EEG signals analysis using multiscale entropy for depth of anesthesia monitoring during surgery through artificial neural networks

In order to build a reliable index to monitor the depth of anesthesia (DOA), many algorithms have been proposed in recent years, one of which is sample entropy (SampEn), a commonly used and important tool to measure the regularity of data series. However, SampEn only estimates the complexity of signals on one time scale. In this study, a new approach is introduced using multiscale entropy (MSE) considering the structure information over different time scales. The entropy values over different time scales calculated through MSE are applied as the input data to train an artificial neural network (ANN) model using bispectral index (BIS) or expert assessment of conscious level (EACL) as the target. To test the performance of the new index's sensitivity to artifacts, we compared the results before and after filtration by multivariate empirical mode decomposition (MEMD). The new approach via ANN is utilized in real EEG signals collected from 26 patients before and after filtering by MEMD, respectively; the results show that is a higher correlation between index from the proposed approach and the gold standard compared with SampEn. Moreover, the proposed approach is more structurally robust to noise and artifacts which indicates that it can be used for monitoring the DOA more accurately.This research was financially supported by the Center for Dynamical Biomarkers and Translational Medicine, National Central University, Taiwan, which is sponsored by Ministry of Science and Technology (Grant no. MOST103-2911-I-008-001). Also, it was supported by National Chung-Shan Institute of Science & Technology in Taiwan (Grant nos. CSIST-095-V301 and CSIST-095-V302) and National Natural Science Foundation of China (Grant no. 51475342)

Intermittent blood pressure prediction via multiscale entropy and ensemble artificial neural networks

This study evaluates the correlation between the intermittent blood pressure (BP) and the photoplethysmography (PPG). This study of a total of twenty-five cases is started by the partitioning of the PPG signal into a 5-minute segment. The segmented PPG is filtered by ensemble empirical mode decomposition (EEMD). The feature extraction method, multiscale entropy (MSE) is utilized for the purified signal to achieve some information. The seventy-five scale of MSE is taken into the input of the artificial neural network (ANN) modeling. The output of this system are the intermittent diastolic and systolic blood pressure. Originally, a thousand models areis created. The best model is chosen for the best single ANN model. In advanced, the ensemble artificial neural network (EANN) model is initiated to observe the testing data. The result, compared to the best single ANN model, shows that the EANN model recognizes better the testing data by producing lower mean absolute error (MAE)

Photoplethysmography temporal marker-based machine learning classifier for anesthesia drug detection

Anesthesia drug overdose hazards and lack of gold standards in anesthesia monitoring lead to an urgent need for accurate anesthesia drug detection. To investigate the PPG waveform features affected by anesthesia drugs and develop a machine-learning classifier with high anesthesia drug sensitivity. This study used 64 anesthesia and non-anesthesia patient data (32 cases each), extracted from Queensland and MIMIC-II databases, respectively. The key waveform features (total area, rising time, width 75%, 50%, and 25%) were extracted from 16,310 signal recordings (5-s duration). Discriminant analysis, support vector machine (SVM), and K-nearest neighbor (KNN) were evaluated by splitting the dataset into halve training (11 patients, 8570 segments) and halve testing dataset (11 patients, 7740 segments). Significant differences exist between PPG waveform features of anesthesia and non-anesthesia groups (p  0.05). The KNN classifier achieved 91.7% (AUC = 0.95) anesthesia detection accuracy with the highest sensitivity (0.88) and specificity (0.90) as compared to other classifiers. Kohen’s kappa also shows almost perfect agreement (0.79) with the KNN classifier. The KNN classifier trained with significant PPG features has the potential to be used as a reliable, non-invasive, and low-cost method for the detection of anesthesia drugs for depth analysis during surgical operations and postoperative monitoring. GRAPHICAL ABSTRACT: [Image: see text

Termite Resistance of a Fast-Growing Pine Wood Treated by In Situ Polymerization of Three Different Precursors

