Comparison of bispectral index, 95% spectral edge frequency and approximate entropy of the EEG, with changes in heart rate variability during induction of general anaesthesia

Heart rate variability as a predictor of intraoperative autonomic nervous system homeostasis

The aim of the proof-of-concept study is to investigate the level of concordance between the heart rate variability (HRV), the EEG-based Narcotrend Index as a surrogate marker for the depth of hypnosis, and the minimal alveolar concentration (MAC) of the inhalation anesthetic sevoflurane across the entire course of a surgical procedure. This non-blinded cross-sectional study recorded intraoperative HRV, Narcotrend Index, and MAC in 31 male patients during radical prostatectomy using the Da-Vinci robotic-assisted surgical system at Mannheim University Medical Center. The degree of concordance was calculated using repeated measures correlation with the R package (rmcorr) and presented using the rmcorr coefficient (rrm). The Narcotrend Index correlates significantly across all measures with the time-dependent parameter of HRV, the standard deviation of the means of RR intervals (SDNN) (rrm = 0.2; p < 0.001), the frequency-dependent parameters low frequency (LF) (rrm = 0.09; p = 0.04) and the low frequency/high frequency ratio (LF/HF ratio) (rrm = 0.11; p = 0.002). MAC correlated significantly negatively with the time-dependent parameter of heart rate variability, SDNN (rrm = -0.28; p < 0.001), the frequency-dependent parameter LF (rrm = -0.06; p < 0.001) and the LF/HF ratio (rrm = -0.18; p < 0.001) and the Narcotrend Index (rrm = -0.49; p < 0.001) across all measures. HRV mirrors the trend of the Narcotrend Index used to monitor depth of hypnosis and the inhibitory influence of the anesthetic sevoflurane on the autonomic nervous system. Therefore, HRV can provide essential information about the homeostasis of the autonomic nervous system during general anesthesia. DRKS00024696, March 9th, 2021.

Comparative Electroencephalographic Profile of a New Anesthetic and Anticonvulsant That Is Selective for the GABA A R Slow Receptor Subtype

BACKGROUND

Anesthetics like propofol increase electroencephalography (EEG) power in delta frequencies (0.1-4 Hz), with a decrease of power in bandwidths >30 Hz. Propofol is nonselective for gamma amino butyric acid type A receptor subtypes (GABA A R) as it enhances all 3 GABA A R subtypes (slow, fast, and tonic). Our newly developed anesthetic class selectively targets GABA A R-slow synapses to depress brain responsiveness. We hypothesized that a selective GABA A R-slow agonist, KSEB 01-S2, would produce a different EEG signature compared to the broad-spectrum GABA A R agonist (propofol), and tested this using rat EEG recordings.

METHODS

Male rats were studied after Institutional Animal Care and Use Committees (IACUC) approval from the US Army Medical Research Institute of Chemical Defense and the University of Michigan. Rats were anesthetized using isoflurane (3%-5% induction, 1%-3% maintenance) with oxygen at 0.5 to 1.0 L/min. Stainless steel screws were placed in the skull and used to record subcranial cortical EEG signals. After recovery, either propofol or KSEB 01-S2 was administered and effects on EEG signals were analyzed.

RESULTS

As previously reported, propofol produced increased power in delta frequencies (0.1-4 Hz) compared to predrug recordings and produced a decrease in EEG power >30 Hz but no significant changes were seen within ±20 seconds of losing the righting reflex. By contrast, KSEB 01-S2 produced a significant increase in theta frequency percent power (median 14.7%, 16.2/13.8, 75/25 confidence interval; to 34.7%, 35/31.8; P < .015) and a significant decrease in low gamma frequency percent power (16.9%, 18.6/15.8; to 5.45%, 5.5/5.39; P < .015) for all rats at ± 20 seconds of loss of consciousness (LOC). Both anesthetics produced a flattening of chaotic attractor plots from nonlinear dynamic analyses, like that produced by volatile and dissociative anesthetics at LOC.

CONCLUSIONS

KSEB 01-S2 produced a markedly different EEG pattern, with a selective increase observed in the theta frequency range. KSEB 01-S2 also differs markedly in its activity at the GABA A R-slow receptor subtype, suggesting a possible mechanistic link between receptor subtype specificity and EEG frequency band signatures. Increased theta together with depressed gamma frequencies is interesting because GABA A R slow synapses have previously been suggested to underlie theta frequency oscillations, while fast synapses control gamma activity. These reciprocal effects support a previous model for theta and nested gamma oscillations based on inhibitory connections between GABA A R fast and slow interneurons. Although each anesthetic produced a unique EEG response, propofol and KSEB 01-S2 both increased slow wave activity and flattened chaotic attractor plots at the point of LOC.

Nonlinear analysis of neuronal firing modulated by sinusoidal stimulation at axons in rat hippocampus

Introduction

Electrical stimulation of the brain has shown promising prospects in treating various brain diseases. Although biphasic pulse stimulation remains the predominant clinical approach, there has been increasing interest in exploring alternative stimulation waveforms, such as sinusoidal stimulation, to improve the effectiveness of brain stimulation and to expand its application to a wider range of brain disorders. Despite this growing attention, the effects of sinusoidal stimulation on neurons, especially on their nonlinear firing characteristics, remains unclear.

Methods

To address the question, 50 Hz sinusoidal stimulation was applied on Schaffer collaterals of the rat hippocampal CA1 region in vivo. Single unit activity of both pyramidal cells and interneurons in the downstream CA1 region was recorded and analyzed. Two fractal indexes, namely the Fano factor and Hurst exponent, were used to evaluate changes in the long-range correlations, a manifestation of nonlinear dynamics, in spike sequences of neuronal firing.

Results

The results demonstrate that sinusoidal electrical stimulation increased the firing rates of both pyramidal cells and interneurons, as well as altered their firing to stimulation-related patterns. Importantly, the sinusoidal stimulation increased, rather than decreased the scaling exponents of both Fano factor and Hurst exponent, indicating an increase in the long-range correlations of both pyramidal cells and interneurons.

