Combination of Entropy and Electroencephalograpy for Deep of Hypnosis Monitoring Improvement during General Anesthesia
Article Information
Alberto Martinez Ruiz^{1}, Blanca Escontrela^{2*}, Itxaso Merino Julian^{2}
^{1}Chief of Department of Anesthesia and Critical Care Cruces University Hospital, Barakaldo, Basque Country, Spain
^{2}Consultant Department of Anesthesia and Critical Care Cruces University Hospital, Barakaldo, Basque Country, Spain
^{*}Corresponding Author: Blanca Escontrela. Consultant Department of Anesthesia and Critical Care Cruces University Hospital, Barakaldo, Basque Country, Spain
Received: 18 May 2023; Accepted: 26 May 2023; Published: 03 November 2023
Citation: Alberto Martinez Ruiz, Blanca Escontrela, Itxaso Merino Julian. Combination of Entropy and Electroencephalograpy for Deep of Hypnosis Monitoring Improvement during General Anesthesia. Anesthesia and Critical Care 5 (2023): 5368.
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Background: Deep of hypnosis monitoring based on electroencephalogram (EEG) and end tidal anesthetic concentration (ETAC) of volatile agents are a standard of care in patients under general anesthesia. Entropy and ETAC could reflected the effects of volatile agents as evidence had been showed, but its accuracy for hypnosis monitoring during nociceptive stimulation in patients under neuromuscular block and emergence is controversial.
Methods: Prospective, singlegroup, doubleblinded and observational study was conducted in young female patients scheduled for minor gynecological surgery under general anesthesia. A standard protocol was administered to all patients and deep of hypnosis monitoring using entropy and quantitative EEG (qEEG) was blind to anesthesiologist in charge. Primary outcomes were described changes and discrimination capacity of hypnosis depth of entropy, qEEG and ETAC from induction to emergence. Secondary outcome was described an equation of hypnosis state prediction using entropy, qEEG and ETAC based on its discrimination capacity of hypnosis depth.
Results: 42 patients scheduled for minor gynecological surgery under general anesthesia were included. Combination of frontal electromyogram (fEMG), Response Entropy (RE), delta and theta activities showed an excellent prediction capacity of hypnosis state. Entropy and fEMG showed an excellent discrimination capacity of hypnosis depth from induction to emergence. Delta activity showed a good discrimination capacity of hypnosis state during emergence. Electroencephalogram amplitude during maintenance and median Frequency (MF) during emergence showed an acceptable discrimination capacity of hypnosis depth. Beta/Delta ratio (B/D) during induction and emergence showed an acceptable discrimination capacity of hypnosis depth. During maintenance Burst Suppression corrected for Spectral Edge Frequency ratio (BcSEF) and Beta/Theta ratio (B/T) showed an acceptable discrimination capacity of hypnosis depth. ETAC showed a good discrimination capacity from laryngoscopy to emergence. ETAC variability was poorly explained for entropy. Entropy variability was poorly explained for ETAC and EEG variables.
Conclusions: Our study described an equation for hypnosis state prediction combining fast, intermediate and slow activity. An interesting finding of the present study was that delta activity, MF, amplitude and corrected variables probably reflecting the balance between slow and fast activity could improve deep of hypnosis monitoring, but limitations related to little size and design of this study warrants further validation.
Keywords
Deep of hypnosis; Electroencephalogram; Entropy; General anesthesia.
Deep of hypnosis articles; Electroencephalogram articles; Entropy articles; General anesthesia articles
Article Details
1. Introduction
Deep of hypnosis monitoring is a currently recommended standard of care during general anesthesia according to guidelines [1] as an indication of anesthetic drugs effects on brain electrical activity registered for processed electroencephalographic monitors like Bispectral Index (BIS) and entropy. Although end tidal anesthetic concentration (ETAC) could indicate drug concentration in blood and brain [1], previous studies showed that entropy monitoring also reflected effects of sevoflurane (2) and reduces its uptake [3]. Nevertheless, alert state after emergence of general anesthesia is a main challenge for deep of hypnosis monitors because this brain reactivation probably come from subcortical structures [4] not registered for electroencephalographic monitors. Entropy monitor (GE Healthcare®, Helsinki, Finland) analyses space and temporal integration of brain neuronal activity registered for frontal electroencephalogram (EEG) [2,4]. State entropy (SE) and Response Entropy (RE) gives complementary information for deep of hypnosis monitoring [2,49]. SE showed dominant band of EEG spectrum, reflecting mainly cortical brain electrical activity [4,5,7,8,1015] using a time window between 60 to 16 seconds obtaining values from 0 (deep hypnosis state) to 91 (awake state) [8,11,13,14,16,17]. RE showed higher frequency bands including those of frontal electromyogram (fEMG) [4,5,7,8,9,1013], with a time window between 15 to 1.92 seconds, obtaining values from 0 to 100 [8,11,13,14,16,17]. Entropy correctly reflected all general anesthesia phases according to evidence [4]. RE has advantages for monitoring induction and emergence [11,18], with benefits for potentially reducing time to alert state [2,19,20]. Classic intraoperative awareness associated with increase of higher amplitude bands of fEMG could be faster monitored for RE compared to SE and BIS [18]. A lack of strong evidence related to entropy performance for time to awakening and recall of intraoperative awareness make necessary more powerful studies [21].
Important changes of hypnosis depth during general anesthesia mainly related to imbalance between surgical stimulation and hypnosis drugs concentration probably required a combined approach for accurately monitoring and reducing complications associated to slight or deep anesthesia probably related with higher mortality and costs. Quantitative electroencephalogram (qEEG) and entropy classically showed changes associated to general anesthesia [18, 22]. Corrected electroencephalographic variables reflecting balance between fast and slow activity could have advantages for deep of hypnosis monitoring. This study aims to describe a prediction equation of hypnosis state combining entropy and qEEG variables for deep of hypnosis monitoring improvement during general volatile anesthesia with sevoflurane.
2. Methods
2.1 Study design and population:
This prospective, single group, double blind and observational study was conducted in Anesthesia and Critical Care Department of Cruces University Hospital between 2019 and 2020. Female patients between 18 to 60 years, ASA I and II scheduled for minor gynecology surgery after informed consent sign were included (Table 1). Patients with neurological disease, substance abuse history, obesity, negative decision to participate in study or simultaneously participating in another study were excluded. (Table 1). Ethical Committee of Clinical Investigation acceptance was obtained before inclusion of patients.
Table 1: Inclusion and Exclusion criteria
Inclusion criteria 
Female patients Age between 18 and 60 years ASA I and II. Scheduled minor gynecological surgery Signed informed consent 
Exclusion criteria 
Neurological disease History of substance abuse Obesity (BMI greater than 28) Patient refusal to participate in study Simultaneous participation in another study 
BMI: Body Mass Index
2.2 Anesthesia protocol:
A standard monitoring with NonInvasive Blood Pressure, electrocardiogram and peripheral pulse oximeter (Sp02) was placed when patient arrived at operating room. Preoxygenation with 100% of oxygen until reaching an exhaled fraction of oxygen close to 90% and standard intravenous induction with 1 mg of midazolam, 3 ug/Kg of fentanyl, 2 mg/Kg of propofol and 0.15 mg/Kg of cisatracurium was performed in all patients. Then, ventilation with face mask for 180 seconds and orotracheal intubation was made. If difficulties, ventilation with face mask was restarted and a new attempt of intubation was made in 60 seconds. Mechanical ventilation with mixture of air and oxygen for a fraction of inspired oxygen (Fi02) of 50%, tidal volume of 6 ml/Kg, positive endexpiratory pressure (PEEP) between 4 to 6 cm of H_{2}0 and respiratory rate between 10 to 12 breaths per minute was made with adjustments based on anesthesiologist decision. Sevoflurane at 2% was used for anesthesia maintenance with a constant Minimum Alveolar Concentration (MAC) between 0.7 to 1.2 and ETAC was monitored. Additional doses of fentanyl were administered based on anesthesiologist decision. Intravenous analgesia with 50 mg of dexketoprofen or 2 g of metamizole and 1 g of paracetamol prior to emergence and prophylaxis of postoperative nausea and vomiting (PONV) with ondansetron 4 mg intravenous was administered.
