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The current evaluations of the levels of consciousness during anesthesia have limited precision. This can produce negative clinical consequences such as intraoperative awareness or neurological damage due to under- or over-infusion of anesthesia, respectively. The study's objective is to determine and classify biomarkers of electrical and hemodynamical brain activity associated with the levels of consciousness between wakefulness and anesthesia. For this purpose, a parietal electroencephalography (EEG) and a functional near-infrared spectroscopy (fNIRS) measurement paradigm will be used, as well as machine-learning. Volunteering patients (n = 25), who will be subject to an endoscopy procedure, will be measured during the infusion of anesthesia with propofol. EEG and fNIRS parameters will then be related to the Modified Ramsay clinical scale of consciousness.
Devices for data acquisition The brain electrical signals will be measured with four electrodes and a Cyton OpenBCI board with a sampling frequency of 250 Hz. The brain hemodynamical signals will be measured with a NIRSport with 16 optodes and a sampling frequency of 15.525 Hz and a wavelength of 760 nm. The electrodes and optodes will be positioned in the parietal zone, following the recommended 10-5 system for the multimodal EEG-fNIRS paradigm. The software Lab Streaming Layer will be used to synchronize the EEG and fNIRS data.
Data acquisition Patients will be summoned 30 minutes before their clinical appointment to put, adjust and calibrate the EEG and fNIRS systems. A 5-minute baseline measurement will be performed before the endoscopy procedure, with eyes closed. Afterward, patients will be anesthetized with a constant infusion rate of 20 mg/kg/h-1 of intravenous propofol until loss of consciousness, within 20 minutes. During infusion, the Modified Ramsay Scale will be used by the anesthetist in charge to measure the levels of consciousness, assessed by the patient's response to verbal or painful stimuli. The level of consciousness will be evaluated every two minutes until complete loss of consciousness, which is assumed after the loss of defensive or purposeful response to a second standard tetanic stimulation. After this, the endoscopy procedure will continue normally following standard clinical protocols. Before leaving, patients will be asked to answer the BRICE survey, used to evaluate the patient's experience during surgery or similar procedures. Patients will be measured throughout the whole endoscopy procedure. Any additional considerations will be managed in a case-by-case manner by the medical staff in charge.
Justification of the chosen sample size The sample size (n = 25) was determined by the Cohen Test, with a statistical power of 0.8 and an alfa power of 0.05, to determine significant intraspecific subject differences when comparing wakefulness, deep sedation, and intermediate levels of consciousness.
EEG data analysis The delta (0.1-3 Hz), theta (4-7 Hz), lower-alpha (8-12 Hz), upper-alpha (12-15 Hz), and beta/gamma (15-40 Hz) power bands will be used as features for the decoding model. The features will be calculated using a moving window of one minute. The levels of consciousness identified using the Modified Ramsay Scale will be paired with the corresponding window. The software Homer 2012: MNE in Python will be used for these analyses.
fNIRS data analysis The temporal reference of the oxygenated (HbO2) and deoxygenated (HHb) hemoglobin will be obtained from the optical signals, using the modified Beer-Lambert law. The regions of interest (ROIs) will be obtained in relation to regional local average activity. The average, maximal, and slope of the signal of each ROI will be obtained. Vector-phase analyses will also be implemented, with one-minute windows. The software Homer 2021: MNE in Python will be used for these analyses.
Classification using machine-learning For each one-minute window, the EEG and fNIRS features will be given to a Support Vector Machine (SVM) classifier using a basal radius. Each window will be attached to the level of consciousness according to the Modified Ramsay Scale. Three models will be tested: only EEG, only fNIRS, and fNIRS + EEG. Each model will try to decode the patient's level of consciousness using the aforementioned scale.
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| Measure | Description | Time Frame |
|---|---|---|
| Brain electrophysiological activity by electroencephalography wavelength band powers | The delta (0.1-3 Hz), theta (4-7 Hz), lower-alpha (8-12 Hz), upper-alpha (12-15 Hz), and beta/gamma (15-40 Hz) electroencephalography wavelength band powers will be used as features for the decoding model. | During the whole endoscopy and recovery (1 - 2 hours) |
| Temporal brain oxygenation by near-infrared light spectroscopy wavelengths | The temporal brain area of the oxygenated (HbO2) and deoxygenated (HHb) hemoglobin will be obtained from the optical signals, using the modified Beer-Lambert law. | During the whole endoscopy and recovery (1 - 2 hours) |
| Levels of Consciousness with the Modified Ramsay Sedation Scale | During infusion, the Modified Ramsay Scale will be used by the anesthetist in charge to measure the patient's level of consciousness. This scale has a total of eight levels, each of which indicates an increasing level of unconsciousness, assessed qualitatively by the patient's response to verbal or painful stimuli. The level of consciousness will be evaluated every two minutes until complete loss of consciousness, which is assumed after the loss of defensive or purposeful response to a second standard tetanic stimulation. | 20 minutes |
| Measure | Description | Time Frame |
|---|---|---|
| BRICE survey responses | Before leaving, patients will be asked to answer the BRICE survey, used to evaluate the patient's experience during surgery or similar procedures (Table 6. in Kotsovolis & Komninos, 2009). | 10 minutes |
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Inclusion Criteria:
Exclusion Criteria:
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Relatively healthy volunteering patients, within 20 to 40 years of age, who will undergo an endoscopy procedure and meet all the eligibility criteria.
