Detection of characteristic waves of sleep EEG by neural network analysis

In psychiatry, the sleep stage is one of the most important evidence for diagnosing mental disease. However, doctors require much labor and skill for diagnosis, so a quantitative and objective method is required for more accurate diagnosis since it depends on the doctor's experience. For this reason, an automatic diagnosis system must be developed. In this paper, we propose a new type of neural network (NN) model referred to as a sleep electroencephalogram (EEG) recognition neural network (SRNN) which enables us to detect several kinds of important characteristic waves in sleep EEG which are necessary for diagnosing sleep stages. Experimental results indicate that the proposed NN model was much more capable than other conventional methods for detecting characteristic waves.     

EEG信号中癫痫棘波的小波变换检测

基于小波变换良好的时频局部化特性,研究了一种利用连续小波变换提取脑电信号中的癫痫棘波的方法,实验结果表明这种方法能够方便而有效地对脑电信号中的癫痫棘波进行检测。     

Normalization schemes in a neural network image compression algorithm

Recently multilayer neural networks have been used for still picture compression. In these networks it is necessary to normalize the gray levels in the input picture before they are fed into the neural network. In this paper we investigate six different normalization functions, of which four are new and appear for the first time in this paper. We show that the compression efficiency of a neural network depends on the normalization function used and that the new normalization functions consistently outperform the traditional normalization functions.     

基于人工鱼群算法优化神经网络在网络入侵检测中的应用研究

为解决传统入侵检测算法存在的检测正确率低、高误播率和检测效率低的问题,结合BP神经网络算法在网络入侵检测中的优点,提出一种采用人工鱼群算法优化BP神经网络算法的方法。通过仿真实验表明,采用优化的神经网络对入侵数据进行学习和检测,与传统网络入侵检测算法相比,具有较高的检测准确率和效率,可以很好地检测各种网络入侵类型,大大提高了网络的安全性能。     

基于遗传算法优化BP神经网络的预测建模

为了进一步提高基于BP神经网络的预测模型精度,本文针对BP神经网络收敛速度慢,参数选择随机等特点,采用了遗传算法对BP神经网络进行优化,并提出了一种基于遗传算法优化BP神经网络的预测模型,从而进一步提高预测模型的预测精度,通过对比未使用遗传算法优化的BP神经网络的预测模型发现基于遗传算法优化BP神经网络的预测模型在提升预测精度方面具有非常好的效果,是一种非常高效的方法.     

Development of a neural network based algorithm for radar snowfallestimation

Using radar to measure snowfall accumulation has been a research topic in radar meteorology for decades. Traditionally, a parametric reflectivity-snowfall (Z-S) relationship is used to estimate ground snowfall amounts based on radar observations. However, the accuracy and reliability of Z-S relationship are limited by the wide variability of the Z-S relationship with snowfall type. In this paper, the authors introduce a neural network based approach to address the problem of snowfall estimation from radar by taking into account the vertical structure of precipitation. The motivation for using a multilayer feedforward neural network (MFNN), such as the radial-basis function (RBF) network, is the good universal function approximation capability of the network. The network is trained using vertical reflectivity profiles averaged over a 9-km² area as the input and ground snowfall amounts as the target output. Separate data, which are not part of the training data, are used to test the generalization performance of the RBF network after the training is done. Radar reflectivity data collected by the CSU-CHILL multiparameter radar and ground snowfall measurements recorded by snowgages located at the Stapleton International Airport (SIA), Stapleton, CO, and the Denver International Airport (DIA), Denver, CO, during the Winter and Icing and Storms Projects (WISP94) were used for this study. The snowfall estimates from the RBF network are shown to be better than those obtained from conventional Z-S algorithms. The neural network based approach provides an alternate method to the snowfall estimation problem     

群智能算法优化神经网络在网络安全的应用研究

网络安全是当前网络管理领域研究中的重点,针对BP神经网络的阈值和连接权值优化问题,提出一种群智能算法优化BP神经网络参数的方法,并将其应用于网络安全.首先对群智能算法中的生物地理学算法进行改进,加快其收敛速度,然后采用改进生物地理学算法择BP神经网络的阈值和连接权值,最后采用网络入侵数据集对其有效性和优越性进行测试.结果表明,生物地理学算法可以快速找到BP神经网络的最优阈值和连接权值,提高了网络入侵检测的正确率,可以有效的保护网络系统的安全.     