2.3 Deep of hypnosis monitoring:
Deep of hypnosis monitoring using entropy and qEEG was placed for study team and anesthesiologist in charge of operating room was blinded to it. Entropy, qEEG and ETAC were observed before induction throughout surgery and after emergence. Entropy sensor (GE Healthcare®, DatexOhmeda Division, Instrumentarium Corp, Helsinki, Finland) was placed over skin of frontal area cleaned with 70% alcohol according to manufacturer recommendations with the first, second and third electrodes on frontal, orbicular and temporal muscles, respectively. Impedance of electrodes with skin was found to be less than 7.5 kΩ. Values of SE and RE were calculated for MENTROPY® module of DatexOhmeda S/5 anesthesia monitor (GE Healthcare®, DatexOhmeda Division, Instrumentarium Corp, Helsinki, Finland) using a scale from 0 to 91 for SE and 0 to 100 for RE. qEEG surface electrodes (Zipprep, Aspect Medical Systems Inc, Norwood, Massachusetts) were placed over skin of frontal area cleaned with 70% alcohol in position Fp1, Fp2, F7, F8 and Fz according to international system 10/20. Impedance of electrodes with skin was less than 10 kΩ. Spectral analysis of qEEG including Spectral Edge Frequency (SEF), Medium frequency (MF), amplitude, Burst Suppression Rate (BSR), relative powers α, β ,δ, θ and fEMG was performed for MEEG® module of DatexOhmeda S/5 anesthesia monitor (GE Healthcare®, Datex Ohmeda Division, Instrumentarium Corp,Helsinki, Finland). A frequency range between 0.5 to 30 Hz was used for qEEG and 60 to 300 Hz for fEMG.
Variables:
Baseline variables were age, Heart Rate (HR), Systolic Blood Pressure (SBP), Sp02 and temperature. Intraoperative variables were HR, SBP, ETAC, SE, RE, SEF, MF, BSR, amplitude, fEMG and α, β, δ, θ activity.
SEF corrected for BSR (BcSEF), Beta/Theta (B/T) and Beta/Delta (B/D) ratios were corrected EEG variables calculated using follow equations:
Spectral Edge Frequency corrected for Burst Suppression Rate (BcSEF)
BcSEF= SEF95 x (1 BSR /100)
Beta/Theta ratio (B/T)
B/T= % beta band / % theta band
Beta/Delta ratio (B/D)
B/D= % beta band / % delta band
In time 0 (T0) patient was awake, time 1 (T1) was induction of anesthesia, time 2 (T2) was nociceptive stimulation of laryngoscopy, time 3 to 6 (T36) was maintenance and time 7 to 8 (T78) was emergence.
Clinical variables were awake defined as patient responds to verbal command or sleep when patient does not respond to verbal commands or nociceptive stimulus (jaw pull) in each anesthesia times of induction, laryngoscopy, maintenance and emergence.
Outcomes:
Primary outcomes were described changes and discrimination capacity of hypnosis depth of entropy, qEEG and ETAC in each study times. Correlation between entropy, qEEG, ETAC and hemodynamic variables (HR and SBP) and regression analysis to explained variability of ETAC and entropy were another primary endpoints.
Secondary outcome was described an equation of hypnosis state prediction using entropy and qEEG based on its discrimination capacity of hypnosis depth.
Statistical analysis:
All baseline and intraoperative variables were stored using DatexOhmeda S/5 Collect software (DatexOhmeda Division, Instrumentarium Corp,Helsinki, Finland) for offline analysis. Statistical analysis was performed using SPSS 19 software (IBM® SPSS Chicago IL). Sample size was calculated based on previously published data of likelihood of entropy, qEEG variables and ETAC to correctly discriminate hypnosis depth [23,24]. Kolmogorov Smirnov test was used to determine normality of distributions. Mean, median, standard deviation (SD) and interquartile ranges (IQR) of variables were calculated in each study times.
Comparisons of means was performed using t test for normal distributed variables and Wilcoxon test for nonnormal distributed variables to determine differences between study times with p value of 5%. Receiver Operating Characteristics (ROC) curves showed discrimination capacity of entropy, qEEG variables and ETAC to monitoring deep of hypnosis in each study times using Wilcoxon or Mann Whitney tests; values higher than 0.7 were considered acceptable and rounding 1 a perfect discrimination capacity. A linear correlation was performed to determine the strength of association between entropy, qEEG, ETAC and hemodynamic variables (HR and SBP) using Rho Spearman test and considering a strong correlation if values rounding + 1 and 1. Linear regression analysis determined capacity of entropy to explained ETAC variability or ETAC and qEEG variables to explained entropy variability.
A logistic regression analysis was performed for described an equation of depth of hypnosis prediction using entropy and qEEG variables. Validity of the model was tested with 2log plausibility, R square of Cox and Snell and R square of Nagelkerke. The overall fit of the model was checked with the Hosmer Lemeshow test.
3. Results and Discussion
3.1 Baseline variables:
42 patients were included in this study. Baseline variables were mean SBP of 140 mmHg, HR of 95 bpm and temperature of 36 ºC (Table 2)
Table 2: Baseline variables
AGE (years) Mean (SD) 
39 (8) 
SYSTOLIC BLOOD PRESSURE (mmHg) Mean (SD) 
139.7 (13.2) 
HEART RATE (bpm) Mean (SD) 
94.76 (1.6) 
Sp0_{2} (%) Mean (SD) 
99 (0.47) 
TEMPERATURE (ºC) Mean (SD) 
36 (0.19) 
3.2 Comparison of Means:
Comparison of means of SBP, HR, ETAC, entropy, qEEG and corrected variables from induction to emergence of general anesthesia were made (Table 3, figures 1 to 16 supplemental content)
Table 3: Comparison of means of Systolic Blood Pressure, Heart Rate, End Tidal Anesthetic Concentration, Entropy, Quantitative Electroencephalographic and Corrected Variables from induction to emergence of anesthesia
Variable 
Induction 
Laryngoscopy 
Maintenance 
Emergence 
SYSTOLIC BLOOD PRESSURE (SBP) (95% CI) 
↓10,082 (3,953  16,210 p 0.002 
↑20,975 (27,618  14,332) p 0.000 
↓22,620 (18,563  26,678) p 0.000 
↑8,201 (12,508  3,895) p 0.000 
HEART RATE (HR) (95% CI) 
≈ 7,143 (11,662  2,623) p 0.002 
↑6,725 (11,152  2,299) 
↓7,855 (4,632  11,078) p 0.000 
↑5,288 (8,532  2,044) p 0.000 
END TIDAL ANESTHETIC CONCENTRATION (ETAC) (95% CI) 
↑0,055 (0,095  0,015) p 0.008 
↑0,938 (1,013  0,862) p 0.000 
↓0,109 (0,023  0,240) p 0.104 
↓0,439 (0,342  0,536) p 0.000 
STATE ENTROPY (SE) (95% CI) 
↓61,976 (59,150  64,802) p 0.000 
↑26,961 (30,572  23,349) p 0.000 
↑ 5,565(7,927  3,204) p 0.000 
↑21,511 (23,876  19,146) p 0.000 
RESPONSE ENTROPY (RE) (95% CI) 
↓69,968 (67,131  72,806) 
↑27,748(31,638  23,858) p 0.000 
↑7,203 (10,019  4,386) p 0.000 
↑27,204 (30,113  24,296) p 0.000 
FRONTAL ELECTROMYOGRAM (fEMG) (95% CI) 
↓3,609 (2,883  4,334) p 0.000 
↑0,125 (0,651  0,401) p 0.639 
↓ 0,048 (0,154  0,058) p 0.368 
↑1,840(2,452  1,228) p0.000 
AMPLITUDE (95% CI) 
↑ 11,930 (15,636  8,223) p 0.000 
↓7,115(3,944  10,286) p 0.000 
↓ 4,261 (2,747  5,774) p 0.000 
↓5,045 (2,628  7,463) p0.000 
ALPHA POWER (95% CI) 
↓4,139 (1,455  6,823) p 0.003 
↑6,591 (8,464  4,718) p 0.000 
↑8,976 (11,345  6,607) p 0.000 
↓8,672 (5,434  11,910) p0.000 
BETA POWER (95% CI) 
↓4,806 (2,852  6,759) p 0.000 
↑3,035 (4,402  1,667) p 0.000 
↓0,371 (1,160  0,418) p 0.354 
↑2,824 (5,466  0,183) p 0.036 
DELTA POWER (95% CI) 
↑12,287 (17,174  7,400) p 0.000 
↓15,957 (12,050  19,863) p 0.000 
↓12,702 (9,300  16,103) p 0.000 
↑9,916 (14,876  4,956) p 0.000 
THETA POWER (95% CI) 
↓3,361 (1,716  5,006) p 0.000 
↑6,357 (8,082  4,631) p 0.000 
↑3,298 (4,579  2,017) p 0.000 
↓4,053 (2,336  5,771) p 0.000 
SPECTRAL EDGE FREQUENCY (SEF) (95% CI) 
↓4,806 (3,234  6,377) p 0.000 
↑5,052 (6,122  3,983) p 0.000 
↑0,895 (1,460  0,330) p 0.002 
↑1,382 (2,301  0,462) p0.004 
MEDIAN FREQUENCY (MF) (95% CI) 
↓0,389 (0,168  0,946) p 0.169 
↑0,626 (1,089  0,163) p 0.008 
↑1,226 (1,650  0,802) p 0.000 
↓1,214 (0,383  2,044) p 0.005 
SPECTRAL EDGE FREQUENCY CORRECTED FOR SUPPRESSION RATE (BcSEF) (95% CI) 
↓1,808 (0,813  2,803) p 0.000 
↑3,341 (4,139  2,542) p 0.000 
↓1,516 (0,519  2,514) p 0.003 
↓3,665 (2,712  4,618) p 0.000 
BETA /THETA RATIO (B/T) (95% CI) 
↓0,717 (0,093  1,342) p 0.025 
↑0,145 (0,362 0,072) p 0.187 
↑0,145 (0,314  0,024) p 0.092 
↑1,885 (3,267 0,502) p 0.008 
BETA/DELTA RATIO (B/D) (95% CI) 
≈ 0,019 (0,040  0,077) p 0.530 
↑0,000 (0,049  0,049) p 1.000 
≈0,016 (0,055  0,023) p 0.416 
↓0,183 (0,071  0,438) p 0,157 
3.3 Discrimination Capacity:
Discrimination capacity of SBP, HR, ETAC, entropy, qEEG and corrected variables from induction to emergence of general anesthesia was observed (Table 4, Figures 1 to 12).
Table 4: Discrimination capacity of Systolic Blood Pressure, Heart Rate, End tidal of Anesthetic Concentration, Entropy, Quantitative Electroencephalographic and Corrected Variables from induction to emergence of anesthesia
INDUCTION 
LARYNGOSCOPY 
MAINTENANCE 
EMERGENCE 

