Comparative study of Poincaré plot analysis using short electroencephalogram signals during anaesthesia with spectral edge frequency 95 and bispectral index

Chaotic nature of the electroencephalogram during shallow and deep anesthesia: From analysis of the Lyapunov exponent

In conscious states, the electrodynamics of the cortex are reported to work near a critical point or phase transition of chaotic dynamics, known as the edge-of-chaos, representing a boundary between stability and chaos. Transitions away from this boundary disrupt cortical information processing and induce a loss of consciousness. The entropy of the electroencephalogram (EEG) is known to decrease as the level of anesthesia deepens. However, whether the chaotic dynamics of electroencephalographic activity shift from this boundary to the side of stability or the side of chaotic enhancement during anesthesia-induced loss of consciousness remains poorly understood. We investigated the chaotic properties of EEGs at two different depths of clinical anesthesia using the maximum Lyapunov exponent, which is mathematically regarded as a formal measure of chaotic nature, using the Rosenstein algorithm. In 14 adult patients, 12 s of electroencephalographic signals were selected during two depths of clinical anesthesia (sevoflurane concentration 2% as relatively deep anesthesia, sevoflurane concentration 0.6% as relatively shallow anesthesia). Lyapunov exponents, correlation dimensions and approximate entropy were calculated from these electroencephalographic signals. As a result, maximum Lyapunov exponent was generally positive during sevoflurane anesthesia, and both maximum Lyapunov exponents and correlation dimensions were significantly greater during deep anesthesia than during shallow anesthesia despite reductions in approximate entropy. The chaotic nature of the EEG might be increased at clinically deeper inhalational anesthesia, despite the decrease in randomness as reflected in the decreased entropy, suggesting a shift to the side of chaotic enhancement under anesthesia.

Comparative analysis of the effect of electromyogram to bispectral index and 95% spectral edge frequency under remimazolam and propofol anesthesia: a prospective, randomized, controlled clinical trial

Background

Bispectral index (BIS), an index used to monitor the depth of anesthesia, can be interfered with by the electromyogram (EMG) signal. The 95% spectral edge frequency (SEF95) also can reflect the sedation depth. Remimazolam in monitored anesthesia care results in higher BIS values than propofol, though in the same sedation level assessed by Modified Observers Assessment of Alertness and Sedation (MOAA/S). Our study aims to illustrate whether EMG is involved in remimazolam causing higher BIS value than propofol preliminarily and to explore the correlations among BIS, EMG, and SEF95 under propofol and remimazolam anesthesia.

Patients and methods

Twenty-eight patients were randomly divided into propofol (P) and remimazolam (RM) groups. Patients in the two groups received alfentanil 10 μg/kg, followed by propofol 2 mg/kg and remimazolam 0.15 mg/kg. Blood pressure (BP), heart rate (HR), and oxygen saturation (SpO2) were routinely monitored. The BIS, EMG, and SEF95 were obtained through BIS VISTATM. The primary outcomes were BIS, EMG, and the correlation between BIS and EMG in both groups. Other outcomes were SEF95, the correlation between BIS and SEF95, and the correlation between EMG and SEF95. And all the statistical and comparative analysis between these signals was conducted with SPSS 26.0 and GraphPad Prism 8.

Results

BIS values, EMG, and SEF95 were significantly higher in the RM group than in the P group (all p < 0.001). There was a strong positive correlation between BIS and EMG in the RM group (r = 0.416). Nevertheless, the BIS in the P group showed a weak negative correlation with EMG (r = -0.219). Both P (r = 0.787) and RM group (r = 0.559) had a reasonably significant correlation coefficient between BIS and SEF95. SEF95 almost did not correlate with EMG in the RM group (r = 0.101).

Conclusion

Bispectral index can be interfered with high EMG intensity under remimazolam anesthesia. However, EMG can hardly affect the accuracy of BIS under propofol anesthesia due to low EMG intensity and a weak negative correlation between EMG and BIS. Moreover, SEF95 may have a great application prospect in predicting the sedation condition of remimazolam.

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.

