Assessing rheoencephalography dynamics through analysis of the interactions among brain and cardiac networks during general anesthesia.

Cerebral blood flow (CBF) reflects the rate of delivery of arterial blood to the brain. Since no nutrients, oxygen or water can be stored in the cranial cavity due to space and pressure restrictions, a continuous perfusion of the brain is critical for survival. Anesthetic procedures are known to affect cerebral hemodynamics, but CBF is only monitored in critical patients due, among others, to the lack of a continuous and affordable bedside monitor for this purpose. A potential solution through bioelectrical impedance technology, also known as rheoencephalography (REG), is proposed, that could fill the existing gap for a low-cost and effective CBF monitoring tool. The underlying hypothesis is that REG signals carry information on CBF that might be recovered by means of the application of advanced signal processing techniques, allowing to track CBF alterations during anesthetic procedures. The analysis of REG signals was based on geometric features extracted from the time domain in the first place, since this is the standard processing strategy for this type of physiological data. Geometric features were tested to distinguish between different anesthetic depths, and they proved to be capable of tracking cerebral hemodynamic changes during anesthesia. Furthermore, an approach based on Poincaré plot features was proposed, where the reconstructed attractors form REG signals showed significant differences between different anesthetic states. This was a key finding, providing an alternative to standard processing of REG signals and supporting the hypothesis that REG signals do carry CBF information. Furthermore, the analysis of cerebral hemodynamics during anesthetic procedures was performed by means of studying causal relationships between global hemodynamics, cerebral hemodynamics and electroencephalogram (EEG) based-parameters. Interactions were detected during anesthetic drug infusion and patient positioning (Trendelenburg positioning and passive leg raise), providing evidence of the causal coupling between hemodynamics and brain activity. The provided alternative of REG signal processing confirmed the hypothesis that REG signals carry information on CBF. The simplicity of the technology, together with its low cost and easily interpretable outcomes, should provide a new opportunity for REG to reach standard clinical practice. Moreover, causal relationships among the hemodynamic physiological signals and brain activity were assessed, suggesting that the inclusion of REG information in depth of anesthesia monitors could be of valuable use to prevent unwanted CBF alterations during anesthetic procedures.

Semimechanistic models to relate noxious stimulation, movement, and pupillary dilation responses in the presence of opioids.

Intraoperative targeting of the analgesic effect still lacks an optimal solution. Opioids are currently the main drug used to achieve antinociception, and although underdosing can lead to an increased stress response, overdose can also lead to undesirable adverse effects. To better understand how to achieve the optimal analgesic effect of opioids, we studied the influence of remifentanil on the pupillary reflex dilation (PRD) and its relationship with the reflex movement response to a standardized noxious stimulus. The main objective was to generate population pharmacodynamic models relating remifentanil predicted concentrations to movement and to pupillary dilation during general anesthesia. A total of 78 patients undergoing gynecological surgery under general anesthesia were recruited for the study. PRD and movement response to a tetanic stimulus were measured multiple times before and after surgery. We used nonlinear mixed effects modeling to generate a population pharmacodynamic model to describe both the time profiles of PRD and movement responses to noxious stimulation. Our model demonstrated that movement and PRD are equally depressed by remifentanil. Using the developed model, we changed the intensity of stimulation and simulated remifentanil predicted concentrations maximizing the probability of absence of movement response. An estimated effect site concentration of 2 ng/ml of remifentanil was found to inhibit movement to a tetanic stimulation with a probability of 81%.

Influence of an "Electroencephalogram-Based" Monitor Choice on the Delay Between the Predicted Propofol Effect-Site Concentration and the Measured Drug Effect.