SYSTOLIC BLOOD PRESSURE (SBP) 
0,757±0.06 
0,58±0.07 
0,457±0.07 
0,635±0.07 
HEART RATE (HR) 
0,515±0,062 
0,554±0,066 
0,540±0,062 
0,603±0,058 
END TIDAL ANESTHESIC CONCENTRATION (ETAC) 
0,52±0,07 
0,982±0,02 
1 
0,884±0,04 
STATE ENTROPY (SE) 
0,997±0,002 
0,959±0,022 
0,998±0,022 
0,844±0,046 
RESPONSE ENTROPY (RE) 
0,999±0,002 
0,985±0,01 
1 
0,862±0,044 
FRONTAL ELECTROMYOGRAM (fEMG) 
0,933±0,04 
0,941±0.032 
0,977±0,02 
0,756±0,06 
AMPLITUDE 
0,829±0,052 
0,739±0,068 
0,781±0,06 
0,644±0,072 
BETA POWER 
0,694±0,062 
0,527±0,066 
0,557±0,064 
0,696±0,062 
THETA POWER 
0,630±0,066 
0,654±0,062 
0,709±0,056 
0,616±0,064 
DELTA POWER 
0,630±0,064 
0,667±0,058 
0,696±0,056 
0,808±0,048 
SPECTRAL EDGE FREQUENCY (SEF) 
0,695±0,062 
0,528±0,066 
0,544±0,066 
0,635±0,064 
MEDIAN FREQUENCY (MF) 
0,514±0,068 
0,645±0,062 
0,7±0,056 
0,759±0,048 
SPECTRAL EDGE FREQUENCY CORRECTED FOR SUPPRESSION RATE (BcSEF) 
0,563±0,066 
0,563±0,033 
0,766±0,027 
0,509±0,07 
BETA/THETA RATIO (B/T) 
0,648±0,068 
0,589±0,068 
0,711±0,062 
0,579±0,06 
BETA/DELTA RATIO (B/D) 
0,711±0,062 
0,537±0,066 
0,524±0,058 
0,713±0,064 
3.4 Correlation:
Correlations of entropy, SBP, HR, ETAC, qEEG and corrected variables from induction to emergence of general anesthesia were observed (Table 5, figures 13 to 17)
Table 5: Correlations of Entropy, Systolic Blood Pressure, Heart Rate, EndTidal Anesthetic Concentration, Quantitative electroencephalogram and Corrected Variables from induction to emergence of anesthesia
INDUCTION 
LARYNGOSCOPY 
MAINTENANCE 
EMERGENCE 

