Frontal EEG Temporal and Spectral Dynamics Similarity Analysis between Propofol and Desflurane Induced Anesthesia Using Hilbert-Huang Transform

Wavelet transform-based mode decomposition for EEG signals under general anesthesia

Background

Mode decomposition methods are used to extract the characteristic intrinsic mode function (IMF) from various multidimensional time series signals. We analyzed an electroencephalogram (EEG) dataset for sevoflurane anesthesia using two wavelet transform-based mode decomposition methods, comprising the empirical wavelet transform (EWT) and wavelet mode decomposition (WMD) methods, and compared the results with those from the previously reported variational mode decomposition (VMD) method.

Methods

To acquire the EEG data, we used the software application EEG Analyzer, which enabled the recording of raw EEG signals via the serial interface of a bispectral index (BIS) monitor. We also created EEG mode decomposition software to perform empirical mode decomposition (EMD), VMD, EWT, and WMD operations.

Results

When decomposed into six IMFs, the EWT enables narrow band separation of the low-frequency bands IMF-1 to IMF-3, in which all central frequencies are less than 10 Hz. However, in the upper IMF of the high-frequency band, which has a center frequency of ≥ 10 Hz, the dispersion within the frequency band covered was widespread among the individual patients. In WMD, a narrow band of clinical interest is specified using a bandpass filter in a Meyer wavelet filter bank within a specific mode-decomposition discipline. When compared with the VMD and EWT methods, the IMF that was decomposed via WMD was accommodated in a narrow band with only a small variance for each patient. Multiple linear regression analyses demonstrated that the frequency characteristics of the IMFs obtained from WMD best tracked the changes in the BIS upon emergence from general anesthesia.

Conclusions

The WMD can be used to extract subtle frequency characteristics of EEGs that have been affected by general anesthesia, thus potentially providing better parameters for use in assessing the depth of general anesthesia.

Variational Mode Decomposition Analysis of Electroencephalograms during General Anesthesia: Using the Grey Wolf Optimizer to Determine Hyperparameters

Frequency analysis via electroencephalography (EEG) during general anesthesia is used to develop techniques for measuring anesthesia depth. Variational mode decomposition (VMD) enables mathematical optimization methods to decompose EEG signals into natural number intrinsic mode functions with distinct narrow bands. However, the analysis requires the a priori determination of hyperparameters, including the decomposition number (K) and the penalty factor (PF). In the VMD analysis of EEGs derived from a noninterventional and noninvasive retrospective observational study, we adapted the grey wolf optimizer (GWO) to determine the K and PF hyperparameters of the VMD. As a metric for optimization, we calculated the envelope function of the IMF decomposed via the VMD method and used its envelope entropy as the fitness function. The K and PF values varied in each epoch, with one epoch being the analytical unit of EEG; however, the fitness values showed convergence at an early stage in the GWO algorithm. The K value was set to 2 to capture the α wave enhancement observed during the maintenance phase of general anesthesia in intrinsic mode function 2 (IMF-2). This study suggests that using the GWO to optimize VMD hyperparameters enables the construction of a robust analytical model for examining the EEG frequency characteristics involved in the effects of general anesthesia.

Changes in EEG frequency characteristics during sevoflurane general anesthesia: feature extraction by variational mode decomposition

Mode decomposition is a method for extracting the characteristic intrinsic mode function (IMF) from various multidimensional time-series signals. Variational mode decomposition (VMD) searches for IMFs by optimizing the bandwidth to a narrow band with the [Formula: see text] norm while preserving the online estimated central frequency. In this study, we applied VMD to the analysis of electroencephalogram (EEG) recorded during general anesthesia. Using a bispectral index monitor, EEGs were recorded from 10 adult surgical patients (the median age: 47.0, and the percentile range: 27.0-59.3 years) who were anesthetized with sevoflurane. We created an application named EEG Mode Decompositor, which decomposes the recorded EEG into IMFs and displays the Hilbert spectrogram. Over the 30-min recovery from general anesthesia, the median (25-75 percentile range) bispectral index increased from 47.1 (42.2-50.4) to 97.4 (96.5-97.6), and the central frequencies of IMF-1 showed a significant change from 0.4 (0.2-0.5) Hz to 0.2 (0.1-0.3) Hz. IMF-2, IMF-3, IMF-4, IMF-5, and IMF-6 increased significantly from 1.4 (1.2-1.6) Hz to 7.5 (1.5-9.3) Hz, 6.7 (4.1-7.6) Hz to 19.4 (6.9-20.0) Hz, 10.9 (8.8-11.4) Hz to 26.4 (24.2-27.2) Hz, 13.4 (11.3-16.6) Hz to 35.6 (34.9-36.1) Hz, and 12.4 (9.7-18.1) Hz to 43.2 (42.9-43.4) Hz, respectively. The characteristic frequency component changes in specific IMFs during emergence from general anesthesia were visually captured by IMFs derived using VMD. EEG analysis by VMD is useful for extracting distinct changes during general anesthesia.