Discussion

The results firstly reported that periodic sinusoidal stimulation without long-range correlations can increase the long-range correlations of neurons in the downstream post-synaptic area. These results provide new nonlinear mechanisms of brain sinusoidal stimulation and facilitate the development of new stimulation modes.

Evaluating the performance of the cognitive workload model with subjective endorsement in addition to EEG

The aptitude-oriented exercises from almost all domains impose cognitive load on their operators. Evaluating such load poses several challenges owing to many factors like measurement mode and complexity, nature of the load, overloading conditions, etc. Nevertheless, the physiological measurement of a specific genre of cognitive load and subjective measurement have not been reported along with each other. In this study, the electroencephalography (EEG)-driven machine learning (Support Vector Machine (SVM)) model is sought along with the support of NASA's Task Load Index (NASA-TLX) rating scale for a novel purpose in workload exploration of operators. The Cognitive Load Theory (CLT) was used as the foundation to design the intrinsic stimulus (Spot the Difference task), as most workloads operators are exposed to are notably intrinsic. The SVM-based three-level classification accuracy ranged from 85.4 to 97.4% (p < 0.05), and the NASA-TLX-based three-level classification accuracy ranged from 88.33 to 97.33%. The t-test results show that the neurometric indices contributing to the classification significantly differed (p < 0.05) for every level. The NASA-TLX scale was utilised for validation in its basic form after the validity (Pearson correlation coefficients 0.338 to 0.805 (p < 0.05)) and reliability (Cronbach's α = 0.753) test. This modeling is beneficial to phase out particular-level cognitive exercises from the curriculum during under or overload workload (critical) conditions.

Towards a versatile mental workload modeling using neurometric indices

Researchers have been working to magnify mental workload (MWL) modeling for a long time. An important aspect of its modeling is feature selection as it interprets bulky and high-dimensional EEG data and enhances the accuracy of the classification model. In this study, a feature selection technique is proposed to obtain an optimized feature set with multiple domain features that can contribute to classifying the MWL at three distinct levels. The brain signals from thirteen healthy subjects were examined while they attended an intrinsic MWL of spotting differences in a set of similar pictures. The Recursive Feature Elimination (RFE) technique selects the robust features from the feature matrix by eliminating all the least contributing features. Along with the Support Vector Machine (SVM), the overall classification accuracy with the proposed RFE reached 0.913 from 0.791 surpassing the other techniques mentioned. The results of the study also significantly display the variation in the mean values of the selected features at the three workload levels (p<0.05). This model can become the principle for defining the workload level quantification applicable to diverse fields like neuroergonomics study, intelligent assistive devices (ADs) development, blue-chip technology exploration, cognitive evaluation of students, power plant operators, traffic operators, etc.

How to play under pressure: EEG monitoring of mental activation training in a professional tennis player

The aim of this study was to monitor the mental activation training during match pressure imageries using a protocol with (MT) and without mental training (wMT) performed in the office and on the tennis court based on the analysis of heart rate, brain waves and subjective ratings in a professional tennis player with high imagery experience. Results showed that both in the office (MTo/wMTo) and on the court (MTc/wMTc) the tennis player's heart rate increased in the match pressure imagery (I.3-8), being higher in the MTo. It decreased in the pressure imagery using mental tools (I.8-13) in the MT. In the case of brainwaves, beta and gamma waves increased in the match pressure imagery (I.3-8); while beta, gamma, delta and theta waves decreased in the pressure imagery using mental tools (I.8-13), being higher in the office. Entropy decreased in the match pressure imagery (I.3-8), being higher in the MTo. It increased in the pressure imagery using mental tools (I.8-13), being higher in the MTo. Regarding subjective ratings, the tennis player felt the pressure in the match pressure imagery, being higher in MT. In the pressure imagery using mental tools he regulated the activation to feel it at an optimal level (7). In the imagery reality, the olfactory and gustatory dimensions were the most difficult to feel in both imageries.

A Robust Bimodal Index Reflecting Relative Dynamics of EEG and HRV With Application in Monitoring Depth of Anesthesia

Supplemental information captured from HRV can provide deeper insight into nervous system function and consequently improve evaluation of brain function. Therefore, it is of interest to combine both EEG and HRV. However, irregular nature of time spans between adjacent heartbeats makes the HRV hard to be directly fused with EEG timeseries. Current study performed a pioneering work in integrating EEG-HRV information in a single marker called cumulant ratio, quantifying how far EEG dynamics deviate from self-similarity compared to HRV dynamics. Experimental data recorded using BrainStatus device with single ECG and 10 EEG channels from healthy-brain patients undergoing operation (N = 20) were used for the validation of the proposed method. Our analyses show that the EEG to HRV ratio of first, second and third cumulants gets systematically closer to zero with increase in depth of anesthesia, respectively 29.09%, 65.0% and 98.41%. Furthermore, extracting multifractality properties of both heart and brain activities and encoding them into a 3-sample numeric code of relative cumulants does not only encapsulates the comparison of two evenly and unevenly spaced variables of EEG and HRV into a concise unitless quantity, but also reduces the impact of outlying data points.

Consciousness and complexity: a consilience of evidence

Over the last years, a surge of empirical studies converged on complexity-related measures as reliable markers of consciousness across many different conditions, such as sleep, anesthesia, hallucinatory states, coma, and related disorders. Most of these measures were independently proposed by researchers endorsing disparate frameworks and employing different methods and techniques. Since this body of evidence has not been systematically reviewed and coherently organized so far, this positive trend has remained somewhat below the radar. The aim of this paper is to make this consilience of evidence in the science of consciousness explicit. We start with a systematic assessment of the growing literature on complexity-related measures and identify their common denominator, tracing it back to core theoretical principles and predictions put forward more than 20 years ago. In doing this, we highlight a consistent trajectory spanning two decades of consciousness research and provide a provisional taxonomy of the present literature. Finally, we consider all of the above as a positive ground to approach new questions and devise future experiments that may help consolidate and further develop a promising field where empirical research on consciousness appears to have, so far, naturally converged.