Entropy, Quantitative Electroencephalogram and Corrected Variables 
SEBeta r 0.714 p0.000 
SEBeta r 0.828 p0.000 
SEBeta r 0.784 p0.000 
SEBeta r0.305 p0.000 
SEDelta r0.628 p 0.000 
SEDelta r 0.674 p0.000 
SEDelta r 0.671 p0.000 
SEDelta r0.015 p0.867 

SETheta r0.489 p0.000 
SETheta r 0.593 p0.000 
SETheta r 0.193 p0.016 
SETheta r0.362 p0.000 

SEAlpha r0.542 p0.000 
SEAlpha r 0.458 p0.000 
SEAlpha r 0.619 p0.000 
SEAlpha r0.183 p0.038 

REBeta r 0.716 p 0.0000 
REBeta r 0.818 p0.000 
REBeta r0.774 p0.000 
REBeta r0.033 p0.713 

REDelta r 0.642 p 0.000 
REDelta r 0.656 p0.000 
REDelta r0.649 p0.000 
REDelta r0.247 p0.005 

RETheta r 0.491 p0.0000 
RETheta r 0.575 p0.000 
RETheta r 0.171 p0.033 
RETheta r0.326 p0.000 

REAlpha r 0.574 p0.0000 
REAlpha r 0.442 p0.000 
REAlpha r0.591 p0.000 
REAlpha r0.293 p0.001 

SESEF r 0.636 p0.000 
SESEF r 0.824 p0.000 
SESEF r0.767 p0.000 
SESEF r0.421 p0.000 

SEMF r 0.354 p0.000 
SEMF r 0.574 p0.000 
SEMF r0.540 p0.000 
SEMF r0.034 p0.706 

SEBcSEF r 0.548 p0.000 
SEBcSEF r 0.346 p0.000 
SEBcSEF r0.604 p0.000 
SEBcSEF R0.228 p0.007 

SEB/T r 0.499 p0.000 
SEB/T r 0.439 p0.000 
SEB/T r0.462 p0.000 
SEB/T r0.383 p0.000 

SEB/D r 0.302 p0.000 
SEB/D r 0.245 p0.005 
SEB/D r.0.317 p0.000 
SEB/D r0.133 p0.133 

RESEF r 0.659 p0.000 
RESEF r 0.820 p0.000 
RESEF r0.753 p0.000 
RESEF r0.185 p0.036 

REMF r 0.336 p0.000 
REMF r 0.549 p0.000 
REMF r0.526 p0.000 
REMF r0.180 p0.041 

REBcSEF r 0.553 p0.000 
REBcSEF r 0.326 p0.000 
REBcSEF r0.589 p0.000 
REBcSEF r0.015 p0.865 

REB/T r 0.482 p0.000 
REB/T r 0.437 p0.000 
REB/T r0.459 p0.000 
REB/T r0.174 p0.050 

REB/D r 0.305 p0.000 
REB/D r 0.235 p0.000 
REB/D r 0.314 p0.000 
REB/D r0.035 p0.694 

Frontal Electromyogram, Entropy, Quantitative Electroencephalogram and Corrected Variables 
SE r 0.129 p0.157 
SE r 0.129 p0.138 
SE r0.305 p0.000 
SE r0.264 p0.003 
RE r 0.106 p0.244 
RE r 0.138 p0.113 
RE r0.299 p0.000 
RE r0.576 p0.000 

Beta r 0.021 p 0.825 
Beta r 0.042 p 0.627 
Beta r 0.230 p 0.004 
Beta r 0.190 p 0.030 

Alpha r 0.191 p 0.038 
Alpha r 0.011 p 0.899 
Alpha r 0.097 p 0.228 
Alpha r 0.312 p 0.000 

Theta r 0.061 p 0.514 
Theta r 0.107 p 0.221 
Theta r 0.122 p 0.132 
Theta r 0.108 p 0.219 

Delta r 0.109 p 0.240 
Delta r 0.045 p 0.606 
Delta r 0.163 p 0.042 
Delta r 0.339 p 0.000 

BcSEF r 0.074 p 0.416 
BcSEF r 0.021 p 0.807 
BcSEF r 0.223 p 0.005 
BcSEF 0.368 p 0.000 

B/T r 0.014 p 0.884 
B/T r 0.130 p 0.135 
B/T r 0.131 p 0.105 
B/T r 0.067 p 0.449 

B/D r 0.074 p 0.426 
B/D r 0.115 p 0.189 
B/D r 0.055 p 0.493 
B/D r 0.221 p 0.011 

Quantitative Electroencephalogram and Corrected Variables 
BetaDelta r 0.814 p0.000 
BetaDelta r0.845 p 0.000 
BetaDelta r0.833 p0.000 
BetaDelta r0.647 p0.000 
BetaAlpha r 0.711 p0.000 
BetaAlpha r 0.695 p0.000 
BetaAlpha r 0.758 p0.000 
BetaAlpha r0.276 p0.001 

BetaTheta r 0.611 p0.000 
BetaTheta r 0.651 p0.000 
BetaTheta r 0.329 p0.000 
BetaTheta r0.220 p0.012 

AlphaDelta r 0.933 p0.000 
AlphaDelta r0.886 p0.000 
AlphaDelta r0.931 p0.000 
AlphaDelta r0.824 p0.000 

AlphaTheta r 0.701 p0.000 
AlphaTheta r0.613 p0.000 
AlphaTheta r 0.389 p0.000 
AlphaTheta r0.463 p0.000 

DeltaTheta r 0.869 p0.000 
DeltaTheta r0.840 p0.000 
DeltaTheta r0.588 p0.000 
DeltaTheta r0.374 p0.000 

BetaSEF r 0.862 p0.000 
BetaSEF r 0.953 p0.000 
BetaSEF r0.951 p0.000 
BetaSEF r0.918 p0.000 

BetaMF r 0.461 p0.000 
BetaMF r 0.725 p0.000 
BetaMF r0.707 p0.000 
BetaMF r0.649 p0.000 

AlphaSEF r 0.943 p0.000 
AlphaSEF r 0.710 p0.000 
AlphaSEF r0.847 p0.000 
AlphaSEF r0.244 p0.005 

AlphaMF r 0.460 p0.000 
AlphaMF r 0.703 p0.000 
AlphaMF r 0.744 p0.000 
AlphaMF r0.754 p0.000 

DeltaSEF r 0.952 p0.000 
DeltaSEF r0.856 p0.000 
DeltaSEF r0.888 p0.000 
DeltaSEF r0.577 p0.000 

DeltaMF r 0.542 p0.000 
DeltaMF r0.863 p0.000 
DeltaMF r0.855 p0.000 
DeltaMF r0.941 p0.000 

ThetaSEF r 0.763 p0.000 
ThetaSEF r0.661 p0.000 
ThetaSEF r0.379 p0.000 
ThetaSEF r0.151 p0.084 

ThetaMF r 0.539 p0.000 
ThetaMF r0.770 p0.000 
ThetaMF r0.531 p0.000 
ThetaMF r0.266 p0.002 

BetaBcSEF r 0.851 p0.000 
BetaBcSEF r0.562 p0.000 
BetaBcSEF r0.950 p0.000 
BetaBcSEF r0.052 p0.558 

BetaB/T r 0.655 p0.000 
BetaB/T r0.542 p0.000 
BetaB/T r0.534 p0.000 
BetaB/T r0.851 p0.000 

BetaB/D r 0.324 p0.000 
BetaB/D r0.329 p0.000 
BetaB/D r0.340 p0.000 
BetaB/D r0.712 p0.000 

AlphaBcSEF r0.941 p0.000 
AlphaBcSEF r0.702 p0.000 
AlphaBcSEF r0.848 p0.000 
AlphaBcSEF r0.027 p0.758 

AlphaB/D r 0.316 p0.000 
AlphaB/D r0.217 p0.012 
AlphaB/D r0.312 p0.000 
AlphaB/D r0.174 p0.047 