This study aims to compare the resistance against subterranean termites of wood–polymer composites produced by in situ polymerization. The biological tests were carried out by choice and no-choice feeding tests. Poly (furfuryl alcohol), poly(styrene) and poly (methyl methacrylate) were studied here. They were impregnated into a Brazilian fast-growing pine wood using a vacuum:pressure method and then cured under simple heating. These treatments were evaluated using chemical (by infrared spectroscopy) and morphological (by scanning electron microscopy) analyses. The termite attack was evaluated by mass loss determination and photography. In general, all the treatments were effective in protecting the fast-growing pine wood. Results obtained by no-choice tests indicated that the treatment solution with 75% of furfuryl alcohol was less effective than the others, which indicates that both choice and no-choice tests may be important in a comprehensive study on the termites resistance of solid woods.Higher Education—CAPES (Financing Code 001) and National Council for Scientific and Technological Development—CNPq (Financing Code 301758/2019-0)

Robust approach for depth of anaesthesia assessment based on hybrid transform and statistical features

To develop an accurate and efficient depth of anaesthesia (DoA) assessment technique that could help anaesthesiologists to trace the patient’s anaesthetic state during surgery, a new automated DoA approach was proposed. It applied Wavelet-Fourier analysis (WFA) to extract the statistical characteristics from an anaesthetic EEG signal and to designed a new DoA index. In this proposed method, firstly, the wavelet transform was applied to a denoised EEG signal, and a Fast Fourier transform was then applied to the wavelet detail coefficient D3. Ten statistical features were extracted and analysed, and from these, five features were selected for designing a new index for the DoA assessment. Finally, a new DoA (WFADoA) was developed and compared with the most popular bispectral index (BIS) monitor. The results from the testing set showed that there were very high correlations between the WFADoA and the BIS index during the awake, light and deep anaesthetic stages. In the case of poor signal quality, the BIS index and the WFADoA were also tested, and the obtained results demonstrated that the WFADoA could indicate the DoA values, while the BIS failed to show valid outputs for those situations

Impact of environmental changes on the dynamics of temporal networks

Dynamics of complex social systems has often been described in the framework of temporal networks, where links are considered to exist only at the moment of interaction between nodes. Such interaction patterns are not only driven by internal interaction mechanisms, but also affected by environmental changes. To investigate the impact of the environmental changes on the dynamics of temporal networks, we analyze several face-to-face interaction datasets using the multiscale entropy (MSE) method to find that the observed temporal correlations can be categorized according to the environmental similarity of datasets such as classes and break times in schools. By devising and studying a temporal network model considering a periodically changing environment as well as a preferential activation mechanism, we numerically show that our model could successfully reproduce various empirical results by the MSE method in terms of multiscale temporal correlations. Our results demonstrate that the environmental changes can play an important role in shaping the dynamics of temporal networks when the interactions between nodes are influenced by the environment of the systems.Comment: 10 pages, 8 figure

Quasi-Periodicities Detection Using Phase-Rectified Signal Averaging in EEG Signals as a Depth of Anesthesia Monitor

Phase-rectified signal averaging (PRSA) has been known to be a useful method to detect periodicities in non-stationary biological signals. Determination of quasi-periodicities in electroencephalogram (EEG) is a candidate for quantifying the changes of depth of anesthesia (DOA). In this paper, DOA monitoring capacity of periodicities detected using PRSA were quantified by assessing EEG signals collected from 56 patients during surgery. The method is compared to sample entropy (SampEn), detrended fluctuation analysis (DFA) and permutation entropy (PE). The performance of quasi-periodicities defined by acceleration capacity (AC) and deceleration capacity (DC) was tested using the area under the receiver operating characteristic curve (AUC) and Pearson correlation coefficient. During the surgery, a significant difference (p < 0.05) in the quasi-periodicities was observed among three different stages under general anesthesia. There is a larger mean AUC and correlation coefficient of quasi-periodicities compared to SampEn, DFA and PE using expert assessment of conscious level (EACL) and bispectral index (BIS) as the gold standard, respectively. Quasi-periodicities detected using PRSA in EEG signals are powerful monitor of DOA and perform more accurate and robust results compared to SampEn, DFA and PE. The results do provide a valuable reference to researchers in the filed of clinical applications.10.13039/501100003711-Center for Dynamical Biomarkers and Translational Medicine, National Central University, Taiwan, which is sponsored by the Ministry of Science and Technology; 10.13039/501100001809-National Natural Science Foundation of China

