brown EN. Clinical Electroencephalography for Anesthesiologists: Part I: Background and Basic Signatures. Anesthesiology. 2015;123(4):937-60. doi: https://doi.org/10.1097/ALN.0000000000000841.
Alkire MT, Hudetz AG, Tononi G. Consciousness and anesthesia. Science. 2008;322:876-80. doi: https://doi.org/10.1126/science.1149213.
Minguillón J, Morillas C, Pelayo F, López-Gordo MÁ. Sistema BCI multiusuario. Cogn Area Networks. 2017;4(1):49-53.
Di Fronso S, Fiedler P, Tamburro G, Haueisen J, Bertollo M, Comani S. Dry EEG in sports sciences: A fast and reliable tool to assess individual alpha peak frequency changes induced by physical effort. Front Neurosci. 2019;13:1-12. doi: https://doi.org/10.3389/fnins.2019.00982.
O'Sullivan M, Temko A, Bocchino A, O'Mahony C, Boylan G, Popovici E. Analysis of a low-cost EEG monitoring system and dry electrodes toward clinical use in the neonatal icu. Sensors (Switzerland). 2019;19(11). doi: https://doi.org/10.3390/s19112637.
Chang Y, Esteban D, Bustamante G, Dizon J, Pérez E, Cervantes I, et al. Feature extraction and signal processing of open-source brain-computer a signal processing tool for open BCI tool descriptions. 2nd Annual Undergraduate Research Expo. University of Texas, Dallas. 2016.
Brunner C, Andreoni G, Bianchi L, Blankertz B, Breitwieser C, Kanoh S, et al. BCI Software Platforms. 2012;303-31. doi: https://doi.org/10.1007/978-3-642-29746-5_16.
Murphy M, Bruno MA, Riedner BA, Boveroux P, Noirhomme Q, Landsness EC, et al. Propofol anesthesia and sleep: A high-density EEG study. Sleep. 2011;34(3). doi: https://doi.org/10.1093/sleep/34.3.283.
Johansson R. Numerical python: Scientific computing and data science applications with numpy, SciPy and matplotlib, Second edition. Numerical Python Scientific Computing and Data Science Applications with Numpy, SciPy and Matplotlib, Second Edition. 2018. 1-700 p. doi: https://doi.org/10.1007/978-1-4842-4246-9_1.
Welch PD. The use of fast fourier transform for the estimation of power spectra: A method based on time aver. aging over short, modified periodograms. IEE Trans audio Electroacoust. 1967;AU-15(2):70-3. doi: https://doi.org/10.1109/TAU.1967.1161901.
Glerean E, Pan RK, Salmi J, Kujala R, Lahnakoski JM, Roine U, et al. Reorganization of functionally connected brain subnetworks in high-functioning autism. Hum Brain Mapp. 2016;37(3):1066-79. doi: https://doi.org/10.1002/hbm.23084.
Percival DB, Walden AT, B PD, T WA. Spectral analysis for physical applications [Internet]. Cambridge University Press; 1993. doi: https://doi.org/10.1017/CBO9780511622762.
Manterola C, Otzen T. Bias in clinical research. Int J Morphol. 2015;33(3):1156-64. doi: https://doi.org/10.4067/S0717-95022015000300056.
Fridman I, Cordeiro M, Rais-Bahrami K, McDonald NJ, Reese JJ, Massaro AN, et al. Evaluation of dry sensors for neonatal EEG recordings. J Clin Neurophysiol. 2016;33(2):149-55. doi: https://doi.org/10.1097/WNP.0000000000000237.
Bleichner MG, Debener S. Concealed, unobtrusive ear-centered EEG acquisition: Ceegrids for transparent EEG. Front Hum Neurosci. 2017;11:1-14. doi: https://doi.org/10.3389/fnhum.2017.00163.
Neumann T, Baum AK, Baum U, Deike R, Feistner H, Hinrichs H, et al. Diagnostic and therapeutic yield of a patient-controlled portable EEG device with dry electrodes for home-monitoring neurological outpatients-rationale and protocol of the HOMEONE pilot study. Pilot Feasibility Stud. 2018;4(1). doi: https://doi.org/10.1186/s40814-018-0296-2.
Lizier JT. JIDT: An information-theoretic toolkit for studying the dynamics of complex systems. Front Robot AI. 2014;1:1-20. doi: https://doi.org/10.3389/frobt.2014.00011.
Jordan D, Stockmanns G, Kochs EF, Pilge S, Schneider G. Electroencephalographic order pattern analysis for the separation of consciousness and unconsciousness. Anesthesiology. 2008;109(6):1014-22. doi: https://doi.org/10.1097/ALN.0b013e31818d6c55.
Bruhn J, Röpcke H, Hoeft A. Approximate entropy as an electroencephalographic measure of anesthetic drug effect during desflurane anesthesia. Anesthesiology. 2000;92(3):715-26. doi: https://doi.org/10.1097/00000542-200003000-00016.
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