一种ECG信号肌电干扰去除方法的研究

实测心电(ECG)信号通常被多种因素干扰,尤其是肌电干扰的去除存在较大困难。本文提出一种结合经验模态分解法(EMD)与主成分分析(PCA)的消噪算法来去除ECG信号的肌电干扰。解决了通常采用小波算法和EMD等方法会导致ECG信号产生振荡和丢失有用信息的难题。本研究利用PCA对含噪信号经EMD分解后的内蕴模态函数(IMF)进行去噪处理,通过对MIT-BIH心电数据进行仿真,以及定性分析了信噪比(SNR)和均方误差(MSE)。结果表明,ECG信号中的肌电干扰被有效去除,所提方法的消噪效果整体上优于小波去噪算法和EMD消噪算法,是一种有效的消噪方法。     

一种面向智慧协同网络的自适配路由策略研究

现有互联网网络体系和机制相对"静态"和"僵化",缺乏支持智慧网络的有效机制.要从根本上解决现有互联网存在的严重弊端,必须创建新的网络理论体系.本文在智慧协同网络"三层"、"两域"体系结构下,针对"网络组件层"的路由自适配问题展开研究,提出基于生物启发的转发网络族群自适配路由策略,实现族群内路由组件之间的智慧协调、动态重构和优化决策,有效解决现有路由策略的静态、僵化等问题.通过数学分析证明:如果设定模型参数μ∈(0,1),提出的自适配路由策略可以始终保持稳定性.最后,通过原型系统验证了提出自适配路由策略是切实可行的,能够提高网络的承载业务数量和提升用户体验.     

基于排序熵算法的麻醉深度检测系统设计

为辅助医生在手术过程中对患者麻醉深度状态的判断,设计了一种基于排序熵算法的麻醉深度检测系统。该系统主要包含3个部分,前端脑电预处理电路、排序熵算法数据处理电路和人机交互软件。前端电路主要包括各类放大电路和滤波电路;排序熵算法数据处理是基于stm32单片机完成;人机交互软件主要用于对测试结果进行记录显示等工作,辅助医生对患者麻醉状况的判断。依据以上方案制作了检测系统,并在实验室环境下进行了测试,能完成相关数据检测。     

“空间多指标生物分析仪器开发与应用”重大专项启动

国家重大科学仪器设备开发专项"空间多指标生物分析仪器开发与应用"项目启动及工作推进会不久前在北京召开。工业和信息化部科技司、科技部条财司、北京理工大学、     

一种提高频谱编码成像技术信噪比的方法EI北大核心CSCD

频谱编码成像技术是一种采用衍射光栅把不同波长照明到样品的不同位置处的新型反射式显微成像技术。搭建了一个基于50 k Hz扫频光源的频谱编码显微系统,为解决无后置放大器情况下探测微弱样品光的问题,采用平衡探测的方法进行了成像测试。通过对USAF-1951分辨率板成像测得横向分辨率由13.93μm提高到5.52μm,采用平衡探测的方法使得洋葱样品图像信噪比(SNR)由15.07 d B提高到22.6 d B。研究结果表明,采用平衡探测的方法能够提高图像分辨率和信噪比。对离体猪胃小凹样品进行成像,验证了频谱编码成像技术在生物消化道内成像的可行性,为下一步该方法实现临床应用奠定了理论基础。     

“超快激光加工”专题前言北大核心CSCD

1960年,梅曼博士研制出红宝石激光器。经过60年的发展,激光在社会生活的各个领域发挥着重要的作用,成为日常生活、工业生产、应用研究中的重要工具。激光加工技术是指通过激光与物质的相互作用,改变材料的物态、成分、组织、应力等性质的技术,具有应用对象广泛、非接触加工、尺寸控制能力强、无污染等特点。典型的激光加工技术包括激光去除加工、激光焊接、激光表面改性、激光3D打印、激光复合加工等。目前,激光加工技术已应用于国防、航天、信息、生物医疗等领域。     

光生物节律因子计算模型的研究

通过实验测量额头温度拟合光生物节律因子-额温差曲线,对现有评价光生物效应作用大小的BioEq模型和CLA模型的计算结果进行分析比较.利用不同图片LCD屏幕的光谱计算节律因子,发现两种模型对于绿色波段的光抑制较强,而对红色波段的光则与实际不相符.通过对九种不同颜色的LED光源测试额头温度,比较模型计算结果与实际光生物效应的作用,发现光生物节律因子与生理体征变化线性递增,其中对BioEq模型的直线拟合相关度达到0.95.     

《化学通报》2014年总目次

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面向脑机接口的犹他神经电极技术

A human brain is composed of a large number of interconnected neurons forming a neural network. To study the functional mechanism of the neural network, it is necessary to record the activity of individual neurons over a large area simultaneously. Brain-computer interface (BCI) refers to the connection established between the human/animal brain and computers/other electronic devices, which enables direct interaction between the brain and external devices. It plays an important role in understanding, protecting, and simulating the brain, especially in helping patients with neurological disorders to restore their impaired motor and sensory functions. Neural electrodes are electrophysiological devices that form the core of BCI, which convert neuronal electrical signals (carried by ions) into general electrical signals (carried by electrons). They can record or interfere with the state of neural activity. The Utah Electrode Array (UEA) designed by the University of Utah is a mainstream neural electrode fabricated by bulk micromachining. Its unique three-dimensional needle-like structure enables each electrode to obtain high spatiotemporal resolution and good insulation between each other. After implantation, the tip of each electrode affects only a small group of neurons around it even allowing to record the action potential of a single neuron. The availability of a large number of electrodes, high quality of signals, and long service life has made UEA the first choice for collecting neuronal signals. Moreover, UEA is the only implantable neural electrode that can record signals in the human cerebral cortex. This article mainly serves as an introduction to the construction, manufacturing process, and functioning of UEA, with a focus on the research progress in fabricating high-density electrode arrays, wireless neural interfaces, and optrode arrays using silicon, glass, and metal as that material of construction. We also discuss the surface modification techniques that can be used to reduce the electrode impedance, minimize the rejection by brain tissue, and improve the corrosion resistance of the electrode. In addition, we summarize the clinical applications where patients can control external devices and get sensory feedback by implanting UEA. Furthermore, we discuss the challenges faced by existing electrodes such as the difficulty in increasing electrode density, poor response of integrated wireless neural interface, and the problems of biocompatibility. To achieve stability and durability of the electrode, advancements in both material science and manufacturing technology are required. We hope that this review can broaden the scope of ideas for the development of UEA. The realization of a fully implantable neural microsystem can contribute to an improved understanding of the functional mechanisms of the neural network and treatment of neurological diseases.