AlphaB/T r 0.447 p0.000 
AlphaB/T r0.230 p0.008 
AlphaB/T 0.441 p0.000 
AlphaB/T r0.017 p0.844 

DeltaBcSEF r0.938 p0.000 
DeltaBcSEF r0.561 p0.000 
DeltaBcSEF r0.890 p0.000 
DeltaBcSEF r0.040 p0.651 

DeltaB/T r0.418 p0.000 
DeltaB/T r0.243 p0.000 
DeltaB/T r0.400 p0.000 
DeltaB/T r0.306 p0.000 

DeltaB/D r 0.314 p0.001 
DeltaB/D r0.332 p0.000 
DeltaB/D r0.334 p0.000 
DeltaB/D r0.557 p0.000 

ThetaBcSEF r 0.737 p0.000 
ThetaBcSEF r0.342 p0.000 
ThetaBcSEF r0.381 p0.000 
ThetaBcSEF r0.190 p0.030 

ThetaB/T r 0.082 p 0.388 
ThetaB/T r0.075 p0.394 
ThetaB/T r0.329 p0.000 
ThetaB/T r0.603 p0.000 

ThetaB/D r 0.278 p0.000 
ThetaB/D r0.104 p0.235 
ThetaB/D r0.116 p0.149 
ThetaB/D r0.149 p0.090 

Hemodynamic variables (SBP and HR), Entropy, Quantitative Electroencephalogram, Frontal Electromyogram, Corrected Variables and EndTidal Anesthetic Concentration 
SBPHR r 0.021 p0.831 
SBPHR: r 0.106 p 0.226 
SBPHR: r 0.376 p 0.000 
SBPHR: r 0.469 p 0.000 
SBPSE: r 0.197 p0.031 
SBPSE: r 0.149 p 0.079 
SBPSE: r 0.067 p 0.399 
SBPSE: r 0.009 p 0.913 

SBPRE: r 0.122 p0.186 
SBPRE: r 0.159 p 0.061 
SBPRE: r 0.072 p 0.363 
SBPRE: r 0.031 p 0.715 

SBPBeta: r 0.096 p0.320 
SBPBeta: r 0.018 p 0.835 
SBPBeta: r 0.123 p 0.130 
SBPBeta: r 0.256 p 0.003 

SBPAlpha: r 0.157 p0.101 
SBPAlpha: r 0.131 p 0.133 
SBPAlpha: r 0.338 p 0.000 
SBPAlpha: r 0.256 p 0.003 

SBPTheta: r 0.173 p0.071 
SBPTheta: r 0.059 p 0.500 
SBPTheta: r 0.228 p 0.005 
SBPTheta: r 0.048 p 0.583 

SBPDelta: r 0.084 p0.381 
SBPDelta: r 0.021 p 0.814 
SBPDelta: r 0.193 p 0.016 
SBPDelta: r 0.232 p 0.008 

SBPSEF: r 0.037 p0.701 
SBPSEF: r 0.025 p 0.771 
SBPSEF: r 0.216 p 0.007 
SBPSEF: r 0.224 p 0.010 

SBPMF: r 0.047 p0.624 
SBPMF: r 0.026 p 0.765 
SBPMF: r 0.025 p 0.755 
SBPMF: r 0.208 p 0.017 

SBPfEMG: r 0.381 p0.000 
SBPfEMG: r 0.010 p 0.909 
SBPfEMG: r 0.012 p 0.880 
SBPfEMG: r 0.166 p 0.059 

SBPBcSEF: r 0.075 p0.413 
SBPBcSEF: r0.187 p 0.026 
SBPBcSEF: r 0.210 p 0.008 
SBPBcSEF: r 0.223 p 0.008 

SBPB/T: r 0.312 p0.001 
SBPB/T: r 0.108 p 0.214 
SBPB/T: r 0.438 p 0.000 
SBPB/T: r 0.225 p 0.010 

SBPB/D: r 0.064 p0.504 
SBPB/D: r 0.015 p 0.864 
SBPB/D: r 0.208 p 0.010 
SBPB/D: r 0.017 p 0.843 

SBPETAC: r 0.345 p 0.000 
SBPETAC: r 0.055 p 0.514 
SBPETAC: r 0.089 p 0.263 
SBPETAC: r 0.078 p 0.361 

HRSBP: r 0.021 p0.831 
HRSBP: r 0.106 p 0.226 
HRSBP: r 0.376 p 0.000 
HRSBP: r 0.469 p 0.000 

HRSE: r 0.157 p0.114 
HRSE: r 0.304 p 0.000 
HRSE: r 0.46 p 0.557 
HRSE: r 0.041 p 0.638 

HRRE: r 0.202 p0.041 
HRRE: r 0.288 p 0.001 
HRRE: r 0.036 p 0.651 
HRRE: r 0.145 p 0.091 

HRBeta: r 0.342 p0.001 
HRBeta: r 0.206 p 0.022 
HRBeta: r 0.104 p 0.200 
HRBeta: r 0.404 p 0.000 

HRAlpha: r 0.351 p0.001 
HRAlpha: r 0.014 p 0.877 
HRAlpha: r 0.035 p 0.661 
HRAlpha: r 0.373 p 0.000 

HRTheta: r 0.063 p0.548 
HRTheta: r 0.296 p 0.001 
HRTheta: r 0.283 p 0.000 
HRTheta: r 0.091 p 0.304 

HRDelta: r 0.228 p0.029 
HRDelta: r 0.159 p 0.080 
HRDelta: r 0.110 p 0.173 
HRDelta: r 0.418 p 0.000 

HRSEF: r 0.266 p0.010 
HRSEF: r 0.171 p 0.058 
HRSEF: r 0.140 p 0.082 
HRSEF: r 0.342 p 0.000 

HRMF: r 0.097 p0.358 
HRMF: r 0.200 p 0.027 
HRMF: r 0.221 p 0.006 
HRMF: r 0.405 p 0.000 

HRfEMG: r 0.149 p0.149 
HRfEMG: r 0.039 p 0.667 
HRfEMG: r 0.243 p 0.002 
HRfEMG: r 0.151 p 0.085 

HRBcSEF: r 0.137 p0.170 
HRBcSEF: r 0.030 p 0.731 
HRBcSEF: r 0.131 p 0.096 
HRBcSEF: r 0.122 p 0.154 

HRB/T: r 0.433 p0.000 
HRB/T: r 0.013 p 0.890 
HRB/T: r 0.242 p 0.002 
HRB/T: r 0.310 p 0.000 

HRB/D: r 0.149 p0.157 
HRB/D: r 0.088 p 0.334 
HRB/D: r 0.049 p 0.543 
HRB/D: r 0.174 p 0.047 

HRETAC: r 0.253 p0.010 
HRETAC: r 0.126 p 0.150 
HRETAC: r 0.108 p 0.172 
HRETAC: r 0.136 p 0.109 

EndTidal Anesthetic Concentration, Hemodynamic variables (SBP and HR), Entropy, Quantitative Electroencephalogram, Frontal Electromyogram and Corrected Variables. 
ETACSBP: r 0.345 p0.000 
ETACSBP: r 0.055 p 0.514 
ETACSBP: r 0.089 p 0.263 
ETACSBP: r 0.078 p 0.361 
ETACHR: r 0.253 p0.010 
ETACHR: r 0.126 p 0.150 
ETACHR: r 0.108 p 0.172 
ETACHR: r 0.136 p 0.109 