Regional entropy of functional imaging signals varies differently in sensory and cognitive systems during propofol-modulated loss and return of behavioral responsiveness

The level and richness of consciousness depend on information integration in the brain. Altered interregional functional interactions may indicate disrupted information integration during anesthetic-induced unconsciousness. How anesthetics modulate the amount of information in various brain regions has received less attention. Here, we propose a novel approach to quantify regional information content in the brain by the entropy of the principal components of regional blood oxygen-dependent imaging signals during graded propofol sedation. Fifteen healthy individuals underwent resting-state scans in wakeful baseline, light sedation (conscious), deep sedation (unconscious), and recovery (conscious). Light sedation characterized by lethargic behavioral responses was associated with global reduction of entropy in the brain. Deep sedation with completely suppressed overt responsiveness was associated with further reductions of entropy in sensory (primary and higher sensory plus orbital prefrontal cortices) but not high-order cognitive (dorsal and medial prefrontal, cingulate, parietotemporal cortices and hippocampal areas) systems. Upon recovery of responsiveness, entropy was restored in the sensory but not in high-order cognitive systems. These findings provide novel evidence for a reduction of information content of the brain as a potential systems-level mechanism of reduced consciousness during propofol anesthesia. The differential changes of entropy in the sensory and high-order cognitive systems associated with losing and regaining overt responsiveness are consistent with the notion of "disconnected consciousness", in which a complete sensory-motor disconnection from the environment occurs with preserved internal mentation.

Hearables: Automatic Overnight Sleep Monitoring With Standardized In-Ear EEG Sensor

OBJECTIVE

Advances in sensor miniaturization and computational power have served as enabling technologies for monitoring human physiological conditions in real-world scenarios. Sleep disruption may impact neural function, and can be a symptom of both physical and mental disorders. This study proposes wearable in-ear electroencephalography (ear-EEG) for overnight sleep monitoring as a 24/7 continuous and unobtrusive technology for sleep quality assessment in the community.

METHODS

A total of 22 healthy participants took part in overnight sleep monitoring with simultaneous ear-EEG and conventional full polysomnography recordings. The ear-EEG data were analyzed in the both structural complexity and spectral domains. The extracted features were used for automatic sleep stage prediction through supervized machine learning, whereby the PSG data were manually scored by a sleep clinician.

RESULTS

The agreement between automatic sleep stage prediction based on ear-EEG from a single in-ear sensor and the hypnogram based on the full PSG was 74.1% in the accuracy over five sleep stage classification. This is supported by a substantial agreement in the kappa metric (0.61).

CONCLUSION

The in-ear sensor is feasible for monitoring overnight sleep outside the sleep laboratory and also mitigates technical difficulties associated with PSG. It, therefore, represents a 24/7 continuously wearable alternative to conventional cumbersome and expensive sleep monitoring.

SIGNIFICANCE

The "standardized" one-size-fits-all viscoelastic in-ear sensor is a next generation solution to monitor sleep-this technology promises to be a viable method for readily wearable sleep monitoring in the community, a key to affordable healthcare and future eHealth.

On the calculation of sample entropy using continuous and discrete human gait data

Sample entropy (SE) has relative consistency using biologically-derived, discrete data >500 data points. For certain populations, collecting this quantity is not feasible and continuous data has been used. The effect of using continuous versus discrete data on SE is unknown, nor are the relative effects of sampling rate and input parameters m (comparison vector length) and r (tolerance). Eleven subjects walked for 10-minutes and continuous joint angles (480Hz) were calculated for each lower-extremity joint. Data were downsampled (240, 120, 60Hz) and discrete range-of-motion was calculated. SE was quantified for angles and range-of-motion at all sampling rates and multiple combinations of parameters. A differential relationship between joints was observed between range-of-motion and joint angles. Range-of-motion SE showed no difference; whereas, joint angle SE significantly decreased from ankle to knee to hip. To confirm findings from biological data, continuous signals with manipulations to frequency, amplitude, and both were generated and underwent similar analysis to the biological data. In general, changes to m, r, and sampling rate had a greater effect on continuous compared to discrete data. Discrete data was robust to sampling rate and m. It is recommended that different data types not be compared and discrete data be used for SE.

Beatquency domain and machine learning improve prediction of cardiovascular death after acute coronary syndrome

Frequency domain measures of heart rate variability (HRV) are associated with adverse events after a myocardial infarction. However, patterns in the traditional frequency domain (measured in Hz, or cycles per second) may capture different cardiac phenomena at different heart rates. An alternative is to consider frequency with respect to heartbeats, or beatquency. We compared the use of frequency and beatquency domains to predict patient risk after an acute coronary syndrome. We then determined whether machine learning could further improve the predictive performance. We first evaluated the use of pre-defined frequency and beatquency bands in a clinical trial dataset (N = 2302) for the HRV risk measure LF/HF (the ratio of low frequency to high frequency power). Relative to frequency, beatquency improved the ability of LF/HF to predict cardiovascular death within one year (Area Under the Curve, or AUC, of 0.730 vs. 0.704, p < 0.001). Next, we used machine learning to learn frequency and beatquency bands with optimal predictive power, which further improved the AUC for beatquency to 0.753 (p < 0.001), but not for frequency. Results in additional validation datasets (N = 2255 and N = 765) were similar. Our results suggest that beatquency and machine learning provide valuable tools in physiological studies of HRV.

