Yinghua Wang

Adaptive Neuro-Fuzzy Inference System for Classification of Background EEG Signals from ESES Patients and Controls

Background electroencephalography (EEG), recorded with scalp electrodes, in children with electrical status epilepticus during slow-wave sleep (ESES) syndrome and control subjects has been analyzed. We considered 10 ESES patients, all right-handed and aged 3–9 years. The 10 control individuals had the same characteristics of the ESES ones but presented a normal EEG. Recordings were undertaken in the awake and relaxed states with their eyes open. The complexity of background EEG was evaluated using the permutation entropy (PE) and sample entropy (SampEn) in combination with the ANOVA test. It can be seen that the entropy measures of EEG are significantly different between the ESES patients and normal control subjects. Then, a classification framework based on entropy measures and adaptive neuro-fuzzy inference system (ANFIS) classifier is proposed to distinguish ESES and normal EEG signals. The results are promising and a classification accuracy of about 89% is achieved.

Multiscale rescaled range analysis of EEG recordings in sevoflurane anesthesia

OBJECTIVE The Hurst exponent (HE) is a nonlinear method measuring the smoothness of a fractal time series. In this study we applied the HE index, extracted from electroencephalographic (EEG) recordings, as a measure of anesthetic drug effects on brain activity. METHODS In 19 adult patients undergoing sevoflurane general anesthesia, we calculated the HE of the raw EEG; comparing the maximal overlap discrete wavelet transform (MODWT) with the traditional rescaled range (R/S) analysis techniques, and with a commercial index of depth of anesthesia - the response entropy (RE). We analyzed each wavelet-decomposed sub-band as well as the combined low frequency bands (HEOLFBs). The methods were compared in regard to pharmacokinetic/pharmacodynamic (PK/PD) modeling, and prediction probability. RESULTS All the low frequency band HE indices decreased when anesthesia deepened. However the HEOLFB was the best index because: it was less sensitive to artifacts, most closely tracked the exact point of loss of consciousness, showed a better prediction probability in separating the awake and unconscious states, and tracked sevoflurane concentration better - as estimated by the PK/PD models. CONCLUSIONS The HE is a useful measure for estimating the depth of anesthesia. It was noted that HEOLFB showed the best performance for tracking drug effect. SIGNIFICANCE The HEOLFB could be used as an index for accurately estimating the effect of anesthesia on brain activity.

Detection of Burst Suppression Patterns in EEG Using Recurrence Rate

Burst suppression is a unique electroencephalogram (EEG) pattern commonly seen in cases of severely reduced brain activity such as overdose of general anesthesia. It is important to detect burst suppression reliably during the administration of anesthetic or sedative agents, especially for cerebral-protective treatments in various neurosurgical diseases. This study investigates recurrent plot (RP) analysis for the detection of the burst suppression pattern (BSP) in EEG. The RP analysis is applied to EEG data containing BSPs collected from 14 patients. Firstly we obtain the best selection of parameters for RP analysis. Then, the recurrence rate (RR), determinism (DET), and entropy (ENTR) are calculated. Then RR was selected as the best BSP index one-way analysis of variance (ANOVA) and multiple comparison tests. Finally, the performance of RR analysis is compared with spectral analysis, bispectral analysis, approximate entropy, and the nonlinear energy operator (NLEO). ANOVA and multiple comparison tests showed that the RR could detect BSP and that it was superior to other measures with the highest sensitivity of suppression detection (96.49%, P = 0.03). Tracking BSP patterns is essential for clinical monitoring in critically ill and anesthetized patients. The purposed RR may provide an effective burst suppression detector for developing new patient monitoring systems.

Permutation auto-mutual information of electroencephalogram in anesthesia.