ETACSE: r 0.220 p0.013 
ETACSE: r 0.346 p 0.000 
ETACSE: r 0.210 p 0.007 
ETACSE: r 0.372 p 0.000 

ETACRE: r 0.220 p0.013 
ETACRE: r 0.358 p 0.000 
ETACRE: r 0.197 p 0.012 
ETACRE: r 0.309 p 0.000 

ETACBeta: r 0.163 p0.077 
ETACBeta: r 0.354 p 0.000 
ETACBeta: r 0.237 p 0.003 
ETACBeta: r 0.229 p 0.009 

ETACAlpha: r 0.148 p0.109 
ETACAlpha: r 0.178 p 0.040 
ETACAlpha: r 0.316 p 0.000 
ETACAlpha: r 0.009 p 0.921 

ETACTheta: r 0.041 p0.656 
ETACTheta: r 0.057 p 0.512 
ETACTheta: r 0.187 p 0.020 
ETACTheta: r 0.272 p 0.002 

ETACDelta: r 0.065 p0.481 
ETACDelta: r 0.208 p 0.010 
ETACDelta: r 0.280 p 0.000 
ETACDelta: r 0.085 p 0.336 

ETACSEF: r 0.030 p0.746 
ETACSEF: r 0.410 p 0.000 
ETACSEF: r 0.327 p 0.000 
ETACSEF: r 0.243 p 0.005 

ETACMF: r 0.097 p0.298 
ETACMF: r 0.260 p 0.003 
ETACMF: r 0.030 p 0.714 
ETACMF: r 0.077 p 0.383 

ETACfEMG: r 0.403 p0.000 
ETACfEMG: r 0.108 p 0.215 
ETACfEMG: r 0.060 p 0.460 
ETACfEMG: r 0.015 p 0.869 

ETACBcSEF: r 0.090 p0.315 
ETACBcSEF: r 0.170 p 0.044 
ETACBcSEF: r 0.378 p 0.000 
ETACBcSEF: r 0.144 p 0.091 

ETACB/T: r 0.156 p0.099 
ETACB/T: r 0.295 p 0.001 
ETACB/T: r0.043 p 0.591 
ETACB/T: r 0.283 p 0.001 