Characterizing Awake and Anesthetized States Using a Dimensionality Reduction Method

Distinguishing between awake and anesthetized states is one of the important problems in surgery. Vital signals contain valuable information that can be used in prediction of different levels of anesthesia. Some monitors based on electroencephalogram (EEG) such as the Bispectral (BIS) index have been proposed in recent years. This study proposes a new method for characterizing between awake and anesthetized states. We validated our method by obtaining data from 25 patients during the cardiac surgery that requires cardiopulmonary bypass. At first, some linear and non-linear features are extracted from EEG signals. Then a method called "LLE"(Locally Linear Embedding) is used to map high-dimensional features in a three-dimensional output space. Finally, low dimensional data are used as an input to a quadratic discriminant analyzer (QDA). The experimental results indicate that an overall accuracy of 88.4 % can be obtained using this method for classifying the EEG signal into conscious and unconscious states for all patients. Considering the reliability of this method, we can develop a new EEG monitoring system that could assist the anesthesiologists to estimate the depth of anesthesia accurately.

Validation of behavioural indicators used to assess unconsciousness in sheep

The validity of behavioural indicators to assess unconsciousness under different slaughter conditions is under (inter)national debate. The aim of this study was to validate eyelid-, withdrawal-, threat reflex and rhythmic breathing as indicators to assess unconsciousness in sheep. Sheep were monitored during repeated propofol anaesthesia (n=12) and during non-stunned slaughter (n=22). Changes in the EEG and behavioural indices of consciousness/unconsciousness were assessed and compared in sheep. Threat reflex and rhythmic breathing correlated with EEG activity during propofol anaesthesia whilst absence of non-rhythmic breathing or threat reflex indicated unconsciousness. None of the behavioural indicators correlated with EEG activity during non-stunned slaughter. Absence of regular breathing and eyelid reflex was observed 00:27±00:12 min and 00:59±00:17 min (mean±SD) respectively after animals were considered unconscious, indicating that absence of regular breathing and eyelid reflex are distinctly conservative indicators of unconsciousness during non-stunned slaughter in sheep.

Sample entropy analysis of EEG signals via artificial neural networks to model patients' consciousness level based on anesthesiologists experience

Electroencephalogram (EEG) signals, as it can express the human brain's activities and reflect awareness, have been widely used in many research and medical equipment to build a noninvasive monitoring index to the depth of anesthesia (DOA). Bispectral (BIS) index monitor is one of the famous and important indicators for anesthesiologists primarily using EEG signals when assessing the DOA. In this study, an attempt is made to build a new indicator using EEG signals to provide a more valuable reference to the DOA for clinical researchers. The EEG signals are collected from patients under anesthetic surgery which are filtered using multivariate empirical mode decomposition (MEMD) method and analyzed using sample entropy (SampEn) analysis. The calculated signals from SampEn are utilized to train an artificial neural network (ANN) model through using expert assessment of consciousness level (EACL) which is assessed by experienced anesthesiologists as the target to train, validate, and test the ANN. The results that are achieved using the proposed system are compared to BIS index. The proposed system results show that it is not only having similar characteristic to BIS index but also more close to experienced anesthesiologists which illustrates the consciousness level and reflects the DOA successfully.

Monitoring depth of anesthesia using combination of EEG measure and hemodynamic variables

Monitoring depth of anesthesia (DOA) via vital signs is a major ongoing challenge for anesthetists. A number of electroencephalogram (EEG)-based monitors such as the Bispectral (BIS) index have been proposed. However, anesthesia is related to central and autonomic nervous system functions whereas the EEG signal originates only from the central nervous system. This paper proposes an automated DOA detection system which consists of three steps. Initially, we introduce multiscale modified permutation entropy index which is robust in the characterization of the burst suppression pattern and combine multiscale information. This index quantifies the amount of complexity in EEG data and is computationally efficient, conceptually simple and artifact resistant. Then, autonomic nervous system activity is quantified with heart rate and mean arterial pressure which are easily acquired using routine monitoring machine. Finally, the extracted features are used as input to a linear discriminate analyzer (LDA). The method is validated with data obtained from 25 patients during the cardiac surgery requiring cardiopulmonary bypass. The experimental results indicate that an overall accuracy of 89.4 % can be obtained using combination of EEG measure and hemodynamic variables, together with LDA to classify the vital sign into awake, light, surgical and deep anesthetised states. The results demonstrate that the proposed method can estimate DOA more effectively than the commercial BIS index with a stronger artifact-resistance.

Time-varying spectral analysis revealing differential effects of sevoflurane anaesthesia: non-rhythmic-to-rhythmic ratio

BACKGROUND

Heart rate variability (HRV) may reflect various physiological dynamics. In particular, variation of R-R peak interval (RRI) of electrocardiography appears regularly oscillatory in deeper levels of anaesthesia and less regular in lighter levels of anaesthesia. We proposed a new index, non-rhythmic-to-rhythmic ratio (NRR), to quantify this feature and investigated its potential to estimate depth of anaesthesia.

METHODS

Thirty-one female patients were enrolled in this prospective study. The oscillatory pattern transition of RRI was visualised by the time-varying power spectrum and quantified by NRR. The prediction of anaesthetic events, including skin incision, first reaction of motor movement during emergence period, loss of consciousness (LOC) and return of consciousness (ROC) by NRR were evaluated by serial prediction probability (PK ) analysis; the ability to predict the decrease of effect-site sevoflurane concentration was also evaluated. The results were compared with Bispectral Index (BIS).

RESULTS

NRR well-predicted first reaction (PK  > 0.90) 30 s ahead, earlier than BIS and significantly better than HRV indices. NRR well-correlated with sevoflurane concentration, although its correlation was inferior to BIS, while HRV indices had no such correlation. BIS indicated LOC and ROC best.

CONCLUSIONS

Our findings suggest that NRR provides complementary information to BIS regarding the differential effects of anaesthetics on the brain, especially the subcortical motor activity.