BACKGROUND: Clinicians can optimize propofol titration by using 2 sources of pharmacodynamic (PD) information: the predicted effect-site concentration for propofol (Ceprop) and the electroencephalographically (EEG) measured drug effect. Relation between these sources should be time independent, that is, perfectly synchronized. In reality, various issues corrupt time independency, leading to asynchrony or, in other words, hysteresis. This asynchrony can lead to conflicting information, making effective drug dosing challenging. In this study, we tried to quantify and minimize the hysteresis between the Ceprop (calculated using the Schnider model for propofol) and EEG measured drug effect, using nonlinear mixed-effects modeling (NONMEM). Further, we measured the influence of EEG-based monitor choice, namely Bispectral index (BIS) versus qCON index (qCON) monitor, on propofol PD hysteresis. METHODS: We analyzed the PD data from 165 patients undergoing propofol-remifentanil anesthesia for outpatient surgery. Drugs were administered using target-controlled infusion (TCI) pumps. Pumps were programmed with Schnider model for propofol and Minto model for remifentanil. We constructed 2 PD models (direct models) relating the Schnider Ceprop to the measured BIS and qCON monitor values. We quantified the models' misspecification due to hysteresis, on an individual level, using the root mean squared errors (RMSEs). Subsequently, we optimized the PD models' predictions by adding a lag term to both models (lag-time PD models) and quantified the optimization using the RMSE. RESULTS: There is a counterclockwise hysteresis between Ceprop and BIS/qCON values. Not accounting for this hysteresis results in a direct PD model with an effect-site concentration which produces 50% of the maximal drug effect (Ce50) of 6.24 and 8.62 µg/mL and RMSE (median and interquartile range [IQR]) of 9.38 (7.92-11.23) and 8.41(7.04-10.2) for BIS and qCON, respectively. Adding a modeled lag factor of 49 seconds to the BIS model and 53 seconds to the qCON model improved both models' prediction, resulting in similar Ce50 (3.66 and 3.62 µg/mL for BIS and qCON) and lower RMSE (median (IQR) of 7.87 (6.49-9.90) and 6.56 (5.28-8.57) for BIS and qCON. CONCLUSIONS: There is a significant "Ceprop versus EEG measured drug effect" hysteresis. Not accounting for it leads to conflicting PD information and false high Ce50 for propofol in both monitors. Adding a lag term improved the PD model performance, improved the "pump-monitor" synchrony, and made the estimates of Ce50 for propofol more realistic and less monitor dependent.

Refined Multiscale Entropy Using Fuzzy Metrics: Validation and Application to Nociception Assessment.

The refined multiscale entropy (RMSE) approach is commonly applied to assess complexity as a function of the time scale. RMSE is normally based on the computation of sample entropy (SampEn) estimating complexity as conditional entropy. However, SampEn is dependent on the length and standard deviation of the data. Recently, fuzzy entropy (FuzEn) has been proposed, including several refinements, as an alternative to counteract these limitations. In this work, FuzEn, translated FuzEn (TFuzEn), translated-reflected FuzEn (TRFuzEn), inherent FuzEn (IFuzEn), and inherent translated FuzEn (ITFuzEn) were exploited as entropy-based measures in the computation of RMSE and their performance was compared to that of SampEn. FuzEn metrics were applied to synthetic time series of different lengths to evaluate the consistency of the different approaches. In addition, electroencephalograms of patients under sedation-analgesia procedure were analyzed based on the patient's response after the application of painful stimulation, such as nail bed compression or endoscopy tube insertion. Significant differences in FuzEn metrics were observed over simulations and real data as a function of the data length and the pain responses. Findings indicated that FuzEn, when exploited in RMSE applications, showed similar behavior to SampEn in long series, but its consistency was better than that of SampEn in short series both over simulations and real data. Conversely, its variants should be utilized with more caution, especially whether processes exhibit an important deterministic component and/or in nociception prediction at long scales.

Poincaré plot analysis of cerebral blood flow signals: Feature extraction and classification methods for apnea detection.

