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Yamochi S, Yamada T, Obata Y, Sudo K, Kinoshita M, Akiyama K, Sawa T. Wavelet transform-based mode decomposition for EEG signals under general anesthesia. PeerJ 2024; 12:e18518. [PMID: 39559333 PMCID: PMC11572389 DOI: 10.7717/peerj.18518] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2024] [Accepted: 10/22/2024] [Indexed: 11/20/2024] Open
Abstract
Background Mode decomposition methods are used to extract the characteristic intrinsic mode function (IMF) from various multidimensional time series signals. We analyzed an electroencephalogram (EEG) dataset for sevoflurane anesthesia using two wavelet transform-based mode decomposition methods, comprising the empirical wavelet transform (EWT) and wavelet mode decomposition (WMD) methods, and compared the results with those from the previously reported variational mode decomposition (VMD) method. Methods To acquire the EEG data, we used the software application EEG Analyzer, which enabled the recording of raw EEG signals via the serial interface of a bispectral index (BIS) monitor. We also created EEG mode decomposition software to perform empirical mode decomposition (EMD), VMD, EWT, and WMD operations. Results When decomposed into six IMFs, the EWT enables narrow band separation of the low-frequency bands IMF-1 to IMF-3, in which all central frequencies are less than 10 Hz. However, in the upper IMF of the high-frequency band, which has a center frequency of ≥ 10 Hz, the dispersion within the frequency band covered was widespread among the individual patients. In WMD, a narrow band of clinical interest is specified using a bandpass filter in a Meyer wavelet filter bank within a specific mode-decomposition discipline. When compared with the VMD and EWT methods, the IMF that was decomposed via WMD was accommodated in a narrow band with only a small variance for each patient. Multiple linear regression analyses demonstrated that the frequency characteristics of the IMFs obtained from WMD best tracked the changes in the BIS upon emergence from general anesthesia. Conclusions The WMD can be used to extract subtle frequency characteristics of EEGs that have been affected by general anesthesia, thus potentially providing better parameters for use in assessing the depth of general anesthesia.
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Affiliation(s)
- Shoko Yamochi
- Department of Anesthesiology, Kyoto Prefectural University of Medicine, Kyoto, Japan
| | - Tomomi Yamada
- Department of Anesthesiology, Kyoto Prefectural University of Medicine, Kyoto, Japan
| | - Yurie Obata
- Department of Anesthesia, Yodogawa Christian Hospital, Osaka, Japan
| | - Kazuki Sudo
- Department of Anesthesiology, Kyoto Prefectural University of Medicine, Kyoto, Japan
| | - Mao Kinoshita
- Department of Anesthesiology, Kyoto Prefectural University of Medicine, Kyoto, Japan
| | - Koichi Akiyama
- Department of Anesthesiology, Kindai University, Osakasayama, Osaka, Japan
| | - Teiji Sawa
- University Hospital, Kyoto Prefectural University of Medicine, Kyoto, Japan
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Morita H, Kinoshita H, Kiyokawa M, Kushikata T, Hirota K. Remimazolam and Remifentanil Anesthetics for an Adolescent Patient with Stiff-Person Syndrome: A Case Report. A A Pract 2024; 18:e01758. [PMID: 38373229 DOI: 10.1213/xaa.0000000000001758] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/21/2024]
Abstract
Stiff-person syndrome (SPS) is a rare autoimmune disease characterized by fluctuating rigidity and stiffness of the axial muscles. There are no reports on the use of remimazolam in a patient with SPS. A 16-year-old Japanese woman with SPS was scheduled to undergo intrathecal baclofen pump exchange. General anesthesia was induced and maintained using remimazolam, remifentanil, and intermittent rocuronium bromide. No intraoperative mobility or significant autonomic symptoms were observed. Additionally, electroencephalographic signature showed sufficient anesthetic depth. The patient's emergence from general anesthesia was uneventful. In conclusion, remimazolam could be considered an effective anesthetic drug for patients with SPS.