The Investigation of Behavior Change in EEG Signals During Induction of Anesthesia

Anesthesiology aims to make anesthesia safer and increase the precision of prognoses. Correct assessment of the anesthesia depth is crucial to its safety. At present, intraoperative electroencephalogram (EEG) monitoring is the primary mode of anesthesia depth monitoring and judgment. However, most clinical anesthesiologists rely on commercial anesthesia depth monitors to judge anesthesia depth, such as bispectral index (BIS) and patient state index (PSI). This may lack an understanding of associated changes in brain wave quantization. Therefore, this study conducts quantitative analyses of EEG signals during anesthesia induction. EEG signals are processed within specific time windows and extracted brainpower density spectrum arrays with different frequency bands, brain electrical signal spectra, source frequencies and other key indicators. Analysis and comparison of these indicators clarifies patterns of variation in EEG signals during early anesthesia induction. The spectral edge frequencies (SEFs) of EEG signals within different time windows can be modeled accurately, from which the specific time points of EEG signal changes are derived. Furthermore, the relationship between patient age and the effect of anesthetic drugs is preliminarily investigated by analyzing the SEF variations of different age groups. This study quantifies changes in the EEG signals of patients at the initial stage of anesthesia induction and drug-related effects are observed, which opens a way for further exploration of EEG changes in patients under general anesthesia.

Poincaré Plot Area of Gamma-Band EEG as a Measure of Emergence From Inhalational General Anesthesia

The Poincaré plot obtained from electroencephalography (EEG) has been used to evaluate the depth of anesthesia. A standalone EEG Analyzer application was developed; raw EEG signals obtained from a bispectral index (BIS) monitor were analyzed using an on-line monitoring system. Correlations between Poincaré plot parameters and other measurements associated with anesthesia depth were evaluated during emergence from inhalational general anesthesia. Of the participants, 20 were adults anesthetized with sevoflurane (adult_{_SEV}), 20 were adults anesthetized with desflurane (adult_{_DES}), and 20 were pediatric patients anesthetized with sevoflurane (ped_{_SEV}). EEG signals were preprocessed through six bandpass digital filters (f0: 0.5–47 Hz, f1: 0.5–8 Hz, f2: 8–13 Hz, f3: 13–20 Hz, f4: 20–30 Hz, and f5: 30–47 Hz). The Poincaré plot-area ratio (PP_AR = PP_{A_fx}/PP_{A_f0}, fx = f1∼f5) was analyzed at five frequency ranges. Regardless of the inhalational anesthetic used, there were strong linear correlations between the logarithm of PP_AR at f5 and BIS (R² = 0.67, 0.79, and 0.71, in the adult_{_SEV}, adult_{_DES}, and ped_{_SEV} groups, respectively). As an additional observation, a part of EMG activity at the gamma range of 30–47 Hz probably influenced the calculations of BIS and PP_{AR_f5} with a non-negligible level. The logarithm of PP_AR in the gamma band was most sensitive to state changes during the emergence process and could provide a new non-proprietary parameter that correlates with changes in BIS during measurement of anesthesia depth.

Changes in electroencephalographic power and bicoherence spectra according to depth of dexmedetomidine sedation in patients undergoing spinal anesthesia

Background: Assessment the depth of dexmedetomidine sedation using electroencephalographic (EEG) features can improve the quality of procedural sedation. Previous volunteer studies of dexmedetomidine-induced EEG changes need to be validated, and changes in bicoherence spectra during dexmedetomidine sedation has not been revealed yet. We aimed to investigate the dexmedetomidine-induced EEG change using power spectral and bicoherence analyses in the clinical setting. Patients and Methods: Thirty-six patients undergoing orthopedic surgery under spinal anesthesia were enrolled in this study. Dexmedetomidine sedation was conducted by the stepwise increase in target effect site concentration (Ce) while assessing sedation levels. Bispectral index (BIS) and frontal electroencephalography were recorded continuously, and the performance of BIS and changes in power and bicoherence spectra were analyzed with the data from the F3 electrode. Results: The prediction probability values for detecting different sedation levels were 0.847, 0.841, and 0.844 in BIS, 95% spectral edge frequency, and dexmedetomidine Ce, respectively. As the depth of sedation increased, δ power increased, but high β and γ power decreased significantly (P <0.001). α and spindle power increased significantly under light and moderate sedation (P <0.001 in light vs baseline and deep sedation; P = 0.002 and P <0.001 in moderate sedation vs baseline and deep sedation, respectively). The bicoherence peaks of the δ and α-spindle regions along the diagonal line of the bicoherence matrix emerged during moderate and deep sedation. Peak bicoherence in the δ area showed sedation-dependent increases (29.93%±7.38%, 36.72%±9.70%, 44.88%±12.90%; light, moderate, and deep sedation; P = 0.008 and P <0.001 in light sedation vs moderate and deep sedation, respectively; P = 0.007 in moderate sedation vs deep sedation), whereas peak bicoherence in the α-spindle area did not change (22.92%±4.90%, 24.72%±4.96%, and 26.96%±8.42%, respectively; P=0.053). Conclusions: The increase of δ power and the decrease of high-frequency power were associated with the gradual deepening of dexmedetomidine sedation. The δ bicoherence peak increased with increasing sedation level and can serve as an indicator reflecting dexmedetomidine sedation levels.