OBJECTIVE: Rheoencephalography is a simple and inexpensive technique for cerebral blood flow assessment, however, it is not used in clinical practice since its correlation to clinical conditions has not yet been extensively proved. The present study investigates the ability of Poincaré Plot descriptors from rheoencephalography signals to detect apneas in volunteers. METHODS: A group of 16 subjects participated in the study. Rheoencephalography data from baseline and apnea periods were recorded and Poincaré Plot descriptors were extracted from the reconstructed attractors with different time lags (τ). Among the set of extracted features, those presenting significant differences between baseline and apnea recordings were used as inputs to four different classifiers to optimize the apnea detection. RESULTS: Three features showed significant differences between apnea and baseline signals: the Poincaré Plot ratio (SDratio), its correlation (R) and the Complex Correlation Measure (CCM). Those differences were optimized for time lags smaller than those recommended in previous works for other biomedical signals, all of them being lower than the threshold established by the position of the inflection point in the CCM curves. The classifier showing the best performance was the classification tree, with 81% accuracy and an area under the curve of the receiver operating characteristic of 0.927. This performance was obtained using a single input parameter, either SDratio or R. CONCLUSIONS: Poincaré Plot features extracted from the attractors of rheoencephalographic signals were able to track cerebral blood flow changes provoked by breath holding. Even though further validation with independent datasets is needed, those results suggest that nonlinear analysis of rheoencephalography might be a useful approach to assess the correlation of cerebral impedance with clinical changes.

Time-frequency Features for Impedance Cardiography Signals During Anesthesia Using Different Distribution Kernels.

OBJECTIVE: This works investigates the time-frequency content of impedance cardiography signals during a propofol-remifentanil anesthesia. MATERIALS AND METHODS: In the last years, impedance cardiography (ICG) is a technique which has gained much attention. However, ICG signals need further investigation. Time-Frequency Distributions (TFDs) with 5 different kernels are used in order to analyze impedance cardiography signals (ICG) before the start of the anesthesia and after the loss of consciousness. In total, ICG signals from one hundred and thirty-one consecutive patients undergoing major surgery under general anesthesia were analyzed. Several features were extracted from the calculated TFDs in order to characterize the time-frequency content of the ICG signals. Differences between those features before and after the loss of consciousness were studied. RESULTS: The Extended Modified Beta Distribution (EMBD) was the kernel for which most features shows statistically significant changes between before and after the loss of consciousness. Among all analyzed features, those based on entropy showed a sensibility, specificity and area under the curve of the receiver operating characteristic above 60%. CONCLUSION: The anesthetic state of the patient is reflected on linear and non-linear features extracted from the TFDs of the ICG signals. Especially, the EMBD is a suitable kernel for the analysis of ICG signals and offers a great range of features which change according to the patient's anesthesia state in a statistically significant way.

Modeling Respiratory Depression Induced by Remifentanil and Propofol during Sedation and Analgesia Using a Continuous Noninvasive Measurement of pCO2.

Respiratory depression is a common adverse effect of propofol and remifentanil. We aimed to develop a model for respiratory depressant effects of propofol with remifentanil in patients undergoing endoscopy with sedation. Data were available for 136 patients undergoing endoscopy with sedation. Participants randomly received infusions of propofol and remifentanil. Predicted plasma concentrations, outputted by infusion pumps, were available. Transcutaneous arterial pressure of carbon dioxide (pCO2) was measured. Data were analyzed using nonlinear mixed-effects modeling methods. Covariate relationships were investigated for age, noxious stimuli (endoscopy tube insertion), and A118G genotype for the µ-opioid receptor (OPRM1). Participants had a median (range) age of 64.0 (25.0-88.0) years, weight of 70.0 (35.0-98.0) kg, and height of 164.0 (147.0-190.0) cm. Seven percent were recessive homozygous for OPRM1 polymorphism. An indirect-effect model with a "modulator" compartment best described pCO2 data (P < 0.001) over a direct-effect model. Remifentanil inhibited pCO2 removal with an IC50 of 1.13 ng/ml and first-order rate constant (ke 0) of 0.28 minute(-1). Propofol affected the modulator compartment with an IC50 of 4.97 µg/ml (no effect-site compartment). Propofol IC50 and remifentanil ke 0 were reduced with increasing age. Noxious stimuli and genotype were not significant covariates. An indirect-effect model with a rebound mechanism can describe remifentanil- and propofol-induced changes in pCO2 in patients undergoing noxious procedures. The model may be useful for identifying optimal dosing schedules for these drugs in a combination that provides adequate sedation but avoids respiratory depression.

