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Wavelet transform-based mode decomposition for EEG signals under general anesthesia.
Shoko Yamochi1, Tomomi Yamada1, Yurie Obata2
1Department of Anesthesiology, Kyoto Prefectural University of Medicine, Kyoto, Japan.
Peerj
|November 19, 2024
Summary
Wavelet mode decomposition (WMD) effectively extracts subtle electroencephalogram (EEG) frequency changes during sevoflurane anesthesia. This method offers improved parameters for monitoring anesthesia depth compared to empirical wavelet transform (EWT) and variational mode decomposition (VMD).
Area of Science:
- Signal processing and biomedical engineering.
- Analysis of electroencephalogram (EEG) signals for anesthesia monitoring.
Background:
- Mode decomposition methods are crucial for extracting intrinsic mode functions (IMFs) from complex time series data.
- Electroencephalogram (EEG) signals are analyzed to understand brain activity during general anesthesia.
Purpose of the Study:
- To compare the efficacy of empirical wavelet transform (EWT) and wavelet mode decomposition (WMD) with variational mode decomposition (VMD) for analyzing EEG signals during sevoflurane anesthesia.
- To identify the most suitable mode decomposition method for tracking changes in bispectral index (BIS) during emergence from anesthesia.
Main Methods:
- Acquired raw EEG data using EEG Analyzer software connected to a bispectral index (BIS) monitor.
- Developed custom software for empirical mode decomposition (EMD), VMD, EWT, and WMD.
- Analyzed EEG signals using EWT and WMD, comparing results with VMD.
Main Results:
- Empirical wavelet transform (EWT) showed widespread dispersion in high-frequency bands (≥ 10 Hz).
- Wavelet mode decomposition (WMD) provided narrow-band separation with minimal variance across patients, outperforming VMD and EWT.
- Multiple linear regression confirmed that WMD-derived IMFs best correlated with BIS changes during anesthesia emergence.
Conclusions:
- Wavelet mode decomposition (WMD) effectively captures subtle EEG frequency alterations induced by general anesthesia.
- WMD offers a promising approach for developing enhanced parameters to assess anesthesia depth.