Conscious wireless electroretinogram and visual evoked potentials in rats

The electroretinogram (ERG, retina) and visual evoked potential (VEP, brain) are widely used in vivo tools assaying the integrity of the visual pathway. Current recordings in preclinical models are conducted under anesthesia, which alters neural physiology and contaminates responses. We describe a conscious wireless ERG and VEP recording platform in rats. Using a novel surgical technique to chronically implant electrodes subconjunctivally on the eye and epidurally over the visual cortex, we are able to record stable and repeatable conscious ERG and VEP signals over at least 1 month. We show that the use of anaesthetics, necessary for conventional ERG and VEP measurements, alters electrophysiology recordings. Conscious visual electrophysiology improves the viability of longitudinal studies by eliminating complications associated with repeated anaesthesia. It will also enable uncontaminated assessment of drug effects, allowing the eye to be used as an effective biomarker of the central nervous system.

[Usefulness of Bispectral Index (BIS) monitoring for early detection of cerebral hypoperfusions]

OBJECTIVES

The goal of the study was to assess whether clinically significant cerebral hypoperfusion in awake patients would be associated with some alterations in the values of the bispectral index (BIS) monitoring.

STUDY DESIGN

Observational study.

POPULATION AND METHODS

We monitored the BIS during endovascular carotid artery occlusion testing in awake patients.

RESULTS

Twenty-eight patients were included. Twenty-one adequately tolerated the procedure. Their BIS value remained stable throughout the procedure. Four patients had poor angiographic tolerance, but no clinical symptoms. Their BIS value slightly decreased during the test (minimal BIS: 83 [79-87]). Three patients had poor clinical and angiographic tolerance of the occlusion. They all experienced an immediate and dramatic decrease in their BIS value (minimal BIS: ipsilateral to clamping: 50 [45-60]; contralateral to clamping: 48 [45-52]). In all patients, the clinical symptoms and the BIS normalized after deflating the occlusion balloon.

CONCLUSION

In awake patients, the observed values of the BIS monitoring seem to be associated with clinically relevant cerebral hypoperfusion.

The Effects of Bispectral Index and Neuromuscular Blockade Monitoring on the Depth of Anaesthesia and Recovery in Cardiac Patients Under Desflurane Anaesthesia

OBJECTIVE

In this study, we aimed to investigate the effects of bispectral index (BIS) and neuromuscular blockade monitoring on the depth of anaesthesia and recovery in cardiac patients, scheduled to undergo open cholecystectomy operation with desflurane anaesthesia.

METHODS

After the approval of the Ethics Committee and consent from the patients, patients were randomly divided into two groups. All patients received standard induction drugs, and 4-6% desflurane was used for maintenance of anaesthesia. In Group I, the anaesthesiologist was blind to BIS, and end-tidal volatile agent concentration (ETVAC) of desflurane was titrated according to the patients' hemodynamic changes. In Group II, ETVAC of desflurane was titrated to maintain BIS at 50-60. The hemodynamic data, BIS values, end-tidal volatile agent concentration (ETVAC) and train of four (TOF) values were recorded at pre-induction, post-induction, post-intubation, 1st and 5th minutes after surgical incision and then every 15 min. At the end of the operation, extubation time and the time to reach an Aldrete recovery score ≥9 were recorded in each group. Additionally, neuromuscular agent and narcotic agent doses were recorded.

RESULTS

The BIS values were lower for Group I in all times, except pre- and post-induction times (p<0.05). ETVAC values of all times were lower for Group II (p<0.05).

CONCLUSION

The requirement of volatile agent, which was given according to BIS monitoring, was lower than in the standard technique, but it is considered not to affect the early extubation, recovery and neuromuscular agent requirement dependent on TOF monitoring.

Approximate entropy of network parameters

We study the notion of approximate entropy within the framework of network theory. Approximate entropy is an uncertainty measure originally proposed in the context of dynamical systems and time series. We first define a purely structural entropy obtained by computing the approximate entropy of the so-called slide sequence. This is a surrogate of the degree sequence and it is suggested by the frequency partition of a graph. We examine this quantity for standard scale-free and Erdös-Rényi networks. By using classical results of Pincus, we show that our entropy measure often converges with network size to a certain binary Shannon entropy. As a second step, with specific attention to networks generated by dynamical processes, we investigate approximate entropy of horizontal visibility graphs. Visibility graphs allow us to naturally associate with a network the notion of temporal correlations, therefore providing the measure a dynamical garment. We show that approximate entropy distinguishes visibility graphs generated by processes with different complexity. The result probes to a greater extent these networks for the study of dynamical systems. Applications to certain biological data arising in cancer genomics are finally considered in the light of both approaches.

Using the Hilbert-Huang transform to measure the electroencephalographic effect of propofol

Monitoring the effect of anesthetic drugs on the central nervous system is a major ongoing challenge in anesthesia research. A number of electroencephalogram (EEG)-based monitors of the anesthetic drug effect such as the bispectral (BIS) index have been proposed to analyze the EEG signal during anesthesia. However, the BIS index has received some criticism. This paper offers a method based on the Hilbert-Huang transformation to calculate an index, called the Hilbert-Huang weighted regional frequency (HHWRF), to quantify the effect of propofol on brain activity. The HHWRF and BIS indices are applied to EEG signals collected from nine patients during a controlled propofol induction and emergence scheme. The results show that both the HHWRF and BIS track the gross changes in the EEG with increasing and decreasing anesthetic drug effect (the prediction probability P(k) of 0.85 and 0.83 for HHWRF and BIS, respectively). Our new index can reflect the transition from unconsciousness to consciousness faster than the BIS, as indicated from the pharmacokinetic and pharmacodynamic modeled parameters and also from the analysis around the point of reawakening. This method could be used to design a new EEG monitoring system to estimate the propofol anesthetic drug effect.

