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

Haruka Kida1,2, Tomomi Yamada3, Shoko Yamochi3

  • 1Department of Anesthesia, Fukuchiyama City Hospital, Kyoto 620-0056, Japan.

Sensors (Basel, Switzerland)
|February 27, 2026
PubMed
Summary

Singular spectrum analysis (SSA) with Hilbert transform improves electroencephalography (EEG) analysis for monitoring anesthesia depth. This method offers better temporal resolution and stable feature extraction for non-stationary EEG signals.

Keywords:
Hilbert–Huang transformdepth of anesthesiaelectroencephalogramgeneral anesthesiasingular spectrum analysissingular value decomposition

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Area of Science:

  • Neuroscience
  • Anesthesiology
  • Signal Processing

Background:

  • Electroencephalography (EEG) is crucial for monitoring anesthesia depth.
  • Conventional Fourier analysis struggles with non-stationary EEG dynamics during anesthesia.
  • Singular Spectrum Analysis (SSA) offers a novel approach to analyze complex EEG signals.

Purpose of the Study:

  • To investigate the utility of SSA combined with Hilbert transform for extracting EEG features during sevoflurane anesthesia.
  • To assess the ability of SSA-derived features to correlate with anesthetic depth.
  • To compare SSA with conventional spectral analysis for EEG monitoring.

Main Methods:

  • Frontal EEG data from 10 patients under sevoflurane anesthesia were analyzed.
  • EEG signals were decomposed into intrinsic mode functions (IMFs) using SSA.
  • Hilbert spectral analysis was applied to IMFs for instantaneous frequency and amplitude extraction.

Main Results:

  • SSA captured phase-dependent EEG changes, including alpha spindle activity and high-frequency components.
  • IMF features showed strong correlations with the Bispectral Index (BIS) (R² = 0.88, MAE < 4).
  • SSA demonstrated superior temporal resolution and stable feature extraction compared to conventional methods.

Conclusions:

  • SSA combined with Hilbert analysis provides a robust framework for quantitative EEG analysis during general anesthesia.
  • This approach may enhance real-time, individualized assessments of anesthetic depth.
  • The findings suggest potential for improved patient safety and anesthetic management.