Variational Mode Decomposition Analysis of Electroencephalograms during General Anesthesia: Using the Grey Wolf...

Area of Science:

• Neuroscience
• Biomedical Engineering
• Signal Processing

Background:

• Electroencephalography (EEG) is crucial for monitoring anesthesia depth.
• Variational Mode Decomposition (VMD) is a signal processing technique used for EEG analysis.
• VMD requires careful selection of hyperparameters like decomposition number (K) and penalty factor (PF).

Purpose of the Study:

• To develop an optimized VMD method for analyzing EEG signals during general anesthesia.
• To adapt the Grey Wolf Optimizer (GWO) for determining VMD hyperparameters (K and PF).
• To establish a robust analytical model for EEG frequency characteristics under anesthesia.

Main Methods:

• Retrospective observational study using noninterventional EEG data.
• Applied Variational Mode Decomposition (VMD) to decompose EEG signals.
• Utilized Grey Wolf Optimizer (GWO) to determine VMD's K and PF hyperparameters.
• Employed envelope entropy of intrinsic mode functions (IMFs) as the fitness function for GWO.

Main Results:

• GWO successfully optimized VMD hyperparameters (K and PF) for EEG analysis.
• Fitness values showed early convergence in the GWO algorithm.
• A fixed K value of 2 was determined to effectively capture alpha wave enhancement in IMF-2 during anesthesia maintenance.
• The optimized VMD method demonstrated robustness in analyzing EEG frequency characteristics.

Conclusions:

• GWO-optimized VMD provides a robust framework for analyzing EEG signals during general anesthesia.
• This approach enhances the measurement of anesthesia depth by examining specific EEG frequency bands.
• The study highlights the potential of adaptive hyperparameter optimization for advanced neurophysiological signal analysis.