Consciousness Detection in a Complete Locked-in Syndrome Patient through Multiscale Approach Analysis

Completely locked-in state (CLIS) patients are unable to speak and have lost all muscle movement. From the external view, the internal brain activity of such patients cannot be easily perceived, but CLIS patients are considered to still be conscious and cognitively active. Detecting the current state of consciousness of CLIS patients is non-trivial, and it is difficult to ascertain whether CLIS patients are conscious or not. Thus, it is important to find alternative ways to re-establish communication with these patients during periods of awareness, and one such alternative is through a brain-computer interface (BCI). In this study, multiscale-based methods (multiscale sample entropy, multiscale permutation entropy and multiscale Poincaré plots) were applied to analyze electrocorticogram signals from a CLIS patient to detect the underlying consciousness level. Results from these different methods converge to a specific period of awareness of the CLIS patient in question, coinciding with the period during which the CLIS patient is recorded to have communicated with an experimenter. The aim of the investigation is to propose a methodology that could be used to create reliable communication with CLIS patients.

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.

Buprenorphine Depresses Respiratory Variability in Obese Mice with Altered Leptin Signaling

BACKGROUND

Opiate-induced respiratory depression is sexually dimorphic and associated with increased risk among the obese. The mechanisms underlying these associations are unknown. The present study evaluated the two-tailed hypothesis that sex, leptin status, and obesity modulate buprenorphine-induced changes in breathing.

METHODS

Mice (n = 40 male and 40 female) comprising four congenic lines that differ in leptin signaling and body weight were injected with saline and buprenorphine (0.3 mg/kg). Whole-body plethysmography was used to quantify the effects on minute ventilation. The data were evaluated using three-way analysis of variance, regression, and Poincaré analyses.

RESULTS

Relative to B6 mice with normal leptin, buprenorphine decreased minute ventilation in mice with diet-induced obesity (37.2%; P < 0.0001), ob/ob mice that lack leptin (62.6%; P < 0.0001), and db/db mice with dysfunctional leptin receptors (65.9%; P < 0.0001). Poincaré analyses showed that buprenorphine caused a significant (P < 0.0001) collapse in minute ventilation variability that was greatest in mice with leptin dysfunction. There was no significant effect of sex or body weight on minute ventilation.

CONCLUSIONS

The results support the interpretation that leptin status but not body weight or sex contributed to the buprenorphine-induced decrease in minute ventilation. Poincaré plots illustrate that the buprenorphine-induced decrease in minute ventilation variability was greatest in mice with impaired leptin signaling. This is relevant because normal respiratory variability is essential for martialing a compensatory response to ventilatory challenges imposed by disease, obesity, and surgical stress.

Assessment of sedation-analgesia by means of Poincaré analysis of the electroencephalogram

Monitoring the levels of sedation-analgesia may be helpful for managing patient stress on minimally invasive medical procedures. Monitors based on EEG analysis and designed to assess general anesthesia cannot distinguish reliably between a light and deep sedation. In this work, the Poincaré plot is used as a nonlinear technique applied to EEG signals in order to characterize the levels of sedation-analgesia, according to observed categorical responses that were evaluated by means of Ramsay Sedation Scale (RSS). To study the effect of high frequencies due to EMG activity, three different frequency ranges (FR1=0.5-110 Hz, FR2=0.5-30 Hz and FR3=30-110 Hz) were considered. Indexes from power spectral analysis and plasma concentration of propofol and remifentanil were also compared with the bispectral index BIS. An adaptive Neurofuzzy Inference System was applied to model the interaction of the best indexes with respect to RSS score for each analysis, and leave-one-out cross validation method was used. The ability of the indexes to describe the level of sedation-analgesia, according with the RSS score, was evaluated using the prediction probability (Pk). The results showed that the ratio SD1/SD2FR3 contains useful information about the sedation level, and SD1FR2 and SD2FR2 had the best performance classifying response to noxious stimuli. Models including parameters from Poincaré plot emerge as a good estimator of sedation-analgesia levels.