A spectral analysis of rotator cuff musculature electromyographic activity: surface and indwelling

Electromyography (EMG) of the shoulder girdle is commonly performed; however, EMG spectral properties of shoulder muscles have not been clearly defined. The purpose of this study was to determine the maximum power frequency, Nyquist rate, and minimum sampling rate for indwelling and surface EMG of the normal shoulder girdle musculature. EMG signals were recorded using indwelling electrodes for the rotator cuff muscles and surface electrodes for ten additional shoulder muscles in ten healthy volunteers. A fast Fourier transform was performed on the raw EMG signal collected during maximal isometric contractions to derive the power spectral density. The 95% power frequency was calculated during the ramp and plateau subphase of each contraction. Data were analyzed with analysis of variance (ANOVA) and paired t tests. Indwelling EMG signals had more than twice the frequency content of surface EMG signals (p < .001). Mean 95% power frequencies ranged from 495 to 560 Hz for indwelling electrodes and from 152 to 260 Hz for surface electrodes. Significant differences in the mean 95% power frequencies existed among muscles monitored with surface electrodes (p = .002), but not among muscles monitored with indwelling electrodes (p = .961). No significant differences in the 95% power frequencies existed among contraction subphases for any of the muscle-electrode combinations. Maximum Nyquist rate was 893 Hz for surface electrodes and 1,764 Hz for indwelling electrodes. Our results suggest that when recording EMG of shoulder muscles, the minimum sampling frequency is 1,340 Hz for surface electrodes and 2,650 Hz for indwelling electrodes. The minimum sampling recommendations are higher than the 1,000 Hz reported in many studies involving EMG of the shoulder.

Spectral features of electroencephalogram in characterizing various brain states under anesthesia

The administration of the anesthetic agents is known to alter the electroencephalogram (EEG) signal significantly with the brain being their primary target. In this study, we analyzed the EEG recorded from six ASA I/II patients undergoing a 1-2 hour surgery. The EEG was collected before and during induction, maintenance and recovery of anesthesia using the 10/20 lead-system. A combination of fentanyl and propofol (± rocuronium) was used for induction and a Sevoflurane in air/O(2) mixture was administered through an endotracheal tube to achieve the steady minimum alveolar concentration (MAC). This study showed that 0 to 4 Hz signal power was most sensitive to the changes associated with induction of anesthesia whereas the 4 to 12 Hz power was important in classifying states during maintenance of anesthesia. Anesthesia also promoted heightened phase coherence in 8 to 16 Hz and 16 to 30 Hz ranges during maintenance and induction of anesthesia, respectively. Additionally, strong cross-frequency coupling between 7 to 20 Hz and 10 to 40 Hz was observed during anesthesia suggesting alteration of neural coding.

Analysis of amplitude-integrated EEG in the newborn based on approximate entropy

Amplitude-integrated electroencephalographic (aEEG), a cerebral-function-monitoring method, is widely used in response to the clinical needs for continuous EEG monitoring. In this paper, we present an approach to analyze aEEG in newborns based on approximate entropy (ApEn). Unlike the traditional aEEG signal processing and diagnosing methods, the Box-Cox transformation is substituted for semilogarithmic amplitude compression to keep the continuity of the signal, reduce the excessive compression of chaotic information in high amplitudes, and use ApEn, rather than the amplitudes of the borders, to estimate the degree of chaos in the signal. Experiments with aEEGs of 120 cases (32 normal and 88 abnormal of full-term infants, and 57 cases of preterm infants) were conducted to validate the effectiveness of the proposed method. The results show an aEEG signal analyzed based on the proposed algorithm always belongs to an abnormal case and needs to be examined by physicians if the corresponding indicator is considered abnormal. The novel description of aEEG could be helpful in detecting brain disorders in the newborn as a new clinical target.

Slow EEG pattern predicts reduced intrinsic functional connectivity in the default mode network: an inter-subject analysis

The last two decades have witnessed great progress in mapping neural networks associated with task-induced brain activation. More recently, identification of resting state networks (RSN) paved the way to investigate spontaneous task-unrelated brain activity. The cardinal features characterising RSN are low-frequency fluctuations of blood oxygenation level dependent (BOLD) signals synchronised between spatially distinct, but functionally connected brain areas. Simultaneous EEG/fMRI has been previously deployed to study the neurophysiological signature of RSN by comparing EEG power with BOLD amplitudes. We hypothesised that band-limited EEG power may be directly related to network-specific functional connectivity (FC) of BOLD signal time courses. Hence, we studied the association between individual EEG signature and FC in a core RSN, the so-called default mode network (DMN). Combined EEG/fMRI data of 20 healthy volunteers collected during a 15-minute rest period were analysed. Using an inter-subject analysis design, we demonstrated a network and frequency specific relation between RSN FC and EEG. In a multiple regression model, EEG band-powers explained 70% of DMN FC variance, with significant partial correlations of DMN FC to delta (r=-0.73) and beta (r=0.53) power. The identified EEG pattern has been previously associated with increased alertness. Conversely, an established EEG-derived sedation index (spectral edge frequency SEF95) closely correlated with DMN FC. The study presents an approach that opens a new perspective to EEG/fMRI correlation. Direct evidence was provided for a distinct neurophysiological correlate of DMN FC. This finding further validates the biological relevance of network-specific intrinsic FC and provides an initial neurophysiological basis for interpreting studies of DMN FC alterations.

Autonomic Nervous System and Cardiovascular Disease

Because anesthesia affects the integrity of the autonomic nervous system, anesthesiologists use vital signs to maintain respiratory and circulatory homeostasis. However, patients with genetic predispositions or with autonomic dysfunctions are at risk of severe complications from anesthesia. For these patients, the monitoring of vital signs may not give sufficient warning to avoid complications. The development of methods to measure autonomic tone could be of interest to anesthesiologists because they could warn of changes in autonomic tone before vital signs are affected. New noninvasive methods are being developed to obtain measurements of parasympathetic and sympathetic output allowing for the monitoring of perioperative autonomic tone. These measurements are based on analysis of heart rate and blood pressure variability. In this report, the principals of the analysis of heart rate and blood pressure variability will be explained and the usefulness of these methods to anesthesiologists will be discussed.

