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Power spectrum and spectrogram of EEG analysis during general anesthesia: Python-based computer programming analysis

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Abstract

The commonly used principle for measuring the depth of anesthesia involves changes in the frequency components of the electroencephalogram (EEG) under general anesthesia. Therefore, it is essential to construct an effective spectrum and spectrogram to analyze the relationship between the depth of anesthesia and the EEG frequency during general anesthesia. This paper reviews the computer programming techniques for analyzing the spectrum and spectrogram derived from a single-channel EEG recorded during general anesthesia. A periodogram is obtained by repeating a Fourier transform on EEG segments separated by short time intervals, but spectral leakage (i.e., dissociation from the true spectrum) occurs as a consequence of unnatural segmentation and noise. While offsetting the securing of the dynamic range, practical analyses of the adaptation of the window function are explained. Finally, the multitaper method, which can suppress artifacts caused by the edges of the analysis segments, suppress noise, and probabilistically infer values that are close to the real power spectral density, is explained using practical examples of the analysis. All analyses were performed and all graphs plotted using Python under Jupyter Notebook. The analyses demonstrated the effectiveness of Python-based programming under the integrated development environment Jupyter Notebook for constructing an effective spectrum and spectrogram for analyzing the relationship between the depth of anesthesia and EEG frequency analysis in general anesthesia.

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Data availability

The sample data and programming code using the analysis in this paper are available as supplementary data.

Code availability

The supplementary archive file (spectral_analysis.zip) contains all the Python programming code files (.ipynb files of Jupyter Notebook, and the PDF print files) for the EEG analyses described in this paper: (1) EDF2rawEEG.pdf: Python code that transforms the EEG in EDF format to microvolt data. (2) eeg_bis_10_20_2.tsv: Sample EEG data used in the following spectral analyses. (3) spectral_analysis_1.pdf and spectral_analysis_1.ipynb: Python code for the spectral analysis of the EEG data using sine and cosine waves, and the ARIMA model. (4) spectral_analysis_2_deep_anesth.pdf and spectral_analysis_2_deep_anesth.ipynb: Python code for the spectral analysis of the EEG data at the phase of deep general anesthesia. (5) spectral_analysis_3_before_emeregence.pdf and spectral_analysis_3_before_emeregence.ipynb: Python code for the spectral analysis of the EEG data at the phase before emergence from general anesthesia. (6) spectral_analysis_4_emergence.pdf and spectral_analysis_4_emergence.ipynb: Python code for the spectral analysis of EEG data at the phase of emergence from general anesthesia.

Change history

  • 24 December 2021

    Supplementary file has been included to the article.

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Acknowledgements

We thank Edanz (https://jp.edanz.com/ac) for editing a draft of this manuscript.

Funding

We do not have any financial or other interest in any product mentioned in this manuscript.

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Authors and Affiliations

  1. Department of Anesthesiology, Kyoto Prefectural University of Medicine, Kyoto, Japan

    Teiji Sawa & Tomomi Yamada

  2. Department of Anesthesia, Yodogawa Christian Hospital, Osaka, Japan

    Yurie Obata

Authors
  1. Teiji Sawa
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  2. Tomomi Yamada
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  3. Yurie Obata
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Contributions

TS conducted the study, data collection, data analysis, and manuscript preparation. TY and YO helped revise the manuscript. All authors gave final approval of the submitted manuscript.

Corresponding author

Correspondence to Teiji Sawa.

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Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

The EEG data used as examples for spectral analyses in this review were obtained from an anesthetized patient under ethical approval (No. ERB-C-1074-2) by the Institutional Review Board for Human Experiments at the Kyoto Prefectural University of Medicine (IRB of KPUM).

Informed consent

For this non-interventional and noninvasive retrospective observational study, informed patient consent was waived by the IRB of KPUM; patients were provided with an opt-out option, of which they were notified in the preoperative anesthesia clinic.

Research involving human and animal rights

The EEG data used as an example for the data analysis in this review were approved and the requirement for written informed consent was waived by the institutional review board of KPUM.

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Sawa, T., Yamada, T. & Obata, Y. Power spectrum and spectrogram of EEG analysis during general anesthesia: Python-based computer programming analysis. J Clin Monit Comput 36, 609–621 (2022). https://doi.org/10.1007/s10877-021-00771-4

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  • DOI: https://doi.org/10.1007/s10877-021-00771-4

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