Time-trend analysis of the center frequency of the intrinsic mode function from the Hilbert-Huang transform of electroencephalography during general anesthesia: a retrospective observational study

BACKGROUND

Anesthesiologists are required to maintain an optimal depth of anesthesia during general anesthesia, and several electroencephalogram (EEG) processing methods have been developed and approved for clinical use to evaluate anesthesia depth. Recently, the Hilbert-Huang transform (HHT) was introduced to analyze nonlinear and nonstationary data. In this study, we assessed whether the changes in EEG characteristics during general anesthesia that are analyzed by the HHT are useful for monitoring the depth of anesthesia.

METHODS

This retrospective observational study enrolled patients who underwent propofol anesthesia. Raw EEG signals were obtained from a monitor through a previously developed software application. We developed an HHT analyzer to decompose the EEG signal into six intrinsic mode functions (IMFs) and estimated the instantaneous frequencies (HHT_IF) for each IMF. Changes over time in the raw EEG waves and parameters such as HHT_IF, BIS, spectral edge frequency 95 (SEF95), and electromyogram parameter (EMGlow) were assessed, and a Gaussian process regression model was created to assess the association between BIS and HHT_IF.

RESULTS

We analyzed EEG signals from 30 patients. The beta oscillation frequency range (13-25 Hz) was detected in IMF1 and IMF2 during the awake state, then after loss of consciousness, the frequency decreased and alpha oscillation (8-12 Hz) was detected in IMF2. At the emergence phase, the frequency increased and beta oscillations were detected in IMF1, IMF2, and IMF3. BIS and EMGlow changed significantly during the induction and emergence phases, whereas SEF95 showed a wide variability and no significant changes during the induction phase. The root mean square error between the observed BIS values and the values predicted by a Gaussian process regression model ranged from 4.69 to 9.68.

CONCLUSIONS

We applied the HHT to EEG analyses during propofol anesthesia. The instantaneous frequency in IMF1 and IMF2 identified changes in EEG characteristics during induction and emergence from general anesthesia. Moreover, the HHT_IF in IMF2 showed strong associations with BIS and was suitable for depicting the alpha oscillation. Our study suggests that the HHT is useful for monitoring the depth of anesthesia.

A Two-Stream Graph Convolutional Network Based on Brain Connectivity for Anesthetized States Analysis

Investigating neural mechanisms of anesthesia process and developing efficient anesthetized state detection methods are especially on high demand for clinical consciousness monitoring. Traditional anesthesia monitoring methods are not involved with the topological changes between electrodes covering the prefrontal-parietal cortices, by investigating electrocorticography (ECoG). To fill this gap, a framework based on the two-stream graph convolutional network (GCN) was proposed, i.e., one stream for extracting topological structure features, and the other one for extracting node features. The two-stream graph convolutional network includes GCN Model 1 and GCN Model 2. For GCN Model 1, brain connectivity networks were constructed by using phase lag index (PLI), representing different structure features. A common adjacency matrix was founded through the dual-graph method, the structure features were expressed on nodes. Therefore, the traditional spectral graph convolutional network can be directly applied on the graphs with changing topological structures. On the other hand, the average of the absolute signal amplitudes was calculated as node features, then a fully connected matrix was constructed as the adjacency matrix of these node features, as the input of GCN Model 2. This method learns features of both topological structure and nodes of the graph, and uses a dual-graph approach to enhance the focus on topological structure features. Based on the ECoG signals of monkeys, results show that this method which can distinguish awake state, moderate sedation and deep sedation achieved an accuracy of 92.75% in group-level experiments and mean accuracy of 93.50% in subject-level experiments. Our work verifies the excellence of the graph convolutional network in anesthesia monitoring, the high recognition accuracy also shows that the brain network may carry neurological markers associated with anesthesia.