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Affiliation(s)
| | | | | | | | - Kazuyoshi Hirota
- From the Departments of Anesthesiology
- Perioperative Medicine for Community Healthcare
- Perioperative Stress Management, Hirosaki University Graduate School of Medicine, Hirosaki, Japan
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Yamada T, Obata Y, Sudo K, Kinoshita M, Naito Y, Sawa T. Changes in EEG frequency characteristics during sevoflurane general anesthesia: feature extraction by variational mode decomposition. J Clin Monit Comput 2023; 37:1179-1192. [PMID: 37395808 DOI: 10.1007/s10877-023-01037-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2023] [Accepted: 05/16/2023] [Indexed: 07/04/2023]
Abstract
Mode decomposition is a method for extracting the characteristic intrinsic mode function (IMF) from various multidimensional time-series signals. Variational mode decomposition (VMD) searches for IMFs by optimizing the bandwidth to a narrow band with the [Formula: see text] norm while preserving the online estimated central frequency. In this study, we applied VMD to the analysis of electroencephalogram (EEG) recorded during general anesthesia. Using a bispectral index monitor, EEGs were recorded from 10 adult surgical patients (the median age: 47.0, and the percentile range: 27.0-59.3 years) who were anesthetized with sevoflurane. We created an application named EEG Mode Decompositor, which decomposes the recorded EEG into IMFs and displays the Hilbert spectrogram. Over the 30-min recovery from general anesthesia, the median (25-75 percentile range) bispectral index increased from 47.1 (42.2-50.4) to 97.4 (96.5-97.6), and the central frequencies of IMF-1 showed a significant change from 0.4 (0.2-0.5) Hz to 0.2 (0.1-0.3) Hz. IMF-2, IMF-3, IMF-4, IMF-5, and IMF-6 increased significantly from 1.4 (1.2-1.6) Hz to 7.5 (1.5-9.3) Hz, 6.7 (4.1-7.6) Hz to 19.4 (6.9-20.0) Hz, 10.9 (8.8-11.4) Hz to 26.4 (24.2-27.2) Hz, 13.4 (11.3-16.6) Hz to 35.6 (34.9-36.1) Hz, and 12.4 (9.7-18.1) Hz to 43.2 (42.9-43.4) Hz, respectively. The characteristic frequency component changes in specific IMFs during emergence from general anesthesia were visually captured by IMFs derived using VMD. EEG analysis by VMD is useful for extracting distinct changes during general anesthesia.
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Affiliation(s)
- Tomomi Yamada
- Department of Anesthesiology, Graduate School of Medical Science, Kyoto Prefectural University of Medicine, 465 Kajiicho, Kawaramachi-Hirokoji, Kamigyo, Kyoto, 602-8566, Japan
| | - Yurie Obata
- Department of Anesthesiology, Yodogawa Christian Hospital, Shibashima 1-7-50, Higashiyodogawa, Osaka, 533-0024, Japan
| | - Kazuki Sudo
- Department of Anesthesiology, Graduate School of Medical Science, Kyoto Prefectural University of Medicine, 465 Kajiicho, Kawaramachi-Hirokoji, Kamigyo, Kyoto, 602-8566, Japan
| | - Mao Kinoshita
- Department of Anesthesiology, Graduate School of Medical Science, Kyoto Prefectural University of Medicine, 465 Kajiicho, Kawaramachi-Hirokoji, Kamigyo, Kyoto, 602-8566, Japan
| | - Yoshifumi Naito
- Department of Anesthesiology, Graduate School of Medical Science, Kyoto Prefectural University of Medicine, 465 Kajiicho, Kawaramachi-Hirokoji, Kamigyo, Kyoto, 602-8566, Japan
| | - Teiji Sawa
- Department of Anesthesiology, Graduate School of Medical Science, Kyoto Prefectural University of Medicine, 465 Kajiicho, Kawaramachi-Hirokoji, Kamigyo, Kyoto, 602-8566, Japan.
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Obata Y, Yamada T, Akiyama K, Sawa T. Time-trend analysis of the center frequency of the intrinsic mode function from the Hilbert-Huang transform of electroencephalography during general anesthesia: a retrospective observational study. BMC Anesthesiol 2023; 23:125. [PMID: 37059989 PMCID: PMC10105429 DOI: 10.1186/s12871-023-02082-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2023] [Accepted: 04/06/2023] [Indexed: 04/16/2023] Open
Abstract
BACKGROUND Anesthesiologists are required to maintain an optimal depth of anesthesia during general anesthesia, and several electroencephalogram (EEG) processing methods have been developed and approved for clinical use to evaluate anesthesia depth. Recently, the Hilbert-Huang transform (HHT) was introduced to analyze nonlinear and nonstationary data. In this study, we assessed whether the changes in EEG characteristics during general anesthesia that are analyzed by the HHT are useful for monitoring the depth of anesthesia. METHODS This retrospective observational study enrolled patients who underwent propofol anesthesia. Raw EEG signals were obtained from a monitor through a previously developed software application. We developed an HHT analyzer to decompose the EEG signal into six intrinsic mode functions (IMFs) and estimated the instantaneous frequencies (HHT_IF) for each IMF. Changes over time in the raw EEG waves and parameters such as HHT_IF, BIS, spectral edge frequency 95 (SEF95), and electromyogram parameter (EMGlow) were assessed, and a Gaussian process regression model was created to assess the association between BIS and HHT_IF. RESULTS We analyzed EEG signals from 30 patients. The beta oscillation frequency range (13-25 Hz) was detected in IMF1 and IMF2 during the awake state, then after loss of consciousness, the frequency decreased and alpha oscillation (8-12 Hz) was detected in IMF2. At the emergence phase, the frequency increased and beta oscillations were detected in IMF1, IMF2, and IMF3. BIS and EMGlow changed significantly during the induction and emergence phases, whereas SEF95 showed a wide variability and no significant changes during the induction phase. The root mean square error between the observed BIS values and the values predicted by a Gaussian process regression model ranged from 4.69 to 9.68. CONCLUSIONS We applied the HHT to EEG analyses during propofol anesthesia. The instantaneous frequency in IMF1 and IMF2 identified changes in EEG characteristics during induction and emergence from general anesthesia. Moreover, the HHT_IF in IMF2 showed strong associations with BIS and was suitable for depicting the alpha oscillation. Our study suggests that the HHT is useful for monitoring the depth of anesthesia.