EEG artifacts reduction by multivariate empirical mode decomposition and multiscale entropy for monitoring depth of anaesthesia during surgery

Electroencephalography (EEG) has been widely utilized to measure the depth of anaesthesia (DOA) during operation. However, the EEG signals are usually contaminated by artifacts which have a consequence on the measured DOA accuracy. In this study, an effective and useful filtering algorithm based on multivariate empirical mode decomposition and multiscale entropy (MSE) is proposed to measure DOA. Mean entropy of MSE is used as an index to find artifacts-free intrinsic mode functions. The effect of different levels of artifacts on the performances of the proposed filtering is analysed using simulated data. Furthermore, 21 patients' EEG signals are collected and analysed using sample entropy to calculate the complexity for monitoring DOA. The correlation coefficients of entropy and bispectral index (BIS) results show 0.14 ± 0.30 and 0.63 ± 0.09 before and after filtering, respectively. Artificial neural network (ANN) model is used for range mapping in order to correlate the measurements with BIS. The ANN method results show strong correlation coefficient (0.75 ± 0.08). The results in this paper verify that entropy values and BIS have a strong correlation for the purpose of DOA monitoring and the proposed filtering method can effectively filter artifacts from EEG signals. The proposed method performs better than the commonly used wavelet denoising method. This study provides a fully adaptive and automated filter for EEG to measure DOA more accuracy and thus reduce risk related to maintenance of anaesthetic agents.This research was financially supported by the Center for Dynamical Biomarkers and Translational Medicine, National Central University, Taiwan, which is sponsored by Ministry of Science and Technology (Grant Number: NSC102-2911-I-008-001). Also, it was supported by Chung-Shan Institute of Science and Technology in Taiwan (Grant Numbers: CSIST-095-V301 and CSIST-095-V302) and National Natural Science Foundation of China (Grant Number: 51475342)

Changes in EEG Permutation Entropy in the evening and in the transition from wake to sleep

Peer reviewe

Self-Organized Criticality as a Neurodynamical Correlate of Consciousness: A neurophysiological approach to measure states of consciousness based on EEG-complexity features

