Assessment of Anesthetic Depth Through EEG Mode Decomposition Using Singular Spectrum Analysis

TL;DR

This paper introduces a new method using singular spectrum analysis to monitor anesthetic depth through EEG signals, offering better accuracy and adaptability than traditional methods.

Contribution

The novel use of SSA with Hilbert transform enables high-resolution decomposition of EEG signals without predefined frequency bands, improving depth-of-anesthesia monitoring.

Findings

SSA-based decomposition captured phase-dependent EEG changes, including α spindle activity during maintenance and high-frequency components before emergence.

Regression models using SSA-derived parameters achieved strong correlation with BIS values (R² ≈ 0.88).

Abstract

What are the main findings?
Singular spectrum analysis (SSA) combined with Hilbert transform enabled robust, high-resolution decomposition of non-stationary EEG signals during sevoflurane general anesthesia, effectively separating trends, rhythmic components, and fast activity without predefined frequency bands.SSA-derived intrinsic mode function (IMF) parameters showed strong correlations with the BIS, and multiple linear regression models using selected IMF center frequencies and total power accurately predicted BIS values during the transition from maintenance to emergence (R2 ≈ 0.88).

Singular spectrum analysis (SSA) combined with Hilbert transform enabled robust, high-resolution decomposition of non-stationary EEG signals during sevoflurane general anesthesia, effectively separating trends, rhythmic components, and fast activity without predefined frequency bands.

SSA-derived intrinsic mode function (IMF) parameters showed strong correlations with the BIS, and multiple linear regression models using selected IMF center frequencies and total power accurately predicted BIS values during the transition from maintenance to emergence (R2 ≈ 0.88).

What are the implications of the main findings?
SSA offers a physiologically grounded and temporally precise alternative to conventional Fourier-based EEG analysis for depth-of-anesthesia monitoring, overcoming limitations related to non-stationarity and fixed frequency assumptions.This approach has the potential to improve real-time, individualized anesthesia management and provides a scalable framework into which advanced modeling or AI techniques could be integrated to enhance patient safety and reduce the risk of over- or under-anesthesia.

SSA offers a physiologically grounded and temporally precise alternative to conventional Fourier-based EEG analysis for depth-of-anesthesia monitoring, overcoming limitations related to non-stationarity and fixed frequency assumptions.

This approach has the potential to improve real-time, individualized anesthesia management and provides a scalable framework into which advanced modeling or AI techniques could be integrated to enhance patient safety and reduce the risk of over- or under-anesthesia.

(1) Background: Electroencephalography (EEG) is widely used to monitor the depth of anesthesia; however, conventional Fourier-based analyses are limited in their ability to characterize non-stationary anesthetic-induced EEG dynamics. In this study, we investigated the utility of singular spectrum analysis (SSA) combined with the Hilbert transform for extracting physiologically meaningful EEG features under sevoflurane general anesthesia. (2) Methods: Frontal EEG data from ten patients undergoing sevoflurane anesthesia were analyzed from the maintenance phase through emergence. Using SSA, short EEG segments were decomposed into six intrinsic mode functions (IMFs) without pre-specified basis functions or frequency bands. Hilbert spectral analysis was applied to each IMF to obtain instantaneous frequency and amplitude characteristics. (3) Results: The SSA-based decomposition clearly captured phase-dependent EEG changes, including α spindle activity during maintenance and increasing high-frequency components preceding emergence. Multiple linear regression models incorporating IMF center frequencies and total power demonstrated strong correlations with the bispectral index (BIS), achieving high predictive accuracy (R2 = 0.88, MAE < 4). Compared with conventional spectral approaches, SSA provided superior temporal resolution and stable feature extraction for non-stationary EEG signals. (4) Conclusions: These findings indicate that SSA combined with Hilbert analysis is a robust framework for quantitative EEG analysis during general anesthesia and may enhance real-time, individualized assessments of anesthetic depth.

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