Continuous EEG monitoring in severe Guillain-Barré syndrome patients

When patients Guillain-Barré syndrome have complete paralysis clinical measures of sedation cannot be applied. In this situation continuous EEG offers a convenient, effective method of monitoring the depth of sedation, using spectral edge frequency (SEF) to quantify EEG activity. The authors report 3 patients with severe Guillain-Barré syndrome managed with sedation aimed at a SEF95 below 4.0 Hz (delta coma), using a subhairline montage with the DATEX bedside EEG module. Two of the patients were easily managed using this system for an average of 16 days, and both were completely amnestic of this period of time with no serious complication. The third one had still some residual muscle activity and SEF was unreliable in this case, so its use was abandoned. Continuous EEG monitoring using SEF is a useful tool to manage sedation in the most severely paralyzed Guillain-Barré syndrome patients. Incorporation of a low-pass filter would be of benefit to remove any residual muscle activity, which confounds the target level of sedation with this method; SEF has theoretical advantages over the bispectral index in this population. Comparative studies of various continuous EEG monitoring methods in such patients should better define their relative effectiveness.

EEG power spectrum analysis for monitoring depth of anaesthesia during experimental surgery

The first attempts to introduce computerized power spectrum analysis of the electroencephalogram (EEG) as an intraoperative anaesthesia monitoring device started approximately 30 years ago. Since that time, the effects of various anaesthetic agents, sedative and analgesic drugs on the EEG pattern have been addressed in numerous studies in human patients and different animal species. These studies revealed dose-dependent changes in the EEG power spectrum for many intravenous and volatile anaesthetics. Moreover, EEG responses evoked by surgical stimuli during relative light levels of surgical anaesthesia have been classified as 'arousal' and 'paradoxical arousal' reaction, previously referred to as 'desynchronization' and 'synchronization', respectively. Contrasting reports on the correlation between quantitative EEG (QEEG) variables derived from power spectrum analysis (i.e. spectral edge frequency, median frequency) and simultaneously recorded clinical signs such as movement and haemodynamic responses, however, limited the routine use of intraoperative EEG monitoring. In addition, the appearance of EEG burst suppression pattern and isoelectricity at clinically relevant concentrations/doses of newer general anaesthetics (i.e. isoflurane, sevoflurane, propofol) may have weakened the dose-related EEG changes previously reported. Despite these findings, the EEG power spectrum analysis may still provide valuable information during intraoperative monitoring in the individual subject. The information obtained from EEG power spectrum analysis may be further supplemented by newer EEG indices such as bispectral index and approximate entropy or other neurophysiological monitors including auditory evoked potentials or somatosensory evoked potentials.

Effects of averaging data series on the electroencephalographic response to noxious visceral stimulation in isoflurane-anaesthetized dogs

The effects of averaging epochs on electroencephalographic (EEG) responses to visceral stimulation has been determined in seven isoflurane-anaesthetized dogs. Quantitative EEG variables including 80% spectral edge frequency (SEF80), median frequency (MF), relative power in the delta, theta, alpha, beta band and power band ratios (theta/delta, alpha/delta, beta/delta) were recorded over 1min before stimulation and during a 1-minute stimulation period. During off-line analysis EEG variables were derived from either single 2-second EEG epochs or as an average from 5, 10, 15 and 30 consecutive 2-second epochs. Noxious stimulation resulted in significant increases in SEF80, MF, alpha power, beta power, alpha/delta ratio and beta/delta ratio. The number of variables that were significantly affected as well as the strength of changes as indicated by p-values, however, varied with the number of epochs subjected to averaging. The data suggest that stimulation-induced EEG changes may be more pronounced at lower rather than at higher averaging rates.

Monitoring consciousness: the current status of EEG-based depth of anaesthesia monitors

Direct and indirect inhibitory effects of anaesthetic agents on cortical activity are reflected in the electroencephalogram (EEG) as: (i) a shift from low-amplitude, high-frequency EEG, to high-amplitude, low-frequency activity (indicative of cortical depowering) and; (ii) the appearance of spindles and K-complexes (indicative of thalamocortical hyperpolarisation and sensory blockade). Existing EEG monitors use cortical activity as a proxy measure for consciousness. However the state of the cortex at any given moment does not accurately predict the state that it will enter in response to a noxious stimulus, and EEG monitors do not differentiate well between different levels of rousability. Also the literature reveals many instances where the EEG pattern is dissociated from conscious state (e.g. an awake-looking EEG, but an unresponsive patient; or a slow-wave EEG in an awake patient). Fortunately, a slow-wave EEG (even in the presence of a responsive patient) usually indicates profound amnesia for explicit memory.

The effects of surgical levels of sevoflurane and propofol anaesthesia on heart rate variability

BACKGROUND AND OBJECTIVE

The laryngeal mask has become a widely accepted alternative to endotracheal intubation and mask ventilation. The laryngeal tube is a relatively new supraglottic airway device for airway management. We compared the new version of the laryngeal tube with the laryngeal mask.

METHODS

In a randomized design, either a laryngeal tube (n = 66) or a laryngeal mask (n = 66) were inserted. Ease of insertion, oxygenation and ventilation, spirometry data and postoperative airway morbidity were determined.

RESULTS

After successful insertion, it was possible to maintain oxygenation and ventilation in all the patients. Insertion success rates after the first, second and third attempts were 84.8% (n = 56), 12.1% (n = 8) and 3% (n = 2) for the laryngeal tube compared with 56.1% (n = 37), 25.8% (n = 17) and 18.2% (n = 12) for the laryngeal mask (P = 0.001). There was no significant difference in peak airway pressure, and dynamic compliance between the groups (P > 0.05). Blood on the cuff after removal of the device was noted in one patient with the laryngeal tube and in 10 patients with the laryngeal mask. Six patients in the laryngeal mask group complained of hoarseness (P = 0.012).

