Topology-based kernels with application to inference problems in Alzheimer's disease

Alzheimer's disease (AD) research has recently witnessed a great deal of activity focused on developing new statistical learning tools for automated inference using imaging data. The workhorse for many of these techniques is the support vector machine (SVM) framework (or more generally kernel-based methods). Most of these require, as a first step, specification of a kernel matrix K between input examples (i.e., images). The inner product between images I(i) and I(j) in a feature space can generally be written in closed form and so it is convenient to treat K as "given." However, in certain neuroimaging applications such an assumption becomes problematic. As an example, it is rather challenging to provide a scalar measure of similarity between two instances of highly attributed data such as cortical thickness measures on cortical surfaces. Note that cortical thickness is known to be discriminative for neurological disorders, so leveraging such information in an inference framework, especially within a multi-modal method, is potentially advantageous. But despite being clinically meaningful, relatively few works have successfully exploited this measure for classification or regression. Motivated by these applications, our paper presents novel techniques to compute similarity matrices for such topologically-based attributed data. Our ideas leverage recent developments to characterize signals (e.g., cortical thickness) motivated by the persistence of their topological features, leading to a scheme for simple constructions of kernel matrices. As a proof of principle, on a dataset of 356 subjects from the Alzheimer's Disease Neuroimaging Initiative study, we report good performance on several statistical inference tasks without any feature selection, dimensionality reduction, or parameter tuning.     

Turn-on phenomena in optically and electrically fired thyristors

An analytical solution of the time-dependent transport equation including surface recombination and optical injection for a diffused thyristor base is presented. By means of the classical two-transistor model, the turn-on transient may be predicted. Apart from the influence of technological parameters like base width, doping profile, and diffusion length, the effect of optical versus electrical injection as well as surface recombination are discussed. Surface recombination may significantly influence the optical turn-on threshold. Despite the rigorous simplicity of the model, the dynamic turn-on behavior of an optically fired high-voltage thyristor is in very good agreement with the experiment. The data, being presented for a wide range of parameters, are useful for practical design.     

Spectral and nonlinear analyses of MEG background activity in patients with Alzheimer's disease.

The aim of the present study is to analyze the magnetoencephalogram (MEG) background activity from patients with Alzheimer's disease (AD) and elderly control subjects. MEG recordings from 20 AD patients and 21 controls were analyzed by means of two spectral [median frequency (MF) and spectral entropy (SpecEn)] and two nonlinear parameters [approximate entropy (ApEn) and Lempel-Ziv complexity (LZC)]. In the AD diagnosis, the highest accuracy of 75.6% (80% sensitivity, 71.4% specificity) was obtained with the MF according to a linear discriminant analysis (LDA) with a leave-one-out cross-validation procedure. Moreover, we wanted to assess whether these spectral and nonlinear analyses could provide complementary information to improve the AD diagnosis. After a forward stepwise LDA with a leave-one-out cross-validation procedure, one spectral (MF) and one nonlinear parameter (ApEn) were automatically selected. In this model, an accuracy of 80.5% (80.0% sensitivity, 81.0% specificity) was achieved. We conclude that spectral and nonlinear analyses from spontaneous MEG activity could be complementary methods to help in AD detection.     

Dopant redistribution and formation of electrically active complexes in SiGe

A review of our recent experimental investigations of dopant diffusion and characterization of electrically active defect complexes in strained and relaxed epitaxial SiGe/Si heterostructures is given. Three topics: (i) Sb and P diffusion, (ii) properties of divacancy and P-vacancy complexes, and (iii) thermal donor generation in SiGe are discussed in detail. High resolution X-ray diffraction (HRXRD), secondary ion mass spectrometry (SIMS), deep-level transient spectroscopy (DLTS), admittance spectroscopy (ADSPEC) and thermally stimulated capacitance (TSCAP) have been employed for the relaxation control, tracer profiling, deep- and shallow-level characterization in our samples. A part of the electrical measurements is performed in MOS Si/SiGe/Si components.     