Interaction between EEG and drug concentration to predict response to noxious stimulation during sedation-analgesia: effect of the A118G genetic polymorphism.

The level of sedation in patients undergoing medical procedures is affected by the interaction between the effect of the anesthetic and analgesic agents and the pain stimuli. The presence of the A118G single nucleotide polymorphism (SNP) in the OPRM1 gene affects the requirements of opioids for patients undergoing sedation-analgesia. The purpose of this work is to evaluate the influence of the SNP A118G in OPRM1 on EEG measures for the prediction of the response to pain stimulation during endoscopy procedure. The proposed measures were based on power spectral density and auto-mutual information function. It was found that the statistical performances of the EEG measures improved when the presence of the SNP was taken into account (prediction probability Pk>0.9).

Prediction of nociceptive responses during sedation by time-frequency representation.

The level of sedation in patients undergoing medical procedures evolves continuously, such as the effect of the anesthetic and analgesic agents is counteracted by pain stimuli. The monitors of depth of anesthesia, based on the analysis of the electroencephalogram (EEG), have been progressively introduced into the daily practice to provide additional information about the state of the patient. However, the quantification of analgesia still remains an open problem. The purpose of this work is to analyze the capability of prediction of nociceptive responses based on the time-frequency representation (TFR) of EEG signal. Functions of spectral entropy, instantaneous power and instantaneous frequency were calculated in order to predict the presence or absence of the nociceptive responses to different stimuli during sedation in endoscopy procedure. Values of prediction probability of Pk above 0.75 and percentages of sensitivity and specificity above 70% and 65% respectively were achieved combining TFR functions with bispectral index (BIS) and with concentrations of propofol (CeProp) and remifentanil (CeRemi).

Modeling the influence of the A118G polymorphism in the OPRM1 gene and of noxious stimulation on the synergistic relation between propofol and remifentanil: sedation and analgesia in endoscopic procedures.

BACKGROUND: The presence of the A118G single nucleotide polymorphism in the OPRM1 gene as well as noxious stimulation might affect the requirements of remifentanil for patients undergoing ultrasonographic endoscopy under sedation-analgesia with propofol and remifentanil. Bispectral index (BIS) was used as a surrogate measure of effect. METHOD: A total of 207 patients were screened for A118G and randomly received different combinations of propofol and remifentanil, changed depending on the nausea response to endoscopy tube introduction. Nonlinear mixed effects modelling was used to establish the relation between propofol and remifentanil with respect to BIS and to investigate the influence of A118G or noxious stimulation. The value of k e0 for propofol and remifentanil was estimated to avoid the hysteresis between predicted effect site concentration (Ce) and BIS. RESULTS: Data from 176 patients were analysed. Eleven were recessive homozygous for A118G (OPRM = 1). A total of 165 patients were either dominant homozygous or heterozygous and considered normal (OPRM = 0). The estimated values of k e0 for propofol and remifentanil were 0.122 and 0.148 min(-1). Propofol and remifentanil were synergistic with respect to the BIS (α = 1.85). EC50 estimate for propofol was 3.86 µg/ml and for remifentanil 19.6 ng/ml in normal patients and 326 ng/ml in OPRM = 1 patients. BIS increases around 4% for the same effect site concentrations with noxious stimulation. CONCLUSIONS: Predicted effect site concentration of remifentanil ranging 1-5 ng/ml synergistically potentiates the effects of propofol on the BIS but has no effect in A118G patients. Noxious stimulation increases BIS values by 4% at the same concentrations of propofol and remifentanil.

