Comparison of FFT and marginal spectra of EEG using empirical mode decomposition to monitor anesthesia

BACKGROUND AND OBJECTIVE Intraoperative awareness refers that patients can recall aspects of their surgery after being put under general anesthesia. This distressing complication causes affected patients to be conscious and probably feel pain, leading to emotional trauma or other sequelae. Monitoring and administrating the depth of anesthesia is necessary to prevent patients from awareness during a medical operation. In this paper, we analyzed the electroencephalograms (EEGs) of patients to characterize their anesthesia. The data set, "awareness" and "anesthesia" groups, each contained 558 samples, including patients who had undergone different types of surgeries. METHODS EEG signals acquired from patients in an aware state or under anesthesia were decomposed into a set of intrinsic mode functions (IMFs) through empirical mode decomposition (EMD). Fast Fourier transform (FFT) and Hilbert transform (HT) analyses were then performed on each IMF to determine the frequency spectra. The probability distributions of expected values of frequencies were generated for the same IMF in the two groups of patients. The corresponding statistical data, including analysis of variance tests, were also calculated. A receiver operating characteristic curve was used to identify optimal frequency value to discriminate between the two states of consciousness. RESULTS The frequencies of the IMFs for aware patients were found to be higher than those for anesthetized patients. The optimal frequency threshold by using FFT (or HT) for IMF 1 was 21.08 (or 25.00) Hz. IMF1 performed the highest with respect to the area under the curve (AUC) of 0.993 for FFT (or 0.989 for HT); hence it can be applied as a useful classifier to distinguish between fully anesthetized patients and aware patients. CONCLUSIONS This paper proposes a method for identifying whether patients' state of consciousness during a range of surgery types is "under anesthesia" or "aware." Our method involves using EEG to characterize the depth of anesthesia through two frequency analysis techniques. On the basis of our analyses, we conclude that the performance of IMF1 is satisfactory in distinguishing between patients' states of consciousness during surgery requiring general anesthesia.

[1]  Ram Bilas Pachori,et al.  Analysis of normal and epileptic seizure EEG signals using empirical mode decomposition , 2011, Comput. Methods Programs Biomed..

[2]  Karen B. Domino,et al.  The Incidence of Awareness During Anesthesia: A Multicenter United States Study , 2004, Anesthesia and analgesia.

[3]  K. Otto.  EEG power spectrum analysis for monitoring depth of anaesthesia during experimental surgery , 2008, Laboratory animals.

[4]  Boualem Boashash,et al.  Estimating and interpreting the instantaneous frequency of a signal. I. Fundamentals , 1992, Proc. IEEE.

[5]  J. Devitt,et al.  Recall of intraoperative events after general anaesthesia and cardiopulmonary bypass , 1993, Canadian journal of anaesthesia = Journal canadien d'anesthesie.

[6]  Qin Wei,et al.  Adaptive Computation of Multiscale Entropy and Its Application in EEG Signals for Monitoring Depth of Anesthesia During Surgery , 2012, Entropy.

[7]  R. Nunes,et al.  Bispectral index and other processed parameters of electroencephalogram: an update. , 2012, Revista brasileira de anestesiologia.

[8]  P. Welch.  The use of fast Fourier transform for the estimation of power spectra: A method based on time averaging over short, modified periodograms , 1967 .

[9]  K. Leslie,et al.  For Personal Use. Only Reproduce with Permission from the Lancet , 2022 .

[10]  Leonardo Teixeira Domingues Duarte,et al.  When the bispectral index (bis) can give false results. , 2009, Revista brasileira de anestesiologia.

[11]  C Prys-Roberts,et al.  Anaesthesia: a practical or impractical construct? , 1987, British Journal of Anaesthesia.

[12]  Norden E. Huang,et al.  A review on Hilbert‐Huang transform: Method and its applications to geophysical studies , 2008 .