ETACB/D: r 0.047 p0.613 
ETACB/D: r 0.096 p 0.273 
ETACB/D: r 0.080 p 0.325 
ETACB/D: r 0.147 p 0.093 
3.4 Combined analysis of entropy, qEEG, corrected variables, ETAC and hemodynamic variables
Induction:
SE and RE decreasing showed an excellent discrimination capacity of hypnosis state and acceptable correlation with beta waves (Table 3, 4 and 5, Figure 1, 2 and 13A). fEMG decreasing showed an excellent discrimination capacity of hypnosis state without significant correlation with qEEG (Table 3, 4 and 5, Figure 1). qEEG showed a decrease of rapid and medium activity along with a slow waves increased leading to a higher amplitude and decrease of SEF (Table 3), but discrimination capacity was excellent only for amplitude (Table 4, Figure 2 and 3). Fast, medium and slow waves showed a good correlation between them and with SEF (Table 5, Figure 13 C,D,E and F), but correlations with MF was weak (Figure 13 B). BcSEF and B/T significantly decreased but only B/D showed an acceptable capacity for hypnosis depth discrimination (Table 3 and 4). BcSEF showed a good correlation with fast, medium and slow waves (Table 5). ETAC significantly increases but not showed an adequate capacity to hypnosis state prediction nor correlated with qEEG or corrected variables (Table 3,4 and 5). SBP significantly decrease and showing an acceptable discrimination of hypnosis depth but not correlating with entropy, qEEG, corrected variables and ETAC (Table 3, 4 and 5, Figure 1). HR not changed and showing a poor discrimination capacity not correlating with entropy, qEEG, corrected variables and ETAC (Table 3, 4 and 5, Figure 1).
Laryngoscopy:
SE and RE increasing showed excellent discrimination capacity of hypnosis depth and a higher correlation with beta activity and SEF (Table 3, 4 and 5. Figure 4, 5, 14A and B). fEMG not significantly changed but showed excellent capacity for deep of hypnosis monitoring without significant correlation with entropy and qEEG (Table 3, 4 and 5, Figure 4). Alpha, beta, theta, SEF and MF increasing with a decrease of delta and less amplitude of qEEG was seen during this phase (Table 3), but discrimination capacity of hypnosis state was only acceptable for amplitude (Table 4, Figure 5 and 6). A good correlation between delta with beta, alpha and theta was seen (Table 5). Beta, alpha and delta had a good correlation with SEF and MF, but theta only showed acceptable correlation with MF (Table 5). BcSEF significantly increases only presenting acceptable correlation with alpha activity (Table 3 and 5) with a poor discrimination capacity of hypnosis depth (Table 4). ETAC significantly increases showing an excellent capacity for hypnosis state discrimination without correlating with entropy, qEEG and corrected variables (Table 3, 4 and 5). SBP and HR significantly increases but showed a poor discrimination of hypnosis depth not correlating with entropy, qEEG, corrected variables and ETAC (Table 3, 4 and 5, Figure 4).
Maintenance:
SE and RE increases significantly but showing values reflecting an adequate hypnosis level (40 to 60) presenting an excellent discrimination capacity of hypnosis depth and a good correlation with beta activity and SEF (Table 3, 4 and 5, Figure 7, 8 and 15A). fEMG not significantly changed and showed an excellent discrimination capacity for hypnosis monitoring without significant correlation with entropy and qEEG (Table 3, 4 and 5, Figure 7 and 8). qEEG showed a significant decrease of delta waves and increase of alpha and theta activity without significant changes of beta waves, causing a decrease of EEG amplitude and increasing SEF and MF (Table 3). A good correlation between beta with alpha and delta waves, alpha with delta and beta activity, alpha, beta and delta waves with SEF and MF were seen (Table 5, Figure 15 B and C). Only qEEG amplitude and theta activity showed an acceptable discrimination capacity of hypnosis level (Table 4, Figure 8 and 9). BcSEF significantly decreased showing a good discrimination capacity of hypnosis depth and excellent correlation with beta, alpha and delta waves (Table 3, 4 and 5). B/T not significantly changed and showed an acceptable discrimination capacity for hypnosis monitoring (Table 3 and 4). ETAC not significantly changed and showed an excellent discrimination capacity without correlations with entropy, qEEG and corrected variables (Table 3, 4 and 5). SBP and HR significantly decreases but showed a poor discrimination capacity of hypnosis level without correlating with entropy, qEEG, corrected variables and ETAC (Table 3, 4 and 5, Figure 7).
Emergence:
SE and RE significantly increases showing an excellent discrimination capacity of hypnosis state without correlating with fEMG and qEEG (Table 3, 4 and 5, Figure 10, 11, 16A and B). fEMG significantly increasing showed an acceptable discrimination capacity of hypnosis depth without correlating with entropy and qEEG (Table 3, 4 and 5, Figure 10 and 16B). qEEG showed an increase of delta and beta waves and decrease of alpha and theta activity, causing a decrease of amplitude and MF and increase of SEF (Table 3). A good correlation between alpha and delta activity, SEF with beta and MF with alpha and delta waves were seen (Table 5). Delta activity and MF showed a good discrimination capacity of hypnosis state (Table 4, Figure 12). BcSEF significantly decreased and showed a poor discrimination capacity of hypnosis depth (Table 3 and 4). B/D showed no significant changes but an acceptable discrimination of hypnosis state (Table 3 and 4, Figure 12). Good correlations between beta activity with B/D and B/T was seen (Table 5). ETAC significantly decreases showing an excellent capacity for hypnosis monitoring without correlations with entropy, qEEG and corrected variables (Table 3, 4 and 5). SBP and HR significantly increases showing a poor discrimination capacity without correlating with entropy, qEEG, corrected variables and ETAC (Table 3, 4 and 5, Figure 10).
3.5 Linear Regression Analysis:
ETAC variability:
A linear regression analysis was performed to determine if entropy could explain ETAC variability.
ETAC=1.555  (0.13) SE R^{2}: 0.183 (p‹ 0.001)
ETAC=1.569 (0.13) RE R^{2}: 0.213 (p‹ 0.001)
Entropy variability:
A linear regression analysis was performed to determine entropy variability explained for ETAC and qEEG.
SE=72.315  3.63 (ETAC) + 3.501 (fEMG) + 0.36 BSR  0.34 Delta  0.44 Theta R^{2}: 0.532 p‹0.001
RE=73.586 5.127 (ETAC)+4.524 (fEMG)+0.398 BSR  0.319 Delta  0.386 Theta R^{2}: 0.540 p‹0.001
Linear regression showed that ETAC variability was poorly explained for entropy. Entropy variability was explained in half of cases for ETAC, BSR, delta and theta activity.
3.6 Prediction of Hypnosis Depth Equation
A logistic regression analysis was used for hypnosis depth (awake versus sleep) prediction equation using entropy and qEEG.
This model explains 81.8% of the variability of the clinical variable awake or sleep.
ROC of this model showed an Area Under Curve (AUC) of 0.999 (p‹0.001) which means that this equation could correctly predicts 99% of the time clinical status as awake or sleep (Figure 18).
Logistic regression showed that delta and theta activity, fEMG and RE could predict 81% of hypnosis state variability and 99% of the time could correctly predict hypnosis state.
4. Conclusion
This study aims to describe a prediction equation for deep of hypnosis monitoring during general volatile anesthesia with sevoflurane. Our patients were a typical population without significant neurologic diseases, medium age with a situation of normotension and normotermia to avoid possible confounder factors interfering with deep of hypnosis monitoring accuracy using entropy and qEEG.
Several factors related to design must be highlighted. Prospective, single group and observational design in young patients under general anesthesia of short duration probably make possible to describe in a real theater the changes of hypnosis depth resulting of induction, laryngoscopy, maintenance and emergence. Nociceptive stimulation influence for deep of hypnosis monitoring is controversial and was observed in this study during laryngoscopy, classically described as one of the highest pain stimuli during general anesthesia. Effects of neuromuscular paralysis resulting of administration of NMBA during induction to make orotraqueal intubation easier in accordance to universal recommendations probably resulted in a lack of frontal muscles contraction and fEMG reduction likely influencing deep of hypnosis monitoring.
Entropy, qEEG and ETAC showed expected changes during induction, laringoscopy, maintenance and emergence of general anesthesia. Entropy and qEEG reflected significant changes, increasing hypnosis depth during induction followed for classical awareness probably because of intense nociceptive stimulation of laryngoscopy, increasing of intermediate activity resulted of moderate surgical stimulation during maintenance and fast activity in emergence when anesthesia agents were withdrawal. Entropy showed an excellent discrimination capacity during all anesthesia phases. An acceptable discrimination capacity of qEEG intermediate theta activity during maintenance and slow delta activity in emergence were interesting findings of this study not previously published as far as we know. Significant correlations between entropy with qEEG fast activity from induction to maintenance is another interesting finding of our study, probably indicating that fast activity decreasing as hypnosis deepens according to gold standard qEEG could be reflected for a faster monitoring with entropy, as evidence has been previously showed (18). ETAC showed significant changes during induction, laryngoscopy and emergence as a result of our study protocol including volatile anesthetic administration immediately after orotraqueal intubation. ETAC had a higher discrimination capacity from laryngoscopy to maintenance, and linear regression analysis showed that entropy does not influence variability of ETAC, probably indicating that entropy and ETAC should be complementary deep of hypnosis monitoring during balanced general anesthesia with volatile hypnotic agent.