群智能算法优化神经网络在网络安全的应用

研究了一种群智能优化神经网络算法的网络流量检测模型。使用QAPSO算法对RBF神经网络的基函数中心、基函数的宽度以及输出层与隐含层的连接权值进行优化。通过实例对该文研究的检测模型进行分析,使用采集的数据对网络流量识别系统进行训练和性能测试。将该文的研究方法和基于常规PSO算法、基于HPSO算法进行对比,结果表明,该文研究的检测方法具有更快的识别速度以及更好的识别准确率,避免了出现陷入局部最优解的情况发生。     

Massively parallel classification of single-trial EEG signals using a min-max modular neural network

This paper presents a method for classifying single-trial electroencephalogram (EEG) signals using min-max modular neural networks implemented in a massively parallel way. The method has three main steps. First, a large-scale, complex EEG classification problem is simply divided into a reasonable number of two-class subproblems, as small as needed. Second, the two-class subproblems are simply learned by individual smaller network modules in parallel. Finally, all the individual trained network modules are integrated into a hierarchical, parallel, and modular classifier according to two module combination laws. To demonstrate the effectiveness of the method, we perform simulations on fifteen different four-class EEG classification tasks, each of which consists of 1491 training and 636 test data. These EEG classification tasks were created using a set of non-averaged, single-trial hippocampal EEG signals recorded from rats; the features of the EEG signals are extracted using wavelet transform techniques. The experimental results indicate that the proposed method has several attractive features. 1) The method is appreciably faster than the existing approach that is based on conventional multilayer perceptrons. 2) Complete learning of complex EEG classification problems can be easily realized, and better generalization performance can be achieved. 3) The method scales up to large-scale, complex EEG classification problems.     

A patient-specific algorithm for the detection of seizure onset inlong-term EEG monitoring: possible use as a warning device

During long-term electroencephalogram (EEG) monitoring of epileptic patients, a seizure warning system would allow patients and observers to take appropriate precautions. It would also allow observers to interact with patients early during the seizure, thus revealing clinically useful information. We designed patient-specific classifiers to detect seizure onsets. After a seizure and some nonseizure data are recorded in a patient, they are used to train a classifier. In subsequent monitoring sessions, EEG patterns have to pass this classifier to determine if a seizure onset occurs. If it does, an alarm is triggered. Extreme care has been taken to ensure a low false-alarm rate, since a high false-alarm rate would render the system ineffective. Features were extracted from the time and frequency domains and a modified nearest-neighbor (NN) classifier was used. The system reached an onset detection rate of 100% with an average delay of 9.35 s after onset. The average false-alarm rate was only 0.02/h. The method was evaluated in 12 patients with a total of 47 seizures. Results indicate that the system is effective and reasonably reliable. Computation load has been kept to a minimum so that real-time processing is possible     

应用于非均匀性校正的改进的神经网络算法

非制冷红外焦平面的非均匀性对红外系统的图像质量造成严重影响。神经网络的自适应调节性优于传统的定标校正方法,成为研究热点。但是传统的神经网络存在期望值不准确、误差函数精度不高和学习速度不适应网络变化的缺点。本文将目标像元与其4邻近像元的像素值进行比较,按偏差值的大小进行排序,再增加权系数来计算期望值;文章又分析了神经网络出现的局部极小问题,在原有的误差函数基础上引入了隐层饱和度的计算式;并提出了根据总误差值之比来调节学习速度。经仿真实验表明,新算法较好地降低了非均匀度。     

Detection of leukocytes in contact with the vessel wall from in vivo microscope recordings using a neural network

Leukocytes play an important role in the host defense as they may travel from the blood stream into the tissue in reacting to inflammatory stimuli. The leukocyte-vessel wall interactions are studied in post capillary vessels by intravital video microscopy during in vivo animal experiments. Sequences of video images are obtained and digitized with a frame grabber. A method for automatic detection and characterization of leukocytes in the video images is developed. Individual leukocytes are detected using a neural network that is trained with synthetic leukocyte images generated using a novel stochastic model. This model makes it feasible to generate images of leukocytes with different shapes and sizes under various lighting conditions. Experiments indicate that neural networks trained with the synthetic leukocyte images perform better than networks trained with images of manually detected leukocytes. The best performing neural network trained with synthetic leukocyte images resulted in an 18% larger area under the ROC curve than the best performing neural network trained with manually detected leukocytes.     

基于遗传算法的BP神经网络在图像压缩中的应用

以神经网络和遗传算法为代表的进化算法都基于智能信息处理的理论,但是各自都存在一些缺陷.设计并实现了基于遗传算法的BP神经网络算法BP-GA,该算法将遗传算法和BP算法相结合,用基于实数编码的遗传算法优化神经网络的权值后,应用于图像压缩.实验证明,利用此混合神经网络进行图像压缩,压缩比高,图像恢复质量效果好.     