[13]  M. Kertai,et al.  Brain Monitoring with Electroencephalography and the Electroencephalogram-Derived Bispectral Index During Cardiac Surgery , 2012, Anesthesia and analgesia.

[14]  J. Bruhn,et al.  Depth of anaesthesia monitoring: what's available, what's validated and what's next? , 2006, British journal of anaesthesia.

[15]  Hualou Liang,et al.  Intrinsic mode entropy based on multivariate empirical mode decomposition and its application to neural data analysis , 2011, Cognitive Neurodynamics.

[16]  Peter S. Sebel,et al.  Bispectral Index Monitoring Allows Faster Emergence and Improved Recovery from Propofol, Alfentanil, and Nitrous Oxide Anesthesia , 1997 .

[17]  M. Struys,et al.  Comparison of spontaneous frontal EMG, EEG power spectrum and bispectral index to monitor propofol drug effect and emergence , 1998, Acta anaesthesiologica Scandinavica.

[18]  Chin-Feng Lin,et al.  Hilbert–Huang transformation-based time-frequency analysis methods in biomedical signal applications , 2012, Proceedings of the Institution of Mechanical Engineers. Part H, Journal of engineering in medicine.

[19]  J. Sleigh,et al.  Using the Hilbert–Huang transform to measure the electroencephalographic effect of propofol , 2012, Physiological measurement.

[20]  Qin Wei,et al.  Analysis of EEG via Multivariate Empirical Mode Decomposition for Depth of Anesthesia Based on Sample Entropy , 2013, Entropy.

[21]  J. Sleigh,et al.  Analysis of depth of anesthesia with Hilbert–Huang spectral entropy , 2008, Clinical Neurophysiology.

[22]  Nassib G. Chamoun,et al.  An introduction to bispectral analysis for the electroencephalogram , 1994, Journal of Clinical Monitoring.

[23]  Kyle J. Myers,et al.  Foundations of Image Science , 2003, J. Electronic Imaging.

[24]  Xian Peng,et al.  MD-based EEG signal enhancement for auditory evoked potential ecovery under high stimulus-rate paradigm , 2013 .

[25]  B. Kolk,et al.  Awareness under anesthesia and the development of posttraumatic stress disorder. , 2001, General hospital psychiatry.

[26]  Xiaoli Li,et al.  EEG entropy measures in anesthesia , 2015, Front. Comput. Neurosci..

[27]  Jonathan Blackledge,et al.  Digital Signal Processing: Mathematical and Computational Methods, Software Development and Applications , 2001 .

[28]  Stavros M. Panas,et al.  Hilbert-Huang Spectrum as a new field for the identification of EEG event related de-/synchronization for BCI applications , 2008, 2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[29]  I. Rampil.  A Primer for EEG Signal Processing in Anesthesia , 1998, Anesthesiology.

[30]  C. Rosow,et al.  Bispectral index monitoring , 2001 .

[31]  Bernard C. Picinbono,et al.  On instantaneous amplitude and phase of signals , 1997, IEEE Trans. Signal Process..

[32]  E. Bedrosian.  A Product Theorem for Hilbert Transforms , 1963 .

[33]  Ronald D. Miller.  Miller's Anesthesia , 2005 .

[34]  M. Todd,et al.  Practice Advisory for Intraoperative Awareness and Brain Function Monitoring: A Report by the American Society of Anesthesiologists Task Force on Intraoperative Awareness , 2006, Anesthesiology.

[35]  G N Kenny,et al.  Comparison of bispectral EEG analysis and auditory evoked potentials for monitoring depth of anaesthesia during propofol anaesthesia. , 1999, British journal of anaesthesia.

[36]  N. Huang,et al.  The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis , 1998, Proceedings of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences.

[37]  D. Looney,et al.  Time-Frequency Analysis of EEG Asymmetry Using Bivariate Empirical Mode Decomposition , 2011, IEEE Transactions on Neural Systems and Rehabilitation Engineering.