Epilepsy classification using optimized artificial neural network

ABSTRACT

Objectives: An Electroencephalogram (EEG) is the result of co-operative actions performed by brain cells. In other words, it can be defined as the time course of extracellular field potentials that are generated due to the synchronous action of cells. It is widely used for the analysis and diagnosis of several conditions. But this clinical data use to be multi-dimensional, context-dependent, complex, and it causes a concoction of various computing related research challenges. The objective of this study was to develop a computer-aided diagnosis system for epilepsy detection using EEG signals to ease the diagnosis process.

Materials: In this study, EEG datasets for epilepsy disease detection were taken from a public domain (Bonn University, Germany). These EEG recordings contain 100 single-channel EEG signals with maximum duration of 23.6 seconds. This data set was recorded intra-cranially and extra-cranially with the help of a 128-channel amplifier system using a common reference point.

Results: For a unique set of EEG signal features, the Optimized Artificial Neural Network model for classification and validation was developed with optimum neurons in the hidden layer. Results were tested on the basis of accuracy, sensitivity, precision, and specificity for all classes. The proposed Particle Swarm Optimized Artificial Neural Network provided 99.3% accuracy for EEG signal classification.

Discussion: Our results indicate that artificial neural network has efficiency to provide higher accuracy for epilepsy detection if the statistical features are extracted carefully. It is also possible to improve results for real time diagnosis by using optimization technique for error reduction.

Abbreviations: EEG: Electroencephalogram CAD: Computer-Aided Diagnosis ANN: Artificial Neural Network PSO: Particle Swarm Optimization FIR: Finite Impulse Response IIR: Infinite Impulse Response MSE: Mean Square Error.     

杜仲不同提取物对帕金森病小鼠的治疗作用及谱效关系研究

目的探究杜仲不同提取物对帕金森病小鼠的治疗作用及其超高效液相色谱(UPLC)分析与治疗帕金森病的谱效关系。方法通过小鼠爬杆实验、脑内纹状体多巴胺(DA)含量,观察杜仲不同梯度的乙醇提取物对帕金森病小鼠的治疗作用;采用超高效液相色谱-四级杆飞行时间质谱(UPLC-Q-TOF/MS)对杜仲不同提取物进行分析;结合爬杆实验及DA水平结果,采用偏最小二乘法回归(PLSR)分析建立其谱效关系,明确杜仲治疗帕金森病的药效成分。结果杜仲50%、75%乙醇提取物均能明显缩短小鼠爬杆时间;杜仲75%乙醇提取物能显著增加小鼠脑内纹状体DA水平;PLSR分析结果显示,杜仲中杜仲醇苷、鹅掌楸苷、5-羟甲基糠醛、咖啡酸与爬杆实验结果和纹状体多巴胺含量密切相关。结论杜仲乙醇提取物具有抗帕金森病作用,其中75%乙醇提取物效果最显著;杜仲醇苷、鹅掌楸苷、5-羟甲基糠醛、咖啡酸可能是杜仲治疗帕金森病的主要有效成分。     

基于化学计量学方法结合正交偏最小二乘判别分析的陈皮饮片HPLC指纹图谱研究

目的建立陈皮饮片HPLC指纹图谱分析方法,为其质量控制提供技术参考。方法采用HPLC法建立17批陈皮饮片的指纹图谱,通过聚类分析(clusteranalysis,CA)、主成分分析(principalcomponentanalysis,PCA)、正交偏最小二乘判别分析(orthogonal partial least square discrimina te analysis,OPLS-DA)对指纹图谱进行评价。结果建立了陈皮饮片指纹图谱,相似度均0.9,确定了25个共有峰,其中14个峰的变量投影重要性(variableimportance projection,VIP)均1,通过与对照品谱图比对,确定了13号峰为芸香柚皮苷、14号峰为橙皮苷、23号峰为川陈皮素、24号峰为3,5,6,7,8,3′,4′-七甲氧基黄酮、25号峰为桔皮素。结论该方法简单可靠,可用于陈皮饮片鉴别和质量控制。     

