Permutation auto-mutual information of electroencephalogram in anesthesia

Changes in information integration and brain networks during propofol-, dexmedetomidine-, and ketamine-induced unresponsiveness

BACKGROUND

Information integration and network science are important theories for quantifying consciousness. However, whether these theories propose drug- or conscious state-related changes in EEG during anaesthesia-induced unresponsiveness remains unknown.

METHODS

A total of 72 participants were randomised to receive i.v. infusion of propofol, dexmedetomidine, or ketamine at a constant infusion rate until loss of responsiveness. High-density EEG was recorded during the consciousness transition from the eye-closed baseline to the unresponsiveness state and then to the recovery of the responsiveness state. Permutation cross mutual information (PCMI) and PCMI-based brain networks in broadband (0.1-45 Hz) and sub-band frequencies were used to analyse drug- and state-related EEG signature changes.

RESULTS

PCMI and brain networks exhibited state-related changes in certain brain regions and frequency bands. The within-area PCMI of the frontal, parietal, and occipital regions, and the between-area PCMI of the parietal-occipital region (median [inter-quartile ranges]), baseline vs unresponsive were as follows: 0.54 (0.46-0.58) vs 0.46 (0.40-0.50), 0.58 (0.52-0.60) vs 0.48 (0.44-0.53), 0.54 (0.49-0.59) vs 0.47 (0.42-0.52) decreased during anaesthesia for three drugs (P<0.05). Alpha PCMI in the frontal region, and gamma PCMI in the posterior area significantly decreased in the unresponsive state (P<0.05). The frontal, parietal, and occipital nodal clustering coefficients and parietal nodal efficiency decreased in the unresponsive state (P<0.05). The increased normalised path length in delta, theta, and gamma bands indicated impaired global integration (P<0.05).

CONCLUSIONS

The three anaesthetics caused changes in information integration patterns and network functions. Thus, it is possible to build a quantifying framework for anaesthesia-induced conscious state changes on the EEG scale using PCMI and network science.

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.

The Dream Catcher experiment: blinded analyses failed to detect markers of dreaming consciousness in EEG spectral power

The Dream Catcher test defines the criteria for a genuine discovery of the neural constituents of phenomenal consciousness. Passing the test implies that some patterns of purely brain-based data directly correspond to the subjective features of phenomenal experience, which would help to bridge the explanatory gap between consciousness and brain. Here, we conducted the Dream Catcher test for the first time in a step-wise and simplified form, capturing its core idea. The Dream Catcher experiment involved a Data Team, which measured participants' brain activity during sleep and collected dream reports, and a blinded Analysis Team, which was challenged to predict, based solely on brain measurements, whether or not a participant had a dream experience. Using a serial-awakening paradigm, the Data Team prepared 54 1-min polysomnograms of non-rapid eye movement sleep-27 of dreamful sleep and 27 of dreamless sleep (three of each condition from each of the nine participants)-redacting from them all associated participant and dream information. The Analysis Team attempted to classify each recording as either dreamless or dreamful using an unsupervised machine learning classifier, based on hypothesis-driven, extracted features of electroencephalography (EEG) spectral power and electrode location. The procedure was repeated over five iterations with a gradual removal of blindness. At no level of blindness did the Analysis Team perform significantly better than chance, suggesting that EEG spectral power could not be utilized to detect signatures specific to phenomenal consciousness in these data. This study marks the first step towards realizing the Dream Catcher test in practice.

On the automatic parameter selection for permutation entropy

Permutation Entropy (PE) is a cost effective tool for summarizing the complexity of a time series. It has been used in many applications including damage detection, disease forecasting, detection of dynamical changes, and financial volatility analysis. However, to successfully use PE, an accurate selection of two parameters is needed: the permutation dimension n and embedding delay τ. These parameters are often suggested by experts based on a heuristic or by a trial and error approach. Both of these methods can be time-consuming and lead to inaccurate results. In this work, we investigate multiple schemes for automatically selecting these parameters with only the corresponding time series as the input. Specifically, we develop a frequency-domain approach based on the least median of squares and the Fourier spectrum, as well as extend two existing methods: Permutation Auto-Mutual Information Function and Multi-scale Permutation Entropy (MPE) for determining τ. We then compare our methods as well as current methods in the literature for obtaining both τ and n against expert-suggested values in published works. We show that the success of any method in automatically generating the correct PE parameters depends on the category of the studied system. Specifically, for the delay parameter τ, we show that our frequency approach provides accurate suggestions for periodic systems, nonlinear difference equations, and electrocardiogram/electroencephalogram data, while the mutual information function computed using adaptive partitions provides the most accurate results for chaotic differential equations. For the permutation dimension n, both False Nearest Neighbors and MPE provide accurate values for n for most of the systems with a value of n=5 being suitable in most cases.