OBJECTIVE The dynamic change of brain activity in anesthesia is an interesting topic for clinical doctors and drug designers. To explore the dynamical features of brain activity in anesthesia, a permutation auto-mutual information (PAMI) method is proposed to measure the information coupling of electroencephalogram (EEG) time series obtained in anesthesia. APPROACH The PAMI is developed and applied on EEG data collected from 19 patients under sevoflurane anesthesia. The results are compared with the traditional auto-mutual information (AMI), SynchFastSlow (SFS, derived from the BIS index), permutation entropy (PE), composite PE (CPE), response entropy (RE) and state entropy (SE). Performance of all indices is assessed by pharmacokinetic/pharmacodynamic (PK/PD) modeling and prediction probability. MAIN RESULTS The PK/PD modeling and prediction probability analysis show that the PAMI index correlates closely with the anesthetic effect. The coefficient of determination R(2) between PAMI values and the sevoflurane effect site concentrations, and the prediction probability Pk are higher in comparison with other indices. The information coupling in EEG series can be applied to indicate the effect of the anesthetic drug sevoflurane on the brain activity as well as other indices. The PAMI of the EEG signals is suggested as a new index to track drug concentration change. SIGNIFICANCE The PAMI is a useful index for analyzing the EEG dynamics during general anesthesia.

Tracking the coupling of two electroencephalogram series in the isoflurane and remifentanil anesthesia.

OBJECTIVE Coupling in multiple electroencephalogram (EEG) signals provides a perspective tool to understand the mechanism of brain communication. In this study, we propose a method based on permutation cross-mutual information (PCMI) to investigate whether or not the coupling between EEG series can be used to quantify the effect of specific anesthetic drugs (isoflurane and remifentanil) on brain activities. METHODS A Rössler-Lorenz system and surrogate analysis was first employed to compare histogram-based mutual information (HMI) and PCMI for estimating the coupling of two nonlinear systems. Then, the HMI and the PCMI indices of EEG recordings from two sides of the forehead of 12 patients undergoing combined remifentanil and isoflurane anesthesia were demonstrated for tracking the effect of drug on the coupling of brain activities. Performance of all indices was assessed by the correlation coefficients (Rij) and relative coefficient of variation (CV). RESULTS The PCMI can track the coupling strength of two nonlinear systems, and it is sensitive to the phase change of the coupling systems. Compared to the HMI, the PCMI has a better correlation with the coupling strength in nonlinear systems. The PCMI could track the effect of anesthesia and distinguish the consciousness state from the unconsciousness state. Moreover, at the embedding dimension m=4 and lag τ=1, the PCMI had a better performance than HMI in tracking the effect of anesthesia drugs on brain activities. CONCLUSIONS As a measure of coupling, the PCMI was able to reflect the state of consciousness from two EEG recordings. SIGNIFICANCE The PCMI is a promising new coupling measure for estimating the effect of isoflurane and remifentanil anesthetic drugs on the brain activity.

Global Synchronization of Multichannel EEG in Patients With Electrical Status Epilepticus in Sleep

In the research field of epilepsy, it is a challenge to understand the transition of brain activity to electrical status epilepticus in sleep (ESES). In this study, an S-estimator method is proposed to describe the course of global synchronization in multichannel electroencephalograph (EEG) from awake to sleep in 11 patients with ESES. The study confirms that there is a significant increase in spikes and global synchronization from awake to sleep. It is also found that global synchronization is strongly correlated with spikes. The proposed method has the potential of revealing the intrinsic features of EEG signals and the underlying brain dynamics in ESES.

A Robust Coherence-Based Brain Connectivity Method with an Application to EEG Recordings

Electroencephalography (EEG) is important for epileptic loci identification. Recently, brain connection of EEG has been proved to be an efficient approach to identify epileptic loci. Magnitude square coherence (MSC) is one of the traditional methods to calculate the coherence index. In this study, we propose a robust wavelet spectrum coherence (CWSC) algorithm to calculate the brain connection from EEG recordings, and apply it for epileptic loci identification. The coherence index during ictal and inter-ictal stage for one patient with temporal lobe epilepsy was used to demonstrate the performance of the proposed method. This case study showed that the proposed method had better anti-noise capability. In comparison with the MSC approach, it could be more suitable for localization of epileptic loci with noisy EEG signals.