Thermally and electrically isolated single crystal siliconstructures in CMOS technology

Thermally and electrically isolated single crystal silicon structures have been fabricated using a post-processing anisotropic tetramethyl ammonium hydroxide (TMAH) electrochemical etch. The process was carried out on CMOS circuits fabricated by a commercial foundry. Since the etch consists of a single micromachining step performed on packaged and bonded dice, this technique has the potential for cost-effective prototyping and production of integrated sensors and circuits     

Investigating the neural correlates of pathological cortical networks in Alzheimer's disease using heterogeneous neuronal models

This paper describes an investigation into the pathophysiological causes of abnormal cortical oscillations in Alzheimer's disease (AD) using two heterogeneous neuronal network models. The effect of excitatory circuit disruption on the beta band power (13-30 Hz) using a conductance-based network model of 200 neurons is assessed. Then, the neural correlates of abnormal cortical oscillations in different frequency bands based on a larger network model of 1000 neurons consisting of different types of cortical neurons are also analyzed. EEG studies in AD patients have shown that beta band power (13-30 Hz) decreased in the early stages of the disease with a parallel increase in theta band power (4-7 Hz). This abnormal change progresses with the later stages of the disease but with decreased power spectra in other fast frequency bands plus an increase in delta band power (1-3 Hz). Our results show that, despite the heterogeneity of the network models, the beta band power is significantly affected by excitatory neural and synaptic loss. Second, the results of modeling a functional impairment in the excitatory circuit shows that beta band power exhibits the most decrease compared with other bands. Previous biological experiments on different types of cultural excitatory neurons show that cortical neuronal death is mediated by dysfunctional ionic behavior that might specifically contribute to the pathogenesis of β-amyloid-peptide-induced neuronal death in AD. Our study also shows that beta band power was the first affected component when the modeled excitatory circuit begins to lose neurons and synapses.     

MEG and EEG source localization in beamspace

Beamspace methods are applied to EEG/MEG source localization problems in this paper. Beamspace processing involves passing the data through a linear transformation that reduces the data dimension prior to applying a desired statistical signal processing algorithm. This process generally reduces the data requirements of the subsequent algorithm. We present one approach for designing beamspace transformations that are optimized to preserve source activity located within a given region of interest and show that substantial reductions in dimension are obtained with negligible signal loss. Beamspace versions of maximum likelihood dipole fitting, MUSIC, and minimum variance beamforming source localization algorithms are presented. The performance improvement offered by the beamspace approach with limited data is demonstrated by bootstrapping somatosensory data to evaluate the variability of the source location estimates obtained with each algorithm. The quantitative benefits of beamspace processing depend on the algorithm, signal to noise ratio, and amount of data. Dramatic performance improvements are obtained in scenarios with low signal to noise ratio and a small number of independent data samples.     

Recent progress in the development of electrically and optically pumped dye vapor lasers

This paper reviews new results in the field of organic dye vapor lasers. Gain studies of the scintillator dye POPOP in liquid solution and in the vapor phase show that a dye vapor exhibits optical gain nearly as high as in the solution case under excitation by a N2laser. Superradiant emission has been observed from both, optically excited POPOP vapor and electron-beam excited vapor. The optimum operating conditions of an electron-beam pumped dye vapor-buffer gas system with high purity argon as buffer are reported. Potential energy transfer processes from the rare-gas buffer to the dye vapor are discussed. The metastable ionic species Ar+2has been identified as the most likely POPOP excitation source after intense electron pumping of the dense Ar buffer gas at typically 4-5 atm. An estimate of the conversion efficiency from electronic energy deposited in the Ar buffer to stimulated emission of the dye yields 5 percent. These results suggest that an efficient tunable electron beam pumped dye vapor laser is feasible.     

CSSD＋AAR模型在脑电信号处理中的应用

针对BCI技术中的脑电信号处理方法和事件相关去同步化的特点,提出了一种结合时、频、空域的特征提取方法。结合CSSD和AAR模型来提取脑电特征,并对基于AAR模型系数的特征提取方法进行了探讨,最终选择卡尔曼平滑算法提取模型系数,然后将提取的特征用简单的线性分类器进行分类。实验结果表明测试集的分类正确率达到了94．08％,而且这种特征提取方法有很好的时间分辨率,适合于在线分类。这是一种正确率高,时间分辨率高,适合在线分类的好方法。     

A study on the possible usefulness of detrended fluctuation analysis of the electroencephalogram background activity in Alzheimer's disease

