Changes in EEG frequency characteristics during sevoflurane general anesthesia: feature extraction by variational
Tomomi Yamada1, Yurie Obata2, Kazuki Sudo1
1Department of Anesthesiology, Graduate School of Medical Science, Kyoto Prefectural University of Medicine, 465 Kajiicho, Kawaramachi-Hirokoji, Kamigyo, Kyoto, 602-8566, Japan.
Variational Mode Decomposition (VMD) effectively analyzed electroencephalogram (EEG) signals during general anesthesia recovery. This method revealed distinct changes in intrinsic mode functions (IMFs), aiding in understanding anesthesia emergence.
Area of Science:
- Neuroscience
- Signal Processing
- Medical Informatics
Background:
- General anesthesia involves complex changes in brain activity, often monitored using electroencephalogram (EEG).
- Variational Mode Decomposition (VMD) is a signal processing technique for extracting intrinsic mode functions (IMFs) from time-series data.
- Analyzing EEG during anesthesia can provide insights into the depth and emergence from anesthetic states.
Purpose of the Study:
- To apply Variational Mode Decomposition (VMD) for analyzing electroencephalogram (EEG) signals during general anesthesia recovery.
- To develop and utilize an application, EEG Mode Decompositor, for decomposing EEG into IMFs and visualizing the Hilbert spectrogram.
- To identify and characterize changes in specific IMFs corresponding to emergence from general anesthesia.
Main Methods:
- EEG data were recorded from 10 adult patients undergoing sevoflurane anesthesia using a bispectral index monitor.
- The EEG Mode Decompositor application was used to decompose EEG signals into intrinsic mode functions (IMFs) via VMD.
- Central frequencies of the IMFs were analyzed during the 30-minute recovery period, correlating with bispectral index changes.
Main Results:
- A significant increase in the bispectral index from 47.1 to 97.4 was observed during recovery.
- The central frequency of IMF-1 significantly decreased from 0.4 Hz to 0.2 Hz.
- Significant increases in the central frequencies of IMF-2 through IMF-6 were observed, indicating dynamic changes in EEG components during emergence from anesthesia.
Conclusions:
- VMD is a valuable tool for analyzing EEG signals during general anesthesia, enabling the extraction of characteristic frequency changes.
- The identified IMF changes provide visualizable markers for monitoring emergence from anesthesia.
- This approach offers a novel method for understanding the neurophysiological effects of anesthetic agents.
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