Explainable AI (XAI) Applied in Machine Learning for Pain Modeling: A Review

Pain is a complex term that describes various sensations that create discomfort in various ways or types inside the human body. Generally, pain has consequences that range from mild to severe in different organs of the body and will depend on the way it is caused, which could be an injury, illness or medical procedures including testing, surgeries or therapies, etc. With recent advances in artificial-intelligence (AI) systems associated in biomedical and healthcare settings, the contiguity of physician, clinician and patient has shortened. AI, however, has more scope to interpret the pain associated in patients with various conditions by using any physiological or behavioral changes. Facial expressions are considered to give much information that relates with emotions and pain, so clinicians consider these changes with high importance for assessing pain. This has been achieved in recent times with different machine-learning and deep-learning models. To accentuate the future scope and importance of AI in medical field, this study reviews the explainable AI (XAI) as increased attention is given to an automatic assessment of pain. This review discusses how these approaches are applied for different pain types.

Brain Connectivity Analysis in Anesthetized and Awake States: an ECoG Study in Monkeys

Increasingly, studies have shown that changes in brain network topology accompany loss of consciousness such that the functional connectivity of the prefrontal-parietal network differs significantly in anesthetized and awake states. In this work, anesthetized and awake segments of electrocorticography were selected from two monkeys. Using phase lag index, functional connectivity matrices were built in multiple frequency bands. Quantifying topological changes in brain network through graph-theoretic properties revealed significant differences between the awake and anesthetized states. Compared to the awake state, there were distinct increases in overall and Delta prefrontal-frontal connectivity, and decreases in Alpha, Beta1 and Beta2 prefrontal-frontal connectivity during the anesthetized state, which indicate a change in the topology of the small-world network. Using functional connectivity features we achieved a satisfactory classification accuracy (93.68%). Our study demonstrates that functional connectivity features are of sufficient power to distinguish awake versus anesthetized state.Clinical Relevance- This explores the brain network topology in awake and anesthetized states, and provides new ideas for clinical depth of anesthesia monitoring.

Comparison of the Conox (qCON) and Sedline (PSI) depth of anaesthesia indices to predict the hypnotic effect during desflurane general anaesthesia with ketamine

Comparison of two depth of anesthesia indices, qCON (Conox) and PSI (Sedline), during desflurane sedation and their sensitivity to random ketamine boluses in patients undergoing routine surgery. The performance of desflurane and ketamine on both indices was analyzed for 11 patients, and the ketamine sensitivity was compared with another group of 11 patients under sevoflurane and propofol.The MOAA/S was used to determine sedation level and pain. Different boluses of ketamine ranging from 10 to 30 mg where randomly administered in both groups and the effect on the indexes were measured after 4 min.The indices were recorded during the whole surgery, and their correlations with the desflurane concentration and the discrimination between awake and anesthetized states were evaluated with the prediction probability statistic (Pk). The Pk values, mean (se), discriminating between awake and anesthetized states were 0.974(0.016) for the qCON and 0.962(0.0123) for the PSI, while the 1-Pk statistic for the qCON and the PSI with respect to the desflurane concentration were 0.927(0.016) and 0.918(0.018), respectively, with no statistically significant differences.The agreement between both depth of hypnosis parameters was assessed under the Bland-Altman plot and the Spearman correlation, r_s = 0.57(p < 0.001).During the sevoflurane-propofol anesthesia, which served as a control group, both indices experienced a similar behavior with a no significant change of their median values after ketamine. However, during desflurane anesthesia the qCON index did not change significantly after ketamine administration, qCON (before = 33 (4), after = 30 (17); Wilcoxon, p = 0.89), while the PSI experienced a significant increase, PSI (before = 31(6), after = 39(16) Wilcoxon, p = 0.013).This study shows that qCON and PSI have similar performance under desflurane with good discrimination between the awake and anesthetized states. While both indices exhibited similar behavior under ketamine boluses under a sevoflurane-propofol anesthesia, the qCON index had a better performance under ketamine during desflurane anesthesia.