We studied the EEG background activity of Alzheimer's disease (AD) patients with detrended fluctuation analysis (DFA). DFA provides an estimation of the scaling information and long-range correlations in time series. We recorded the EEG in 11 AD patients and 11 age-matched controls. Our results showed two scaling regions in all subjects' channels (for limited time scales from 0.01 to 0.04 s and from 0.08 to 0.43 s, respectively), with a clear bend when their corresponding slopes ($alpha_{1}$ and $alpha_{2}$) were different. No significant differences between groups were found with $alpha_{1}$. However, $alpha_{2}$ values were significantly lower in control subjects at electrodes T5, T6, and O1 ($p$ $≪$ 0.01, Student's $t$-test). These findings suggest that the scaling behavior of the EEG is sensitive to AD. Although $alpha_{2}$ values allowed us to separate AD patients and controls, accuracies were lower than with spectral analysis. However, a forward stepwise linear discriminant analysis with a leave-one-out cross-validation procedure showed that the combined use of DFA and spectral analysis could improve the diagnostic accuracy of each individual technique. Thus, although spectral analysis outperforms DFA, the combined use of both techniques may increase the insight into brain dysfunction in AD.      

PWB方法在矩形开孔电大腔中的应用及其拓展

用功率平衡(PWB)方法快速评估了在平面电磁波照射下矩形开孔电大腔内的平均场值水平,且随着频率的升高,此结果与全波分析软件所得结果的吻合度越来越高。为了得到更完备的腔内场环境描述,采用统计的方法生成不同入射频点下的腔内归一化电场模值概率密度函数(PDF)图,并发现了腔壁电导率的变化对腔内归一化电场模值PDF的影响规律：在各入射频点下,随着腔壁电导率从10 S/m逐渐增大到107 S/m,腔内归一化电场模值PDF曲线越来越趋于平缓并趋于稳定;腔壁电导率为106 S/m和107 S/m时的腔内归一化电场模值PDF非常相似,以入射波频率在10 GHz时为例,利用PWB方法的原理说明了上述现象发生的原因。     

Estimation of force-activation, force-length, and force-velocityproperties in isolated, electrically stimulated muscle

Designing advanced controllers for motor neural prosthesis applications requires appropriate models for electrically stimulated muscle. A nonlinear nonisometric muscle model based on a Hill-type structure is presented. Estimation algorithms were derived to parameterize the passive force-length, the passive force-velocity, the active force-length, and the active force-velocity properties, the isometric recruitment curve, and the linear contraction dynamics of the model. All parameters were based on experimental measurements rather than on values taken from the literature. The estimation methods were validated experimentally using isolated hind-limb muscles in two acute animal model preparations. The results demonstrated that the parameterized model is capable of predicting force output with reasonable accuracy for a wide range of simultaneously varying kinematic and stimulation inputs     

Implementation of compressive sensing in ECG and EEG signal processing

The purpose of this paper is to exploit compressive sensing(CS)method in dealing with electrocardiography(ECG)and electroencephalography(EEG)signals at a high compression ratio. In order to get sparse data of ECG and EEG signals before being compressed, a combined scheme was presented by using wavelet transform and iterative threshold method; then, compressive sensing is executed to make the data compressed. After doing compressive sensing, Bayesian compressive sensing(BCS)is used to reconstruct the original signals. The simulation results show that compressive sensing is an effective method to make data compressed for ECG and EEG signals with high compression ratio and good quality of reconstruction. Furthermore, it shows that the proposed scheme has good denoising effects.     

在线脑机接口中脑电信号的特征提取与分类方法

在脑机接口研究中,针对运动想象脑电信号的特征抽取,提出了一种基于离散小波变换和AR模型的方法.利用Daubechies类小波函数对脑电信号进行3层分解,抽取小波变换系数的统计特征;利用Burg算法提取脑电信号6阶AR模型系数.将这两类特征进行组合后使用神经网络、支持向量机、马氏距离线性判别进行分类并比较分析.采用BCI...     

Excited state polarization,bulk photovoltaic effect and the photorefractive effect in electrically polarized media

Optical absorption in pyroelectric crystals is accompanied by electrical effects (in addition to the well-known pyroelectric effect) which are not present in other materials. Optical excitation between localized states gives rise to an instantaneous macroscopic polarization change due to the change of dipole moment at the absorbing center. Excitation of free carriers from localized states will in general result in a bulk photovoltaic effect due to asymmetric charge transfer. Thus spatially non-uniform illumination gives rise to internal fields, resulting in refractive index variations. Studies of these effects for fast detection, optical logic, memories, and microwave generation will be described.     

