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Electroencephalogram (EEG) monitoring of the effect of anesthetic drugs on the central nervous system has long been used in anesthesia research. Several methods based on nonlinear dynamics, such as permutation entropy (PE), have been proposed to analyze EEG series during anesthesia. However, these measures are still single-scale based and may not completely(More)
The purpose of the present paper is to develop a method, based on equal-time correlation, correlation matrix analysis and surrogate resampling, that is able to quantify and describe properties of synchronization of population neuronal activity recorded simultaneously from multiple sites. Initially, Lorenz-type oscillators were used to model multiple time(More)
BACKGROUND Approximate entropy (AE) has been proposed as a measure of anesthetic drug effect in electroencephalographic data. Recently, a new method called permutation entropy (PE) based on symbolic dynamics was also proposed to measure the complexity in an electroencephalographic series. In this study, the AE and PE were applied to electroencephalographic(More)
This paper describes the use of a computational tool based on the Morlet wavelet transform to investigate the interaction dynamics between oscillations generated by two anatomically distinct neuronal populations. The tool uses cross wavelet transform, coherence, bi-spectrum/bi-coherence and phase synchronization. Using specimen data recorded from the(More)
OBJECTIVE Ordinal patterns analysis such as permutation entropy of the EEG series has been found to usefully track brain dynamics and has been applied to detect changes in the dynamics of EEG data. In order to further investigate hidden nonlinear dynamical characteristics in EEG data for differentiating brain states, this paper proposes a novel(More)
The exiting covariance matching method is not suited for real-time applications due to its demand for exhaustive search. Aiming at this problem, we developed a novel approach based on fuzzy genetic algorithm (GA) to boost the computing efficiency of covariance matching. The approach employs GA in searching for optimal solution in a large image region. To(More)
To further understand functional connectivity in the brain, we need to identify the coupling direction between neuronal signals recorded from different brain areas. In this paper, we present a novel methodology based on permutation analysis and conditional mutual information for estimation of a directionality index between two neuronal populations. First,(More)
Numerous effective anticancer drugs have been developed from botanical sources, and there remains a significant untapped resource in herbal medicines. In this study, we evaluated the chemical composition of extracts from American ginseng after steaming, the antiproliferative effects of the ginsenosides in the extracts on SW-480 human colorectal cancer(More)
BACKGROUND Leiomyoma is a relatively common submucosal tumor in the upper-GI tract. The efficacy of a new method for resection of these tumors, endoscopic band ligation, was evaluated. METHODS The study included 59 patients with 64 small upper-GI leiomyomas arising in the muscularis propria as determined by endoscopy, EUS, and EUS-guided FNA. The(More)
MicroRNAs (miRNAs) are a class of small (19–25 nucleotides) noncoding RNAs that regulate the expressions of a wide variety of genes, including some involved in cancer development. Some recent studies show that DNA methylation contributes to down-regulation of microRNA-375 (miR-375) during tumorigenesis. Whether or not down-regulation of miR-375 also exists(More)