Background and Objectives This thesis was based on the hypothesis that the physics-derived theoretical framework of self-organized criticality can be applied to the neuronal dynamics of the human brain. From a consciousness science perspective, this is especially appealing as critical brain dynamics imply a vicinity a phase transition, which is associated with optimized information processing functions as well as the largest repertoire of configurations that a system explores throughout its temporal evolution. Hence, self-organised criticality could serve as a neurodynamical correlate for consciousness, which provides the possibility of deriving empirically testable neurophysiological indices suitable to characterise and quantify states of consciousness. The purpose of this work was to experimentally examine the feasibility of the self-organized criticality theory as a correlate for states of consciousness. Therefore, it was aimed at answering the following research questions based on the analysis of three 64 channel EEG datasets: (i) Can signatures of self-organized criticality be found on the level of the EEG in terms of scale-free distribution of neuronal avalanches and the presence of long-range temporal correlations (LRTC) in neuronal oscillations? (ii) Are criticality features suitable to differentiate state of consciousness in the spectrum of wakefulness? (iii) Can the neuronal dynamics be shifted towards the critical point of a phase transition associated with optimized information processing function by mind-body interventions? (iv) Can an explicit relationship to other nonlinear complexity features and power spectral density parameter be identified? (v) Do EEG-based criticality features reflect individual temperament traits? Material and Methods (1): Re-analysis: Thirty participants highly proficient in meditation (mean age 47 years, 11 females/19 males, meditation experience of at least 5 years practice or more than 1000 h of total meditation time) were measured with 64-channel EEG during one session consisting of a task-free baseline resting, a reading condition and three meditation conditions, namely thoughtless emptiness, presence monitoring and focused attention. (2): 64-channel EEG was recorded from 34 participants (mean age 36.0 ±13.4 years, 24 females/ 10 males) before, during and after a professional singing bowl massage. Further, psychometric data was assessed including absorption capacity defined as the individual’s capacity for engaging attentional resources in sensory and imaginative experiences measured by the Tellegen-Absorption Scale (TAS-D), subjective changes in in body sensation, emotional state, and mental state (CSP-14) as well as the phenomenology of consciousness (PCI-K). (3): Electrophysiological data (64 channels of EEG, EOG, ECG, skin conductance, and respiration) was recorded from 116 participants (mean age 40.0 ±13.4 years, 83 females/ 33 males) – in collaboration with the Institute of Psychology, Bundeswehr University Munich - during a task-free baseline resting state. The individual level of sensory processing sensitivity was assessed using the High Sensitive Person Scale (HSPS-G). The datasets were analysed applying analytical tools from self-organized criticality theory (detrended fluctuation analysis, neuronal avalanche analysis), nonlinear complexity algorithms (multiscale entropy, Higuchi’s fractal dimension) and power spectral density. In study 1 and 2, task conditions were contrasted, and effect sizes were compared using a paired two-tailed t-test calculated across participants, and features. T-values were corrected for multiple testing using false discovery rate. To calculate correlations between the EEG features, Spearman’s rank correlation was applied after determining that the distribution was not appropriate for parametric testing by the Shapiro-Wilk test. In addition, in study 1, a discrimination analysis was carried out to determine the classification performance of the EEG features. Here, partial least squares regression and receiver operating characteristics analysis was applied. To determine whether the EEG features reflect individual temperament traits, the individual level of absorption capacity (study 2) and sensory processing sensitivity (study 3) was correlated with the EEG features using Spearman’s rank correlation. Results Signatures of self-organized criticality in the form of scale-free distribution of neuronal avalanches and long-range temporal correlations (LRTCs) in the amplitude of neural oscillations were observed in three distinct EEG-datasets. EEG criticality as well as complexity features were suitable to characterise distinct states of consciousness. In study 1, compared to the task-free resting condition, all three meditative states revealed significantly reduced long-range temporal correlation with moderate effect sizes (presence monitoring: d= -0.49, p<.001; thoughtless emptiness: d= -0.37, p<.001; and focused attention: d= -0.28, p=.003). The critical exponent was suitable to differentiate between focused attention and presence monitoring (d= -0.32, p=.02). Further, in study 2, the criticality features significantly changed during the course of the experiment, whereby values indicated a shift towards the critical regime during the sound condition. Both analyses of the first and second dataset revealed that the critical exponent was significantly negatively correlated with the sample entropy, the scaling exponent resulting from the DFA denoting the amount of long-range temporal correlations as well as Higuchi’s fractal dimension in each condition, respectively. In addition, the critical scaling exponent was found to be significantly negatively correlated with the trait absorption (Spearman's ρ= -0.39, p= .007), whereas an association between critical dynamics and the level of sensory processing sensitivity could not be verified (study 3). Conclusion The findings of this thesis suggest that neuronal dynamics are governed by the phenomena of self-organized criticality. EEG-based criticality features were shown to be sensitive to detect experimentally induced alterations in the state of consciousness. Further, an explicit relationship with nonlinear measures determining the degree of neuronal complexity was identified. Thus, self-organized criticality seems feasible as a neurodynamical correlate for consciousness with the potential to quantify and characterize states of consciousness. Its agreement with the current most influencing theories in the field of consciousness research is discussed