Validation of the qCO cardiac output monitor during Valsalva maneuver.

Monitoring cardiac output for a variety of patient conditions is essential to ensure tissue perfusion and oxygenation. Cardiac output can be measured either invasively using a pulmonary artery catheter or non-invasively using impedance cardiography (ICG). The objective of the present study was to validate a cardiac output monitor, the qCO (Quantium Medical, Barcelona, Spain). The qCO is based on the ICG principle. Twenty-five volunteers (18-75 years) were enrolled in the study. The duration of the study was 10 min. The subjects were asked to rest quietly in an armchair for a duration of 5 min. At 5 min they were asked to do a Valsalva maneuver which is known to decrease the cardiac output. The baseline value of the normalized cardiac output (qCO index) was compared with the minimum value during the Valsalva maneuver. The results showed (t-test, p<0.0005) significant difference between the cardiac output estimated at baseline and during the Valsalva maneuver. In conclusion, the qCO was able to indicate trend changes of the cardiac output in volunteers.

Auto-mutual information function of the EEG as a measure of depth of anesthesia.

Monitoring the depth of anesthesia (DOA) is necessary in order to decrease the incident of awareness in anesthesia and to prevent delays in the recovery phase. In the last decades a number of noninvasive methods have been proposed for the analysis of the electroencephalogram (EEG) for monitoring DOA. The objective of this work was to apply auto mutual information function (AMIF) to EEGs of patients under anesthesia in order to find variables able to characterize the following 4 states: awake, sedated, anesthetized and burst suppression episodes. The results show that the single and combined AMIF parameters were able to correctly classify the states in the range 72.2%-94.1% and 61.1%-100%, respectively.

Assessment of the depth of anesthesia based on symbolic dynamics of the EEG.

Methodologies based on symbolic dynamics have successfully demonstrated to reflect the nonlinear behavior of biological signals. In the present study, symbolic dynamics was applied to the electroencephalogram (EEG) in order to describe the level of depth of anesthesia. The EEG was transformed to symbol sequences. Words of three symbols were built from this symbolic series. The results obtained from the EEGs of 36 patients undergoing anesthesia showed that the probabilities of the word types were able to reflect the depth of anesthesia in a similar way to the auditory evoked potential index AAI, a commercial index.

Validation of the Index of Consciousness (IoC) during sedation/analgesia for ultrasonographic endoscopy.

The level of consciousness of patients undergoing surgery evolves continuously as the effect of the anesthetic agents is counteracted by the surgical stimuli. The monitors of depth of anesthesia, based on the analysis of the electroencephalogram (EEG), have been progressively introduced into the daily practice to provide additional information about the state of the patient. In this work, the Index of Consciousness (IoC), a recent index which uses the information from EEG spectrum and symbolic dynamics through a fuzzy inference system, is introduced. The bispectral index BIS, a well-established index, is used to validate the IoC. A good correlation is found between both indexes. Furthermore, IoC presents a significantly higher prediction probability Pk of the level of sedation than BIS, and can thus be considered as an interesting measure of the level of consciousness.

Depth of anesthesia index using cumulative power spectrum.

Over the last ten years, monitors of depth of anesthesia have progressively been integrated in the clinical practice. Based on the analysis of the electroencephalogram (EEG), these monitors deliver an index that helps the anesthesiologist to determine the state of the patient during the surgery. Although they employ different kind of algorithms, spectral parameters are always taken into account to achieve the final indexes. In this work, a new spectral parameter based on the cumulative power spectrum is proposed. When compared to the Spectral Edge Frequency (SEF), a classic spectral parameter, the Cumulative Power Spectrum Index (CPSI) presents a higher correlation with reference indexes (AAI, BIS and CePROP) and a higher prediction probability of the state of the patient. Furthermore, when compared to the reference indexes, the CPSI shows similar performances in terms of correlation and presents a higher prediction probability than two of them (BIS and CePROP).

Detrended fluctuation analysis of EEG as a measure of depth of anesthesia.

