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

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

On Holo-Hilbert spectral analysis: a full informational spectral representation for nonlinear and non-stationary data

The Holo-Hilbert spectral analysis (HHSA) method is introduced to cure the deficiencies of traditional spectral analysis and to give a full informational representation of nonlinear and non-stationary data. It uses a nested empirical mode decomposition and Hilbert–Huang transform (HHT) approach to identify intrinsic amplitude and frequency modulations often present in nonlinear systems. Comparisons are first made with traditional spectrum analysis, which usually achieved its results through convolutional integral transforms based on additive expansions of an a priori determined basis, mostly under linear and stationary assumptions. Thus, for non-stationary processes, the best one could do historically was to use the time–frequency representations, in which the amplitude (or energy density) variation is still represented in terms of time. For nonlinear processes, the data can have both amplitude and frequency modulations (intra-mode and inter-mode) generated by two different mechanisms: linear additive or nonlinear multiplicative processes. As all existing spectral analysis methods are based on additive expansions, either a priori or adaptive, none of them could possibly represent the multiplicative processes. While the earlier adaptive HHT spectral analysis approach could accommodate the intra-wave nonlinearity quite remarkably, it remained that any inter-wave nonlinear multiplicative mechanisms that include cross-scale coupling and phase-lock modulations were left untreated. To resolve the multiplicative processes issue, additional dimensions in the spectrum result are needed to account for the variations in both the amplitude and frequency modulations simultaneously. HHSA accommodates all the processes: additive and multiplicative, intra-mode and inter-mode, stationary and non-stationary, linear and nonlinear interactions. The Holo prefix in HHSA denotes a multiple dimensional representation with both additive and multiplicative capabilities

Illuminating hydrological processes at the soil-vegetation-atmosphere interface with water stable isotopes

Funded by DFG research project “From Catchments as Organised Systems to Models based on Functional Units” (FOR 1Peer reviewedPublisher PDFPublisher PD

Corrigendum to: The TianQin project: current progress on science and technology

In the originally published version, this manuscript included an error related to indicating the corresponding author within the author list. This has now been corrected online to reflect the fact that author Jun Luo is the corresponding author of the article

Finishing the euchromatic sequence of the human genome

The sequence of the human genome encodes the genetic instructions for human physiology, as well as rich information about human evolution. In 2001, the International Human Genome Sequencing Consortium reported a draft sequence of the euchromatic portion of the human genome. Since then, the international collaboration has worked to convert this draft into a genome sequence with high accuracy and nearly complete coverage. Here, we report the result of this finishing process. The current genome sequence (Build 35) contains 2.85 billion nucleotides interrupted by only 341 gaps. It covers ∼99% of the euchromatic genome and is accurate to an error rate of ∼1 event per 100,000 bases. Many of the remaining euchromatic gaps are associated with segmental duplications and will require focused work with new methods. The near-complete sequence, the first for a vertebrate, greatly improves the precision of biological analyses of the human genome including studies of gene number, birth and death. Notably, the human enome seems to encode only 20,000-25,000 protein-coding genes. The genome sequence reported here should serve as a firm foundation for biomedical research in the decades ahead

Investigation of the Effects of Continuous Low-Dose Epidural Analgesia on the Autonomic Nervous System Using Hilbert Huang Transform

Effects of continuous low-dose epidural bupivacaine (0.05-0. 1%) infusion on the Doppler velocimetry for labor analgesia have been well documented. The aim of this study was to monitor the activity of the autonomic nervous system (ANS) for women in labor based on Hilbert Huang transform ( HHT), which performs signal processing for nonlinear systems, such as human cardiac systems. Thirteen pregnant women were included in the experimental group for labor analgesia. They received continuous epidural bupivacaine 0.075% infusion. The normal-to-normal intervals (NN-interval) were downloaded from an ECG holter. Another 20 pregnant women in non- anesthesia labor (average gestation age was 38.6 weeks) were included in the comparison group. In this study, HHT was used to decompose components of ECG signals, which reflect three different frequency bands of a person 's heart rate spectrum (viz. high frequency (HF), low frequency (LF) and very low frequency (VLF)). It was found that the change of energy in subjects without anesthesia was more active than that with continuous epidural bupivacaine 0.075% infusion. The energy values of the experimental group (i.e., labor analgesia) of HF and LF of ANS activities were significantly lower (P < 0.05) than the values of the comparison group ( viz. labor without analgesia), but the trend of energy ratio of LF /HF was opposite. In conclusion, the sympathetic and parasympathetic components of ANS are all suppressed by continuous low-dose epidural bupivacaine 0.075% infusion, but parasympathetic power is suppressed more than sympathetic power

A Novel Continuous Visual Analog Scale Model Derived from Pain-Relief Demand Index Via Hilbert Huang Transform for Postoperative Pain

In this investigation, we proposed a continuous pain intensity scaling model derived from an innovative parameter of pain-relief demand (PRD) index for acute postoperative patients of gynecology using patient- controlled analgesia ( PCA) via the occurrence frequency of pain-relief demands. To derive PRD, a simplified fluctuation time series of pain feeling was simulated according to the distribution of pain- relief demands using an exponential decay function. Furthermore, we applied the Hilbert Huang transform to obtain the time-amplitude distribution of the first intrinsic mode function (IMF) of fluctuation time series and defined the PRD index using a function of amplitude. A total of 2466 visual analogue scale (VAS) values derived from interviews with 470 postoperative patients at various time points were compared with this PRD index extracted from the PCA records of the same patients. According to the statistical result of one-way analysis of variance (ANOVA, P < 0.001), VAS was significantly linearly dependent on the PRD index. Although the result of this VAS model is not a perfect linear relationship between VAS and PRD index (r = 0 .490), it is significantly better than the result of our previous study using a novel fuzzy pain demand index (FPD). This result shows this approach is promising for further study