CONCLUSION

With respect to clinical function, the new version of the laryngeal tube and the laryngeal mask are similar and either device can be used to establish a safe and effective airway in paralysed patients.

Bispectral index monitoring in pediatric anesthesia

PURPOSE OF REVIEW

Anesthetic management guided by bispectral index monitoring has been demonstrated to facilitate earlier recovery in adults. Recent preliminary data also suggest that titration of drugs to achieve a specific bispectral index value may reduce the incidence of intraoperative awareness in high-risk adults. It is unclear, however, if this technology will benefit children as it is based on an algorithm developed from adults. This article reviews the literature on the use of the bispectral index monitor in children.

RECENT FINDINGS

In recent studies, bispectral index scores were shown to correlate well with sedation scores during conscious and deep sedation in pediatric patients, and also with end-tidal concentrations of inhalation agents in children, although bispectral index values may differ with equipotent doses of different agents. Other studies have shown that titrating anesthetic agents to a specific bispectral index value during general anesthesia in the pediatric age group is associated with the use of lower amounts of anesthetic agents and faster recovery from anesthesia in children over 2 years old. However, the utility of bispectral index monitoring in infants, particularly those less than 6 months old, is questionable, as there is little correlation between bispectral index values and other measures of the depth of anesthesia in this subset of patients.

SUMMARY

Bispectral index monitoring may be used to guide anesthetic administration in older children, but its use as a tool for guiding sedation in the younger pediatric patient needs further investigation. It may be necessary to develop a different bispectral index algorithm for children in the younger age groups.

New developments in cerebral monitoring

In past years, cerebral monitoring was mostly focused around global cerebral perfusion and metabolism monitoring, with the use of transcranial Doppler recordings, jugular bulb oximetry and near-infrared spectroscopy. Most of the recently introduced cerebral monitoring modalities, such as brain tissue partial oxygen tension monitoring and cerebral microdialysis, offer new opportunities by providing regional information on the specific brain area in which the probe is inserted. Ideally, these probes should be inserted in that area of the brain that is most vulnerable to ischaemia, but that may be salvageable with appropriate therapy. In this case, the combination of global and regional cerebral monitoring might offer the best information on which to base patient management. Also, the introduction of more clinically useful, functional neuroimaging techniques may be a valuable adjunct to future neurological critical care management.

Monitoring depth of anesthesia

Devices which monitor some aspect of anesthetic drug effects have evolved in the past few years into imperfect, but very useful, clinical tools. With appropriate respect for their limitations these monitors can be used to reduce anesthetic drug utilization and turnover time. The intriguing hypothesis that such monitors will reduce the risk of intraoperative awareness is currently under test.

L'entropie: un moyen d'apprécier le défaut d'analgésie?

OBJECTIVE

Comparison between BIS (Bispectral Index) and state (SE) and response (RE) entropy during laparotomy for inflammatory bowel disease patients (IBD) and evaluation of the variations of RE and SE during nociceptive stimulation.

STUDY DESIGN

Prospective, observational study.

PATIENTS AND METHODS

Fourteen IBD's patients undergoing laparotomy were included. Anaesthesia aimed to maintain BIS between 40 and 60 by isoflurane and nitrous oxide. Analgesia was performed by sufentanil bolus administrated according to an increase of 20% of systolic blood pressure (SBP) and heart rate compared with the baseline values. BIS, RE and SE were measured at each nociceptive stimulation. A variance analysis (Anova) was used to assess BIS, RE and SE variations throughout surgery (p<0.05 as significant). Relationship between BIS, RE and SE was assessed by Pearson correlation (p<0.01 as significant). The ability for SE and RE to predict depth of anaesthesia and intraoperative analgesia was performed by calculating area under the receiver operated curves (AUC).

RESULTS

BIS and entropy parameters had strictly the same evolution during anaesthesia. SBP increased significantly during nociceptive stimulation while no variation of RE was observed. A significant correlation was shown between BIS, RE and SE. The evaluation of anaesthesia depth was good for RE (AUC: 0.932+/-0.26) and SE (AUC: 0.926+/-0.27). There was however no difference between RE and SE to predict analgesic requirement.

CONCLUSION

Because RE includes muscular frequency analysis, it does not allow analgesic requirement evaluation in paralyzed patients.

Nonlinear analysis of discharge patterns in monkey basal ganglia

Spontaneous discharge of basal ganglia neurons is often analyzed with time- or frequency-domain methods. However, it has been shown that sequences of inter-spike interval series are not fully described by such linear procedures. We therefore carried out a characterization of the nonlinear features of spontaneous discharge of neurons in the primate basal ganglia. We studied the spontaneous activity of neurons in the subthalamic nucleus (22 cells), as well as neurons in the external and internal pallidal segments (53 and 39 cells, respectively), recorded with standard extracellular recording methods in two awake Rhesus monkeys. As a measure of the statistical irregularity of neuronal discharge, we compared the approximate entropy of inter-spike interval sequences with that of shuffled representations of the same data. In all three basal ganglia structures, approximately 95% of the original data showed lower approximate entropy values than the shuffled data, suggesting a temporal organization in the original sequence. Fano factor analysis confirmed the presence of a temporal organization of inter-spike interval sequences, and indicated the presence of self-similarity in the great majority of them. In addition, Hurst exponent analysis showed that the inter-spike interval series are persistent. Hurst exponents often differ between short and long scaling ranges. Subsequent principal component analyses allowed us to identify three distinct patterns of the temporal evolution of inter-spike interval sequences in the phase space. These types were found in varying distributions in all three nuclei. Our analyses demonstrate that the discharge of most neurons in the basal ganglia of awake monkeys has nonlinear features that may be important for information coding in the basal ganglia.