Complexity of Multi-Channel Electroencephalogram Signal Analysis in Childhood Absence Epilepsy.

Absence epilepsy is an important epileptic syndrome in children. Multiscale entropy (MSE), an entropy-based method to measure dynamic complexity at multiple temporal scales, is helpful to disclose the information of brain connectivity. This study investigated the complexity of electroencephalogram (EEG) signals using MSE in children with absence epilepsy. In this research, EEG signals from 19 channels of the entire brain in 21 children aged 5-12 years with absence epilepsy were analyzed. The EEG signals of pre-ictal (before seizure) and ictal states (during seizure) were analyzed by sample entropy (SamEn) and MSE methods. Variations of complexity index (CI), which was calculated from MSE, from the pre-ictal to the ictal states were also analyzed. The entropy values in the pre-ictal state were significantly higher than those in the ictal state. The MSE revealed more differences in analysis compared to the SamEn. The occurrence of absence seizures decreased the CI in all channels. Changes in CI were also significantly greater in the frontal and central parts of the brain, indicating fronto-central cortical involvement of "cortico-thalamo-cortical network" in the occurrence of generalized spike and wave discharges during absence seizures. Moreover, higher sampling frequency was more sensitive in detecting functional changes in the ictal state. There was significantly higher correlation in ictal states in the same patient in different seizures but there were great differences in CI among different patients, indicating that CI changes were consistent in different absence seizures in the same patient but not from patient to patient. This implies that the brain stays in a homogeneous activation state during the absence seizures. In conclusion, MSE analysis is better than SamEn analysis to analyze complexity of EEG, and CI can be used to investigate the functional brain changes during absence seizures.

Sample entropy analysis of EEG signals via artificial neural networks to model patients' consciousness level based on anesthesiologists experience.

Electroencephalogram (EEG) signals, as it can express the human brain's activities and reflect awareness, have been widely used in many research and medical equipment to build a noninvasive monitoring index to the depth of anesthesia (DOA). Bispectral (BIS) index monitor is one of the famous and important indicators for anesthesiologists primarily using EEG signals when assessing the DOA. In this study, an attempt is made to build a new indicator using EEG signals to provide a more valuable reference to the DOA for clinical researchers. The EEG signals are collected from patients under anesthetic surgery which are filtered using multivariate empirical mode decomposition (MEMD) method and analyzed using sample entropy (SampEn) analysis. The calculated signals from SampEn are utilized to train an artificial neural network (ANN) model through using expert assessment of consciousness level (EACL) which is assessed by experienced anesthesiologists as the target to train, validate, and test the ANN. The results that are achieved using the proposed system are compared to BIS index. The proposed system results show that it is not only having similar characteristic to BIS index but also more close to experienced anesthesiologists which illustrates the consciousness level and reflects the DOA successfully.

Immobilization of Chinese herbal medicine onto the surface-modified calcium hydrogenphosphate.

To accelerate the healing of bone defects or for healing to take place, it is often necessary to fill them with suitable substance. Various artificial materials defects have been developed. Among these, calcium phosphates and bioactive glass have been proven to be biocompatibile and bioactive materials that can chemically bond with bone, and have been successfully used clinically for repair of bone defects and augmentation of osseous tissue. However, those bioceramics have only the property of osteoconduction without any osteoinduction. Many ligands have been physicochemically absorbed onto substrates to enhance cell-substrate interactions. Although widely developed, they are still limited to use in long-term implantation because of their half-life period. Thus, some interfacial modification will be required for enhancing the efficacy of the delivery system. These models involve the immobilization of biologically active ligands of natural and synthetic origin onto various substrates to produce an interface with stronger chemical bond between ligand and substrate. The advantage of covalently immobilizing a ligand is that a chemical bond is present to prevent ligand or medicine from desorption. In our study, a two-step chemical immobilization was performed to surface-modified calcium hydrogenphosphate powders. The first was to modify the surface of calcium hydrogen-phosphate (CHP) with a coupling agent of hexanmethylene diisocyanate (HMDI). CHP surface modified by HMDI is abbreviated as MCHP. The linkage between CHP and HMDI will be characterized by FTIR. The second step was to immobilize chemically Gusuibu onto MCHP. Moreover, the sorption and desorption of Gusuibu was evaluated and quantitatively analyzed by spectrophotometer and HPLC. Bioceramic CHP was surface-modified by a two-step chemical immobilization. First, the surface of calcium hydrogen-phosphate (CHP) was successfully modified with coupling agent of hexanmethylene diisocyanate (HMDI). The first step was also activated the surface of CHP to induce primary amine terminator. The reaction of this functional group with Gusuibu was the second step. We confirmed simultaneously that Gusuibu could be immobilized chemically onto the surface of MCHP. Although some immobilized Gusuibu was also released rapidly at the first 12h, the degree of the released Gusuibu was lower than both by Gusuibu-adsorbing MCHP and Gusuibu-adsorbing CHP.