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| Name | Affiliation | Role |
|---|---|---|
| Catalina A Saini | Pontificia Universidad Catolica de Chile | Principal Investigator |
| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| Centro de Especialidades Médicas UC | Santiago | Santiago Metropolitan | 8320000 | Chile |
| PubMed Identifier | Type | Citation | Retractions |
|---|---|---|---|
| 32855048 | Background | Aru J, Suzuki M, Larkum ME. Cellular Mechanisms of Conscious Processing. Trends Cogn Sci. 2020 Oct;24(10):814-825. doi: 10.1016/j.tics.2020.07.006. Epub 2020 Aug 24. | |
| 31672663 | Background | Campbell JM, Huang Z, Zhang J, Wu X, Qin P, Northoff G, Mashour GA, Hudetz AG. Pharmacologically informed machine learning approach for identifying pathological states of unconsciousness via resting-state fMRI. Neuroimage. 2020 Feb 1;206:116316. doi: 10.1016/j.neuroimage.2019.116316. Epub 2019 Oct 29. |
| Label | URL |
|---|---|
| Kothe, C. (2014). Lab streaming layer (LSL) | View source |
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The Department of Anesthesiology and the Institute of Biological and Medical Engineering (IIBM) of the Pontificia Universidad Católica de Chile, will take the necessary measurements to protect the access to your clinical information and sensible data from unauthorized people. All the obtained information will be kept confidential. The name, ID, or any other identifiable information, will be anonymized in a database. This information will be stored for five years under the responsibility of the corresponding researchers. The collected data will be published to be used in other studies related to anesthesia and consciousness. All published data will be completely anonymized. It is possible that the obtained results are presented in journals and medical or scientific conferences. However, the name and identifiable data will not be known.
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| ID | Term |
|---|---|
| D014474 | Unconsciousness |
| ID | Term |
|---|---|
| D003244 | Consciousness Disorders |
| D019954 | Neurobehavioral Manifestations |
| D009461 | Neurologic Manifestations |
| D009422 | Nervous System Diseases |
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| 19561776 | Background | Kotsovolis G, Komninos G. Awareness during anesthesia: how sure can we be that the patient is sleeping indeed? Hippokratia. 2009 Apr;13(2):83-9. |
| Background | Lee, M. H., Fazli, S., Mehnert, J., & Lee, S. W. (2015). Subject-dependent classification for robust idle state detection using multi-modal neuroimaging and data-fusion techniques in BCI. Pattern Recognition, 48(8), 2725-2737. https://doi.org/10.1016/j.patcog.2015.03.010 |
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| 15847680 | Background | Levitt DG, Schnider TW. Human physiologically based pharmacokinetic model for propofol. BMC Anesthesiol. 2005 Apr 22;5(1):4. doi: 10.1186/1471-2253-5-4. |
| 25498581 | Background | Sheahan CG, Mathews DM. Monitoring and delivery of sedation. Br J Anaesth. 2014 Dec;113 Suppl 2:ii37-47. doi: 10.1093/bja/aeu378. |
| 15333419 | Background | Sebel PS, Bowdle TA, Ghoneim MM, Rampil IJ, Padilla RE, Gan TJ, Domino KB. The incidence of awareness during anesthesia: a multicenter United States study. Anesth Analg. 2004 Sep;99(3):833-839. doi: 10.1213/01.ANE.0000130261.90896.6C. |
| 31788791 | Background | Sepulveda P, Cortinez LI, Irani M, Egana JI, Contreras V, Sanchez Corzo A, Acosta I, Sitaram R. Differential frontal alpha oscillations and mechanisms underlying loss of consciousness: a comparison between slow and fast propofol infusion rates. Anaesthesia. 2020 Feb;75(2):196-201. doi: 10.1111/anae.14885. Epub 2019 Dec 1. |
| 28003656 | Background | Sitaram R, Ros T, Stoeckel L, Haller S, Scharnowski F, Lewis-Peacock J, Weiskopf N, Blefari ML, Rana M, Oblak E, Birbaumer N, Sulzer J. Closed-loop brain training: the science of neurofeedback. Nat Rev Neurosci. 2017 Feb;18(2):86-100. doi: 10.1038/nrn.2016.164. Epub 2016 Dec 22. |
| 29121108 | Background | Yeom SK, Won DO, Chi SI, Seo KS, Kim HJ, Muller KR, Lee SW. Spatio-temporal dynamics of multimodal EEG-fNIRS signals in the loss and recovery of consciousness under sedation using midazolam and propofol. PLoS One. 2017 Nov 9;12(11):e0187743. doi: 10.1371/journal.pone.0187743. eCollection 2017. |
| 29463928 | Background | Zimeo Morais GA, Balardin JB, Sato JR. fNIRS Optodes' Location Decider (fOLD): a toolbox for probe arrangement guided by brain regions-of-interest. Sci Rep. 2018 Feb 20;8(1):3341. doi: 10.1038/s41598-018-21716-z. |
| D012816 | Signs and Symptoms |
| D013568 | Pathological Conditions, Signs and Symptoms |