Corrected variables could have advantages for deep of hypnosis monitoring reflecting balance between fast and slow activity. In this study, BcSEF showed significant changes from induction to emergence of general anesthesia indicating hypnosis deepens during induction followed for a light anesthesia in laryngoscopy probably because of classic awake and a deep hypnosis level during maintenance probably because of effects of volatile anesthesia increasing BSR and decreasing SEF and paradoxically during emergence when anesthesia agent was withdrawal, but discrimination capacity was acceptable only in maintenance time. B/T reflected a higher hypnosis depth during induction probably resulting of beta activity decreased and theta waves increased because effects of intravenous propofol, followed for a lower hypnosis level only significant during emergence because increasing of beta waves and decreasing of theta activity secondary to hypnotic agent withdrawal, but discrimination capacity was only acceptable during maintenance probably reflecting a lower hypnosis level because of surgical stimulation during this time. B/D not significantly changed from induction to emergence probably resulting of a balance between beta and delta activity during these phases, but discrimination capacity was acceptable in induction and emergence probably indicating the importance of beta and delta activity for deep of hypnosis monitoring especially during induction and emergence. Acceptable discrimination capacity of BcSEF and B/T during maintenance and B/D during induction and emergence were interesting findings of our study not published as far as we know, probably useful for deep of hypnosis monitoring improvement.
fEMG is a source of interference for deep of hypnosis monitoring as evidence had showed [10]. In this study, fEMG significantly decrease during induction because of a lack of frontal neuromuscular activity resulting of action of NMBA administered in this phase for endotraqueal intubation facilitation in laryngoscopy time. fEMG increases during emergence because a frontal neuromuscular activity recovery at the end of surgical procedure to achieve awake and a safe extubation. A very interesting findings of our study were an excellent discrimination capacity of deep of hypnosis state of fEMG from induction to maintenance, decreasing but being good during emergence time and a contribution of fEMG for clinical status of awake vs sleep prediction according to our equation. A lack of correlation with SE and RE along with a lower capacity of fEMG to explained variability of SE and RE according to linear regression in this study was very interesting also, not expected because entropy algorithm includes fEMG band in RE value.
Hemodynamic variables influence for deep of hypnosis monitoring are controversial. In this study, SBP significantly decrease during induction probably because of propofol administration and maintenance resulting of moderate surgical stimulation balanced for effects of volatile anesthetics along with opioids according to anesthesiologist in charge decision without influence of deep of hypnosis monitoring using EEG variables. SBP and HR significantly increased in laryngoscopy and emergence probably because of higher nociceptive stimulation of sympathetic system during these phases. SBP showed only an acceptable discrimination capacity during induction without showed significant correlations with entropy and EEG variables. HR not significantly changed during induction probably resulting of a lack of reflex tachycardia after regular doses of propofol given according to study protocol reflecting our current hospital practice. HR significantly decreased during maintenance probably because of moderate surgical stimulation under effects of volatile and opioid drugs. HR showed a poor discrimination capacity in all phases without correlating with entropy and EEG variables. These results taken together seems to indicate that SBP and HR showed effects of nociceptive stimulation during laryngoscopy and emergence, but its poor discrimination capacity of hypnosis level make these variables not useful for deep of hypnosis monitoring improvement.
Variables influencing changes of entropy and ETAC during general anesthesia in this study should reflect potentially tools for deep of hypnosis monitoring improvement. fEMG and ETAC influence for entropy monitoring has been described earlier [2,417], but in this study interestingly nor fEMG or ETAC could explained variability of entropy values. EGG slow and intermediate activity could indicate transition to a deeper hypnosis level but are less important for entropy monitoring algorithm according to our results and previously published papers [2,417]. In present study ETAC variability was not explained for entropy unlike evidence had showed earlier [2]. We analyzed prediction capacity of hypnosis state of entropy and variables that showed a poor prediction capacity of entropy variability also with a limited value for deep of hypnosis monitoring in our days. Combination of RE, fEMG, delta and theta activity showed an excellent discrimination capacity of hypnosis state in present study. This was a very interesting finding of our study not published as far as we know that reflects the importance of include fast, intermediate and slow activity for deep of hypnosis monitoring improving.
This study has limitations because its little size and single group of female young patients under short anesthesia for scheduled gynecology procedures. For this reason, our results needs to further confirmation for larger studies.
In conclusion, this study showed that entropy had an excellent discrimination capacity of hypnosis state from induction to emergence of general anesthesia. Corrected EEG variables B/D during induction and emergence or BcSEF and B/T during maintenance reflecting balance between slow and fast activity showed a good discrimination capacity with potential for deep of hypnosis monitoring improvement. Combination of fEMG, RE, delta and theta activities showed an excellent discrimination capacity of hypnosis state highlighting the importance of include fast, intermediate and slow activity for a more accurate deep of hypnosis monitoring during general anesthesia. Our interesting findings needs further investigation in larger studies to assess performance of EEG corrected variables, entropy and EEG fast, slow and intermediate activity for deep of hypnosis monitoring improvement.
References:
 Klein AA, Meek T, Allcock E, et al. Recommendations for standards of monitoring during anaesthesia and recovery 2021. Guideline from the Association of Anaesthetists. Anaesthesia 76 (2021): 12121223.
 Ellerkmann RK, Liermann VM, Alves TM, et al. Spectral Entropy and Bispectral Index as Measures of the Electroencephalographic Effects of Sevoflurane. Anesthesiology 101 (2004): 12751282.
 El Hor T, Van Der Linden P, De Hert S, et al. Impact of Entropy Monitoring on Volatile Anesthetic Uptake. Anesthesiology 118 (2013): 6873.
 Schmidt GN, Bischoff P, Standl T, et al. Comparative evaluation of the DatexOhmeda S/5 entropy module and the bispectral index monitor during propofolremifentanil anesthesia. Anesthesiology 101 (2004): 128390.
 Aho AJ, Lyytikainen LP, YliHankala A, et al. Explaining Entropy responses after a noxious stimulus, with or without neuromuscular blocking agents, by means of the raw electroencephalographic and electromyographic characteristics. British Journal of Anaesthesia 106 (2011): 6976.
 Takamatsu I, Ozaki M, Kazama T. Entropy indices vs the bispectral index TM for estimating nociception during sevoflurane anaesthesia. British Journal of Anaesthesia 96 (2006): 6206.
 Aho A.J, YliHankala A, Lyytikäinen LP, et al. Can electromyographic arousal be detected visually on the DatexOhmeda S/5TM Anesthesia Monitor? Acta Anaesthesiol Scand 57 (2013): 36472.
 Baulig W, Seifert B, Schmid E, et al. Comparison of Spectral Entropy and Bispectral Index Electroencephalography in Coronary Artery Bypass Graft Surgery. Journal of Cardiothoracic and Vascular Anesthesia 24 (2010): 54449.
 Musialowicz T, Lahtinen P, Pitkanen O, et al. Comparison of spectral entropy and BIS Vista monitor during general anesthesia for cardiac surgery. Journal of Clinical Monitoring and Computing 25 (2011): 95103.
 ViertioOja H, Maja V, Sarkela M, et al. Description of the Entropy algorithm as applied in the DatexOhmeda S ⁄ 5 Entropy Module. Acta Anaesthesiologica Scandinavica 48 (2004): 15461.
 Gruenewald M, Zhou J, Schloemerkemper N, et al. MEntropy guidance vs standard practice during propofolremifentanil anaesthesia: a randomised controlled trial. Anaesthesia 2007; 62:12249. DOI: 10.1111/j.13652044.2007.05252.x
 Ellerkmann RK, Soehle M, Alves TM, et al. Spectral Entropy and Bispectral Index as Measures of the Electroencephalographic Effects of Propofol. Anesthesia and Analgesia 102 (2006): 145662.
 Meybohm P M, Gruenewald M, Hocker J, et al. Correlation and agreement between the bispectral index vs. state entropy during hypothermic cardiopulmonary bypass. Acta Anaesthesiol Scand 54 (2010): 16975.
 Iannuzzi M, Iannuzzi E, Rossi F, et al. Relationship between bispectral index, electro encephalographic state entropy and effectsite EC50 for propofol at different clinical endpoints. Br J Anaesth 95 (2005): 11724.
 Anderson RE, Barr G, Owall A, et al. Entropy during propofol hypnosis, including an episode of wakefulness. Anaesthesia 59 (2004): 7212
 Gruenewald M, Ilies C. Monitoring the nociceptionantinociception balance. Best Practice & Research Clinical Anaesthesiology 27 (2013): 23547.
 Lysakowski C, Elia N, Czarnetzki C, et al. Bispectral and spectral entropy indices at propofolinduced loss of consciousness in young and elderly patients. British Journal of Anaesthesia 103 (2009): 387393.
 Vakkuri A, YliHankala A, Talja P, et al. Timefrequency balanced spectral entropy as a measure of anesthetic drug effect in central nervous system during sevoflurane, propofol, and thiopental anesthesia. Acta Anaesthesiol Scand 48 (2004): 14553.
 Wu SC, Wang PC, Liao WT, et al. Use of Spectral Entropy Monitoring in Reducing the Quantity of Sevoflurane as Sole Inhalational Anesthetic and in Decreasing the Need for Antihypertensive Drugs in Total Knee Replacement Surgery. Acta Anaesthesiol Taiwan 46 (2008): 106−11.
 Riad W, Schreiber M, Saeed AB. Monitoring with EEG entropy decreases propofol requirement and maintains cardiovascular stability during induction of anaesthesia in elderly patients. Eur J Anaesthesiol 24 (2007):684−8.
 Chhabra A, Subramaniam R, Srivastava A, et al. Spectral entropy monitoring for adults and children undergoing general anaesthesia. Cochrane Database of Systematic Reviews 3 (2016).
 Gibbs FA, Gibbs EL, LennoxWG. Effect on the electroencephalogram of certain drugs which influence nervous activity. Arch Intl Med 60 (1937): 154156.
 Bartel PR, Smith FJ, Becker PJ. A comparison of EEG spectral entropy with conventional quantitative EEG at varying depths of sevoflurane anesthesia. Southern African Journal of Anesthesia and Analgesia (2005): 8993.
 McKay IDH, Voss LJ, Sleigh JW, et al. PharmacokineticPharmacodynamic Modeling the Hypnotic Effect of Sevoflurane Using the Spectral Entropy of the Electroencephalogram. Anesthesia and Analgesia 102 (2006): 917.