On the tracking of rapid dynamic changes in seizure EEG

Estimation of autospectra and coherence and phase spectra of the seizure electroencephalograph (EEG), using the fast Fourier transform (FFT) technique, will cause smearing of the rapid dynamic changes which occur during the seizure. This is inherent to FFT spectral estimation, due to the averaging process which is necessary in order to get consistent spectral estimates. A different approach suggested in the present study is to carry out multivariate autoregressive modeling of the multichannel seizure EEG, combined with adaptive segmentation. In order to obtain good estimates in cases of short record length, the vectorial autoregressive (AR) modeling was based on residual energy ratios. The method has been tested on multichannel seizure EEG recordings from rats with focal epilepsy, caused by intracerebral administration of Kainic acid, and in-depth EEG recordings in patients with temporal lobe epilepsy     

Development of a neural network based algorithm for rainfallestimation from radar observations

Rainfall estimation based on radar measurements has been an important topic in radar meteorology for more than four decades. This research problem has been addressed using two approaches, namely a) parametric estimates using reflectivity-rainfall relation (Z-R relation) or equations using multiparameter radar measurements such as reflectivity, differential reflectivity, and specific propagation phase, and b) relations obtained by matching probability distribution functions of radar based estimates and ground observations of rainfall. In this paper the authors introduce a neural network based approach to address this problem by taking into account the three-dimensional (3D) structure of precipitation. A three-layer perceptron neural network is developed for rainfall estimation from radar measurements. The neural network is trained using the radar measurements as the input and the ground raingage measurements as the target output. The neural network based estimates are evaluated using data collected during the Convection and Precipitation Electrification (CaPE) experiment conducted over central Florida in 1991. The results of the evaluation show that the neural network can be successfully applied to obtain rainfall estimates on the ground based on radar observations. The rainfall estimates obtained from neural network are shown to be better than those obtained from several existing techniques. The neural network based rainfall estimate offers an alternate approach to the rainfall estimation problem, and it can be implemented easily in operational weather radar systems     

Automated epileptic seizure waveform detection method based on the feature of the mean slope of wavelet coefficient counts using a hidden Markov model and EEG signals

Long‐term electroencephalography (EEG) monitoring is time‐consuming, and requires experts to interpret EEG signals to detect seizures in patients. In this paper, we propose a novel automated method called adaptive slope of wavelet coefficient counts over various thresholds (ASCOT) to classify patient episodes as seizure waveforms. ASCOT involves extracting the feature matrix by calculating the mean slope of wavelet coefficient counts over various thresholds in each frequency subband. We validated our method using our own database and a public database to avoid overtuning. The experimental results show that the proposed method achieved a reliable and promising accuracy in both our own database (98.93%) and the public database (99.78%). Finally, we evaluated the performance of the method considering various window sizes. In conclusion, the proposed method achieved a reliable seizure detection performance with a short‐term window size. Therefore, our method can be utilized to interpret long‐term EEG results and detect momentary seizure waveforms in diagnostic systems.     

基于贝叶斯正则化算法BP神经网络钒电池SOC预测

电池荷电状态(SOC)用于表征电池的剩余电量,是全钒液流电池的一个重要参数。在此介绍常用的钒电池SOC预测方法,并对比其优缺点。基于电池SOC的非线性特征,提出采用BP神经网络预测钒电池的SOC,并采用L-M优化算法以及贝叶斯正则化算法对网络进行优化。使用贝叶斯正则化改进的神经网络在对项目中全钒液流电池测试过程实时预测SOC。实验结果表明,采用贝叶斯正则化算法改进的神经网络能够提高SOC的实时预测精度,具有很好的实用前景。     

改进的神经网络算法在垃圾邮件过滤中的应用

针对垃圾邮件威胁信息安全而又屡禁不止的现状,如何从技术上增加对垃圾邮件的控制,维护网络安全成为一个研究的热点问题。人工神经网络具有自适应的特点,在处理变化多端的垃圾邮件问题上有显著优势,但传统算法存在效率低下的问题。现结合模糊理论遗传算法提出了一种改进的BP神经网络算法,在一定程度上提高了算法的效率。通过对中文邮件分类的实验分析,结果表明,本算法的效率优于传统算法,并有较高的识别准确率。     

Detection the Character Wave in Epileptic EEG by Wavelet

Electro encephalography (EEG) is an important clinical tool in theoretical study, diagnosis and treatment of several neurological disorders such as epilepsy and sleeping disorders. For the most part, human epilepsy is the intrinsic brain pathology. Its major manifestation in the epileptic seizure, which may involve a discrete part of the brain partial or the whole cerebral mass generalized. Ictal EEG is characterized by repetitive high-amplitude activity, either fast spikes, slow waves, or s…     

遗传算法优化神经网络在图像目标识别中的应用研究

针对传统的目标识别方法存在易陷入局部最佳值和识别精度低的问题。提出基于遗传算法优化神经网络的图像目标识别方法,通过灰度共生矩阵运算出图像的纹理特征值,并融合像素灰度值构成分类图像的特征矢量,将特征矢量输入到神经网络中实施训练。神经网络先采用遗传算法获取最佳检索范围,再通过高阶神经网络实施寻优运算,获取最佳的图像目标识别结果。实验结果说明,所提方法在图像目标识别精度和效率方面具有较高的优越性。