For several decades, a number of methods have been developed for the noninvasive assessment of the level of consciousness during general anesthesia. In this paper, detrended fluctuation analysis is used to study the scaling behavior of the electroencephalogram as a measure of the level of consciousness. Three indexes are proposed in order to characterize the patient state. Statistical analysis demonstrates that they allow significant discrimination between the awake, sedated and anesthetized states. Two of them present a good correlation with established indexes of depth of anesthesia. The scaling behavior has been found related to the depth of anesthesia and the methodology allows real-time implementation, which enables its application in monitoring devices.

Cerebral state index during propofol anesthesia: a comparison with the bispectral index and the A-line ARX index.

BACKGROUND: The objective of this study was to prospectively test the Cerebral State Index designed for measuring the depth of anesthesia. The Cerebral State Index is calculated using a fuzzy logic combination of four subparameters of the electroencephalographic signal. The performance of the Cerebral State Index was compared with that of the Bispectral Index and the A-Line ARX Index. METHODS: This study applied raw data from two previously published clinical protocols. The patients in protocol 1 were given a continuous propofol infusion, 300 ml/h, until 80% of burst suppression occurred. In protocol 2, a stepwise increased target-controlled infusion of propofol was administered to patients until loss of response to noxious stimuli while the Observer's Assessment of Alertness and Sedation was registered every 4 min. The Cerebral State Index was calculated off-line from the recorded electroencephalographic data. The Spearman rank correlation coefficient between electronic indices and the effect site concentration of propofol was calculated along with the prediction probability of each index to predict the Observer's Assessment of Alertness and Sedation level. RESULTS: The Spearman rank correlation coefficients between the Cerebral State Index, Bispectral Index, and A-Line ARX Index and the propofol effect site concentration were -0.94, -0.89, and -0.82, respectively, in protocol 1, whereas the prediction probability values between the Cerebral State Index, Bispectral Index, and A-Line ARX Index and the Observer's Assessment of Alertness and Sedation score in protocol 2 were 0.92, 0.93, and 0.91, respectively. CONCLUSION: The Cerebral State Index detects well the graduated levels of propofol anesthesia when compared with the propofol effect site concentration and the Observer's Assessment of Alertness and Sedation score.

Comparison of conventional averaged and rapid averaged, autoregressive-based extracted auditory evoked potentials for monitoring the hypnotic level during propofol induction.

BACKGROUND: The extraction of the middle latency auditory evoked potentials (MLAEP) is usually done by moving time averaging (MTA) over many sweeps (often 250-1,000), which could produce a delay of more than 1 min. This problem was addressed by applying an autoregressive model with exogenous input (ARX) that enables extraction of the auditory evoked potentials (AEP) within 15 sweeps. The objective of this study was to show that an AEP could be extracted faster by ARX than by MTA and with the same reliability. METHODS: The MTA and ARX methods were compared with the Modified Observer's Assessment of Alertness and Sedation Scale (MOAAS) in 15 patients scheduled for cardiac surgery and anesthetized with propofol. The peak amplitudes and latencies were recorded continuously for the MTA- and ARX-extracted AEP. An index, AAI, was derived from the ARX-extracted AEP as well. RESULTS: The best predictors of the awake and anesthetized states, in terms of the prediction probability, Pk, were the AAI (Pk [SE] = 0.93 [0.01]) and Na-Pa amplitude (MTA, Pk [SE] = 0.89 [0.02]; ARX, Pk [SE] = 0.87[0.02]). When comparing the AAI at the MOAAS levels 5-3 versus 2-0, significant differences were achieved. During the transitions from awake to asleep, the ARX-extracted AEP were obtained with significantly less delay than the MTA-extracted AEP (28.4 s vs. 6 s). CONCLUSION: The authors conclude that the MLAEP peaks and the AAI correlate well to the MOAAS, whether extracted by MTA or ARX, but the ARX method produced a significantly shorter delay than the MTA.