A wavelet transform based method to determine depth of anesthesia to prevent awareness during 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.

A Call for Real-Time Bispectral Index and Electroencephalogram Monitoring in a Patient Undergoing Aortic Surgery

A 77-year-old woman underwent replacement of the ascending aorta and aortic valve. Before separation from cardiopulmonary bypass, the pump flow was reduced to 0-to-1.1 L/min/m² for four minutes at a tympanic temperature of 34.3°C to perform additional sutureing for aorta-graft anastomosis. Postoperative magnetic resonance imaging revealed watershed cerebral infarction. An offline scalogram of intraoperative electroencephalograms obtained from the bispectral index monitor, which was generated by using continuous wavelet transform with complex Morlet wavelets, readily visualized the process of development of cerebral infarction preceding a significant decrease of regional cerebral oxygen saturation during the low-flow period of cardiopulmonary bypass. The present case demonstrated the possible importance of real-time bispectral index and electroencephalogram monitoring in patients undergoing cardiovascular surgery, especially those undergoing high-risk procedures under hypothermic circulatory arrest.

Time-Frequency Analysis of EEG Signals and GLCM Features for Depth of Anesthesia Monitoring

One of the important tasks in the operating room is monitoring the depth of anesthesia (DoA) during surgery, and noninvasive techniques are very popular. Hence, we propose a new scheme for DoA monitoring considering the time-frequency analysis of electroencephalography (EEG) signals and GLCM features extracted from them. To this end, at first, the time-frequency map (TFM) of each channel of each EEG is computed by smoothed pseudo-Wigner–Ville distribution (SPWVD), where the EEG signal used in this paper is recorded in 15 channels. After that, we consider the gray-level co-occurrence matrix (GLCM) to obtain the content of TFM, and after that, four features such as homogeneity, correlation, energy, and contrast are obtained for each GLCM. Finally, after the selection of efficient features using the minimum redundancy maximum relevance (MRMR) method, the K-nearest neighbor (KNN) classifier is utilized to determine the DoA. Here, we consider the three states, namely, deep hypnotic, surgical anesthesia, and sedation and awake states according to bispectral index (BIS), and each EEG epoch is classified to these states. We also employ data augmentation to enhance the training phase and increase accuracy. We obtain the accuracy and confusion matrix of the proposed method. We also analyze the effects of a number of gray levels of GLCM, distance measure in KNN classifier, and parameters of data augmentation on the performance of the proposed method. Results indicate the efficiency of the proposed method to determine the DoA during surgery.