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Affiliation(s)
- Yurie Obata
- Department of Anesthesiology, Yodogawa Christian Hospital, 1-7-50 Kunijima, Higashiyodogawaku, 533-0024, Osaka, Japan.
| | - Tomomi Yamada
- Department of Anesthesiology, Kyoto Prefectural University of Medicine, Kyoto, Japan
| | - Koichi Akiyama
- Department of Anesthesiology, Kindai University, Osaka, Japan
| | - Teiji Sawa
- Department of Anesthesiology, Kyoto Prefectural University of Medicine, Kyoto, Japan
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Chen X, Xu G, Du C, Zhang S, Zhang X, Teng Z. Poincaré Plot Nonextensive Distribution Entropy: A New Method for Electroencephalography (EEG) Time Series. SENSORS (BASEL, SWITZERLAND) 2022; 22:6283. [PMID: 36016044 PMCID: PMC9415957 DOI: 10.3390/s22166283] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/13/2022] [Revised: 08/11/2022] [Accepted: 08/18/2022] [Indexed: 06/15/2023]
Abstract
As a novel form of visual analysis technique, the Poincaré plot has been used to identify correlation patterns in time series that cannot be detected using traditional analysis methods. In this work, based on the nonextensive of EEG, Poincaré plot nonextensive distribution entropy (NDE) is proposed to solve the problem of insufficient discrimination ability of Poincaré plot distribution entropy (DE) in analyzing fractional Brownian motion time series with different Hurst indices. More specifically, firstly, the reasons for the failure of Poincaré plot DE in the analysis of fractional Brownian motion are analyzed; secondly, in view of the nonextensive of EEG, a nonextensive parameter, the distance between sector ring subintervals from the original point, is introduced to highlight the different roles of each sector ring subinterval in the system. To demonstrate the usefulness of this method, the simulated time series of the fractional Brownian motion with different Hurst indices were analyzed using Poincaré plot NDE, and the process of determining the relevant parameters was further explained. Furthermore, the published sleep EEG dataset was analyzed, and the results showed that the Poincaré plot NDE can effectively reflect different sleep stages. The obtained results for the two classes of time series demonstrate that the Poincaré plot NDE provides a prospective tool for single-channel EEG time series analysis.
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Affiliation(s)
- Xiaobi Chen
- School of Mechanical Engineering, Xi’an Jiaotong University, Xi’an 710049, China
| | - Guanghua Xu
- School of Mechanical Engineering, Xi’an Jiaotong University, Xi’an 710049, China
- State Key Laboratory for Manufacturing Systems Engineering, Xi’an Jiaotong University, Xi’an 710049, China
| | - Chenghang Du
- School of Mechanical Engineering, Xi’an Jiaotong University, Xi’an 710049, China
| | - Sicong Zhang
- School of Mechanical Engineering, Xi’an Jiaotong University, Xi’an 710049, China
| | - Xun Zhang
- School of Mechanical Engineering, Xi’an Jiaotong University, Xi’an 710049, China
| | - Zhicheng Teng
- School of Mechanical Engineering, Xi’an Jiaotong University, Xi’an 710049, China
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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 2021; 36:609-621. [PMID: 34714495 DOI: 10.1007/s10877-021-00771-4] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2021] [Accepted: 10/13/2021] [Indexed: 11/29/2022]
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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Affiliation(s)
- Teiji Sawa
- Department of Anesthesiology, Kyoto Prefectural University of Medicine, Kyoto, Japan.
| | - Tomomi Yamada
- Department of Anesthesiology, Kyoto Prefectural University of Medicine, Kyoto, Japan
| | - Yurie Obata
- Department of Anesthesia, Yodogawa Christian Hospital, Osaka, Japan
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