Gating of the HypoPP-1 mutations: I. Mutant-specific effects and cooperativity

Hypokalemic periodic paralysis type 1 (HypoPP-1) is a hereditary muscular disorder caused by point mutations in the gene encoding the voltage-gated Ca²⁺ channel α subunit (Ca_v1.1). Despite extensive research, the results on HypoPP-1 mutations are minor and controversial, as it is difficult to analyse Ca²⁺ channel activation macroscopically due to an existence of two open states. In this study, we heterologously expressed the wild-type and HypoPP-1 mutations introduced into the rabbit cardiac Ca²⁺ channel (R650H, R1362H, R1362G) in HEK-293 cells. To examine the cooperative effects of the mutations on channel gating, we expressed two double mutants (R650H/R1362H, R650H/R1362G). We performed whole-cell patch-clamp and, to obtain more information, applied a global fitting procedure whereby several current traces elicited by different potentials were simultaneously fit to the kinetic model containing four closed, two open and two inactivated states. We found that all HypoPP-1 mutations have “loss-of-function” features: D4/S4 mutations shift the equilibrium to the closed states, which results in reduced open probability, shorter openings and, therefore, in smaller currents, and the D2/S4 mutant slows the activation. In addition, HypoPP-1 histidine mutants favored the second open state O₂ with a possibly lower channel selectivity. Cooperativity between the D2/S4 and D4/S4 HypoPP-1 mutations manifested in dominant effects of the D4/S4 mutations on kinetics of the double mutants, suggesting different roles of D2/S4 and D4/S4 voltage sensors in the gating of voltage-gated calcium channels. Alexey Kuzmenkin and Chao Hang contributed equally to this work.     

基于液相色谱-质谱技术的乳腺癌转移相关 代谢标志物的筛选

摘要：目的 应用代谢组学技术筛选与乳腺癌转移相关的代谢标志物。方法 收集100例乳腺癌患者和50例 健康志愿者的血清标本,采用高效液相色谱-轨道离子阱质谱联用（HPLC-LTQ Orbitrap XL MS）代谢组学研究平台分 析乳腺癌未转移患者、乳腺癌转移患者和健康人群血清标本的代谢轮廓,并通过模式识别方法结合非参数检验对数 据进行分析。结果 由乳腺癌未转移组、乳腺癌转移组和健康对照组的代谢轮廓构建的正交偏最小二乘判别分析 （OPLS-DA）模型具有很好的判别能力（R2=95.2%,Q2=86.7%）,可以鉴别出用于区分乳腺癌转移与否的8个代谢标志 物,包括溶血磷脂酸［18∶1（9Z）/0∶0］、溶血磷脂酰胆碱（18∶0）、溶血磷脂酰胆碱［20∶3（5Z,8Z,11Z）］、胆碱、磷酸二羟 丙酮（18∶0e）、2R,3S-番石榴酸、芥酸、L-氢化乳清酸。结论 利用代谢组学方法获得的血清代谢轮廓可以用来构建 区分模型和寻找乳腺癌转移相关的代谢标志物,为乳腺癌的早期诊治、预后评估和药物治疗靶点的选择提供支持和 依据。     

Virtual implantations to transition from porcine to bovine animal models for a total artificial heart

Realheart total artificial heart (TAH) is a novel, pulsatile, four-chamber total artificial heart which had been successfully tested acutely in a porcine animal model. However, the bovine model is better suited for long-term testing and thus an evaluation of how the design would fit the bovine anatomy was required. Virtual implantation is a method that enables a computer simulated implantation based on anatomical 3D-models created from computer tomography images. This method is used clinically, but not yet adopted for animal studies. Herein, we evaluated its suitability in the redesign of the outer dimensions and vessel connections of Realheart TAH to transition from the porcine to the bovine animal model. Virtual implantations in combination with bovine cadaver studies enabled a series of successful acute bovine implantations. Virtual implantations are a useful tool to replace the use of animals in early device development and refine subsequent necessary in vivo experiments. The next steps are to carry out human virtual implantations and cadaver studies to ensure the design is optimized for all stages of testing as well as the final recipient.