EEG artifacts reduction by multivariate empirical mode decomposition and multiscale entropy for monitoring depth of anaesthesia during surgery

Electroencephalography (EEG) has been widely utilized to measure the depth of anaesthesia (DOA) during operation. However, the EEG signals are usually contaminated by artifacts which have a consequence on the measured DOA accuracy. In this study, an effective and useful filtering algorithm based on multivariate empirical mode decomposition and multiscale entropy (MSE) is proposed to measure DOA. Mean entropy of MSE is used as an index to find artifacts-free intrinsic mode functions. The effect of different levels of artifacts on the performances of the proposed filtering is analysed using simulated data. Furthermore, 21 patients' EEG signals are collected and analysed using sample entropy to calculate the complexity for monitoring DOA. The correlation coefficients of entropy and bispectral index (BIS) results show 0.14 ± 0.30 and 0.63 ± 0.09 before and after filtering, respectively. Artificial neural network (ANN) model is used for range mapping in order to correlate the measurements with BIS. The ANN method results show strong correlation coefficient (0.75 ± 0.08). The results in this paper verify that entropy values and BIS have a strong correlation for the purpose of DOA monitoring and the proposed filtering method can effectively filter artifacts from EEG signals. The proposed method performs better than the commonly used wavelet denoising method. This study provides a fully adaptive and automated filter for EEG to measure DOA more accuracy and thus reduce risk related to maintenance of anaesthetic agents.This research was financially supported by the Center for Dynamical Biomarkers and Translational Medicine, National Central University, Taiwan, which is sponsored by Ministry of Science and Technology (Grant Number: NSC102-2911-I-008-001). Also, it was supported by Chung-Shan Institute of Science and Technology in Taiwan (Grant Numbers: CSIST-095-V301 and CSIST-095-V302) and National Natural Science Foundation of China (Grant Number: 51475342)

Epilepsy detection using detrended fluctuation analysis

Epilepsy is a disorder of the central nervous system characterized by the loss of consciousness and convulsions. If some early warning signal of an upcoming seizure (diagnosis of preictal period) could be detected, proper treatment could be applied to the patient to help prevent the seizure. In this articles, detrended fluctuation analysis (DFA) has been introduced and used to extract the DFA feature from EEG signal. DFA is a scaling analysis method that provides a simple quantitative parameter to represent the correlation properties of a signal, we come to 100% separation of Normal, Preictal, and Ictal states of the brai

Senile hair graying: H2O2-mediated oxidative stress affects human hair color by blunting methionine sulfoxide repair

Senile graying of human hair has been the subject of intense research since ancient times. Reactive oxygen species have been implicated in hair follicle melanocyte apoptosis and DNA damage. Here we show for the first time by FT-Raman spectroscopy in vivo that human gray/white scalp hair shafts accumulate hydrogen peroxide (H(2)O(2)) in millimolar concentrations. Moreover, we demonstrate almost absent catalase and methionine sulfoxide reductase A and B protein expression via immunofluorescence and Western blot in association with a functional loss of methionine sulfoxide (Met-S=O) repair in the entire gray hair follicle. Accordingly, Met-S=O formation of Met residues, including Met 374 in the active site of tyrosinase, the key enzyme in melanogenesis, limits enzyme functionality, as evidenced by FT-Raman spectroscopy, computer simulation, and enzyme kinetics, which leads to gradual loss of hair color. Notably, under in vitro conditions, Met oxidation can be prevented by L-methionine. In summary, our data feed the long-voiced, but insufficiently proven, concept of H(2)O(2)-induced oxidative damage in the entire human hair follicle, inclusive of the hair shaft, as a key element in senile hair graying, which does not exclusively affect follicle melanocytes. This new insight could open new strategies for intervention and reversal of the hair graying process