小波滤波与AR模型在脑电信号处理的应用

针对脑-计算机接口技术中的脑电信号处理、事件相关同步和事件相关去同步的特点,提出了一种基于离散小波滤波和AR模型来提取脑电信号特征向量的方法。利用Daubechies类小波函数对脑电信号进行4层分解,然后使用Burg算法提取脑电信号8阶AR模型系数,最后用BP神经网络进行分类和比较。得到最优的正确率为71.64%,小波滤波的效果要优于FIR滤波器。     

Array response kernels for EEG and MEG in multilayer ellipsoidal geometry

We present forward modeling solutions in the form of array response kernels for electroencephalography (EEG) and magnetoencephalography (MEG), assuming that a multilayer ellipsoidal geometry approximates the anatomy of the head and a dipole current models the source. The use of an ellipsoidal geometry is useful in cases for which incorporating the anisotropy of the head is important but a better model cannot be defined. The structure of our forward solutions facilitates the analysis of the inverse problem by factoring the lead field into a product of the current dipole source and a kernel containing the information corresponding to the head geometry and location of the source and sensors. This factorization allows the inverse problem to be approached as an explicit function of just the location parameters, which reduces the complexity of the estimation solution search. Our forward solutions have the potential of facilitating the solution of the inverse problem, as they provide algebraic representations suitable for numerical implementation. The applicability of our models is illustrated with numerical examples on real EEG/MEG data of N20 responses. Our results show that the residual data after modeling the N20 response using a dipole for the source and an ellipsoidal geometry for the head is in average lower than the residual remaining when a spherical geometry is used for the same estimated dipole.     

癫痫患者血清细胞因子水平与脑电图的相关性探讨

目的：探究癫痫患者血清细胞因子水平同脑电图之间存在的相关性。方法：将2015年4月~2016年4月期间我院收治的56例癫痫患者作为试验组,再选取同期在我院进行体检的56名健康人作为对照组,全部患者都给予常规脑电图检查。结果：试验组人员的血清肿瘤坏死因子、白细胞介素以及脑电图检测结果等都明显的比对照组高,差异具有统计学意义（P＜0.05）;其中癫痫患者的血清肿瘤坏死因子及白细胞介素之间具有显著的正相关（P＜0.05）。结论：癫痫患者一般具有细胞免疫功能紊乱的症状,免疫细胞CD8、CD4及肿瘤坏死因子（TNF）-α与白细胞介素（IL）-2等均为影响癫痫病情的关键介质。其中脑电图改变同免疫细胞CD8、CD4及TNF-α与IL-2水平等紧密相关,距离癫痫发作期越近,有关免疫细胞及细胞因子异常水平则越高,而同期脑电图异常率则越高。     

不同意识脑电的双谱切片特征分析与分类

脑电是一种典型的非高斯、非线性随机信号,传统时域或频域分析已不能准确表征信号特征,而高阶谱方法对脑电信号的处理却有较好效果。本文通过几组不同意识状态下的脑电测试实验,提取脑电信号,并通过双谱分析提取脑电双谱切片的特征值,借助支持向量机、概率神经网络、最近邻分类算法等3种方法对双谱切片的特征值进行处理,比较其分类效果。研究结果表明,电极C3处脑电与其他电极处的脑电具有不同的双谱特征,不同脑电极信号双谱切片具有明显差异。     

Epileptic seizures detection in EEG using DWT-based ApEn and artificial neural network

There are numerous neurological disorders such as dementia, headache, traumatic brain injuries, stroke, and epilepsy. Out of these epilepsy is the most prevalent neurological disorder in the human after stroke. Electroencephalogram (EEG) contains valuable information related to different physiological state of the brain. A scheme is presented for detecting epileptic seizures from EEG data recorded from normal subjects and epileptic patients. The scheme is based on discrete wavelet transform (DWT) analysis and approximate entropy (ApEn) of EEG signals. Seizure detection is performed in two stages. In the first stage, EEG signals are decomposed by DWT to calculate approximation and detail coefficients. In the second stage, ApEn values of the approximation and detail coefficients are calculated. Significant differences have been found between the ApEn values of the epileptic and the normal EEG allowing us to detect seizures with 100 % classification accuracy using artificial neural network. The analysis results depicted that during seizure activity, EEG had lower ApEn values compared to normal EEG. This gives that epileptic EEG is more predictable or less complex than the normal EEG. In this study, feed-forward back-propagation neural network has been used for classification and training algorithm for this network that updates the weight and bias values according to Levenberg–Marquardt optimization technique.