Investigating Power Density and the Degree of Nonlinearity in Intrinsic Components of Anesthesia EEG by the Hilbert-Huang Transform: An Example Using Ketamine and Alfentanil

Abstract

Empirical mode decomposition (EMD) is an adaptive filter bank for processing nonlinear and non-stationary signals, such as electroencephalographic (EEG) signals. EMD works well to decompose a time series into a set of intrinsic mode functions with specific frequency bands. An IMF therefore represents an intrinsic component on its correspondingly intrinsic frequency band. The word of ‘intrinsic’ means the frequency is totally adaptive to the nature of a signal. In this study, power density and nonlinearity are two critical parameters for characterizing the amplitude and frequency modulations in IMFs. In this study, a nonlinearity level is quantified using degree of waveform distortion (DWD), which represents the characteristic of waveform distortion as an assessment of the intra-wave modulation of an IMF. In the application of anesthesia EEG analysis, the assessments of power density and DWD for a set of IMFs represent dynamic responses in EEG caused by two different anesthesia agents, Ketamine and Alfentanil, on different frequency bands. Ketamine causes the increase of power density and the decrease of nonlinearity on γ-band neuronal oscillation, which cannot be found EEG responses of group B using Alfentanil. Both agents cause an increase of power density and a decrease of nonlinearity on β-band neuronal oscillation accompany with a loss of consciousness. Moreover, anesthesia agents cause the decreases of power density and nonlinearity (i.e. DWD) for the low-frequency IMFs.

A Space-Time Study of Hemorrhagic Fever with Renal Syndrome (HFRS) and Its Climatic Associations in Heilongjiang Province, China

“…Understanding the association between HFRS incidence and climate change (using MEI as a proxy, measuring coupled oceanic-atmospheric character of ENSO event) provides a potential auxiliary way to assess the public health effects of global climate change, since climate variability has important effects on wildlife population dynamics [64,65]. The Hilbert-Huang transformation is a powerful tool for solving mode-mixing problems and can be also used as a filter for decomposing raw HFRS incidence series into several independent series with disparate modes, i.e., IMFs [66]. Different component series were obtained that describe various inherent disease characteristics that cannot be detected in the raw series.…”

Analysis in the frequency domain of multicomponent oscillatory modes of the human electroencephalogram extracted with multivariate empirical mode decomposition

“…These findings are the evidence of one of the limitations of the empirical mode decomposition: the so called mode-mixing, consisting in the appearance of disparate scales in an IMF, or when a signal with a similar scale appears in different IMF components. This fact has been reported and multiple procedures have been proposed to mitigate its effect (Munoz-Gutierrez et al 2018;Soler et al 2020;Tsai et al 2016;Zheng, Xu 2019).…”

“…Some authors have recently suggested that the first IMFs decomposed from the EEG using the multivariate empirical mode decomposition could be associated to classical EEG bands (Noshadi et al 2014;Schiecke et al 2019;Soler et al 2020;Tsai et al 2016). The IMF1 would correspond to the gamma band, the IMF2 to the beta, the IMF3 to the alpha, the IMF4 to the theta, and the IMF5 and IMF6 would be sub-bands of the delta band (Tsai et al 2016).…”

“…In this study we used the APIT-MEMD algorithm for the EEG decomposition, with an alpha parameter of 0.35 and the number of directions was of 128. For calculations in the frequency domain of EEG signals were selected the first 6 extracted IMFs, that contain the spectral range from 1 to 70 Hz as shown by other authors, and our own previous experience (Amo et al 2017;Carella et al 2018;Chen et al 2016;Chen et al 2017;Schiecke et al 2015;Schiecke et al 2019;Tsai et al 2016;Zheng, Xu 2019;Zhuang et al 2017;Estevez-Baez et al 2017a, c). The implementation of the MEMD and the APIT-MEMD algorithms used in this study was based on software resources publicly available at the web site http://www.commsp.ee.ic.ac.uk/~mandic/research/emd.htm.…”

Comparison between traditional FFT and marginal spectra using the Hilbert-Huang transform method for the broadband spectral analysis of the EEG in healthy humans

“…The implementation of the MEMD and the APIT-MEMD algorithms used in this study was based on software resources publicly available at the web site http://www.commsp.ee.ic.ac.uk/~mandic/research/emd.htm. For calculations in the frequency domain of EEG signals were selected the first 6 extracted IMFs, that contain the spectral range from 1 to 70 Hz as shown by other authors (Chen et al 2016;Chen et al 2017;Schiecke et al 2019;Amo et al 2017;Carella et al 2018;Zheng, Xu 2019;Zhuang et al 2017;Tsai et al 2016).…”