Use of Multiple EEG Features and Artificial Neural Network to Monitor the Depth of Anesthesia

The electroencephalogram (EEG) can reflect brain activity and contains abundant information of different anesthetic states of the brain. It has been widely used for monitoring depth of anesthesia (DoA). In this study, we propose a method that combines multiple EEG-based features with artificial neural network (ANN) to assess the DoA. Multiple EEG-based features can express the states of the brain more comprehensively during anesthesia. First, four parameters including permutation entropy, 95% spectral edge frequency, BetaRatio and SynchFastSlow were extracted from the EEG signal. Then, the four parameters were set as the inputs to an ANN which used bispectral index (BIS) as the reference output. 16 patient datasets during propofol anesthesia were used to evaluate this method. The results indicated that the accuracies of detecting each state were 86.4% (awake), 73.6% (light anesthesia), 84.4% (general anesthesia), and 14% (deep anesthesia). The correlation coefficient between BIS and the index of this method was 0.892 (p<0.001). The results showed that the proposed method could well distinguish between awake and other anesthesia states. This method is promising and feasible for a monitoring system to assess the DoA.

A new EEG synchronization strength analysis method: S-estimator based normalized weighted-permutation mutual information

Synchronization is an important mechanism for understanding information processing in normal or abnormal brains. In this paper, we propose a new method called normalized weighted-permutation mutual information (NWPMI) for double variable signal synchronization analysis and combine NWPMI with S-estimator measure to generate a new method named S-estimator based normalized weighted-permutation mutual information (SNWPMI) for analyzing multi-channel electroencephalographic (EEG) synchronization strength. The performances including the effects of time delay, embedding dimension, coupling coefficients, signal to noise ratios (SNRs) and data length of the NWPMI are evaluated by using Coupled Henon mapping model. The results show that the NWPMI is superior in describing the synchronization compared with the normalized permutation mutual information (NPMI). Furthermore, the proposed SNWPMI method is applied to analyze scalp EEG data from 26 amnestic mild cognitive impairment (aMCI) subjects and 20 age-matched controls with normal cognitive function, who both suffer from type 2 diabetes mellitus (T2DM). The proposed methods NWPMI and SNWPMI are suggested to be an effective index to estimate the synchronization strength.

Transcranial Magnetic Stimulation Combined with EEG Reveals Covert States of Elevated Excitability in the Human Epileptic Brain

BACKGROUND

Transcranial magnetic stimulation combined with electroencephalogram (TMS-EEG) can be used to explore the dynamical state of neuronal networks. In patients with epilepsy, TMS can induce epileptiform discharges (EDs) with a stochastic occurrence despite constant stimulation parameters. This observation raises the possibility that the pre-stimulation period contains multiple covert states of brain excitability some of which are associated with the generation of EDs.

OBJECTIVE

To investigate whether the interictal period contains "high excitability" states that upon brain stimulation produce EDs and can be differentiated from "low excitability" states producing normal appearing TMS-EEG responses.

METHODS

In a cohort of 25 patients with Genetic Generalized Epilepsies (GGE) we identified two subjects characterized by the intermittent development of TMS-induced EDs. The high-excitability in the pre-stimulation period was assessed using multiple measures of univariate time series analysis. Measures providing optimal discrimination were identified by feature selection techniques. The "high excitability" states emerged in multiple loci (indicating diffuse cortical hyperexcitability) and were clearly differentiated on the basis of 14 measures from "low excitability" states (accuracy = 0.7).

CONCLUSION

In GGE, the interictal period contains multiple, quasi-stable covert states of excitability a class of which is associated with the generation of TMS-induced EDs. The relevance of these findings to theoretical models of ictogenesis is discussed.

Reduction of the dimensionality of the EEG channels during scoliosis correction surgeries using a wavelet decomposition technique

This paper presents a comparison between the electroencephalogram (EEG) channels during scoliosis correction surgeries. Surgeons use many hand tools and electronic devices that directly affect the EEG channels. These noises do not affect the EEG channels uniformly. This research provides a complete system to find the least affected channel by the noise. The presented system consists of five stages: filtering, wavelet decomposing (Level 4), processing the signal bands using four different criteria (mean, energy, entropy and standard deviation), finding the useful channel according to the criteria's value and, finally, generating a combinational signal from Channels 1 and 2. Experimentally, two channels of EEG data were recorded from six patients who underwent scoliosis correction surgeries in the Pusat Perubatan Universiti Kebangsaan Malaysia (PPUKM) (the Medical center of National University of Malaysia). The combinational signal was tested by power spectral density, cross-correlation function and wavelet coherence. The experimental results show that the system-outputted EEG signals are neatly switched without any substantial changes in the consistency of EEG components. This paper provides an efficient procedure for analyzing EEG signals in order to avoid averaging the channels that lead to redistribution of the noise on both channels, reducing the dimensionality of the EEG features and preparing the best EEG stream for the classification and monitoring stage.

Evolution of electroencephalogram signal analysis techniques during anesthesia

Biosignal analysis is one of the most important topics that researchers have tried to develop during the last century to understand numerous human diseases. Electroencephalograms (EEGs) are one of the techniques which provides an electrical representation of biosignals that reflect changes in the activity of the human brain. Monitoring the levels of anesthesia is a very important subject, which has been proposed to avoid both patient awareness caused by inadequate dosage of anesthetic drugs and excessive use of anesthesia during surgery. This article reviews the bases of these techniques and their development within the last decades and provides a synopsis of the relevant methodologies and algorithms that are used to analyze EEG signals. In addition, it aims to present some of the physiological background of the EEG signal, developments in EEG signal processing, and the effective methods used to remove various types of noise. This review will hopefully increase efforts to develop methods that use EEG signals for determining and classifying the depth of anesthesia with a high data rate to produce a flexible and reliable detection device.