共查询到20条相似文献,搜索用时 9 毫秒
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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. 相似文献
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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。 相似文献
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Doron Kornreich Yaniv Benbenisti H.B. Mitchell Paul Schaefer 《Signal Processing: Image Communication》1997,10(4):269-278
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. 相似文献
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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. 相似文献
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Xiao R. Chandrasekar V. Hongping Liu 《Geoscience and Remote Sensing, IEEE Transactions on》1998,36(3):716-724
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 相似文献
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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. 相似文献
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Massively parallel classification of single-trial EEG signals using a min-max modular neural network 总被引:1,自引:0,他引:1
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. 相似文献
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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 相似文献
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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。 相似文献
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Detection of leukocytes in contact with the vessel wall from in vivo microscope recordings using a neural network 总被引:1,自引:0,他引:1
Egmont-Petersen M Schreiner U Tromp SC Lehmann TM Slaaf DW Arts T 《IEEE transactions on bio-medical engineering》2000,47(7):941-951
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. 相似文献
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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. 相似文献
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On the tracking of rapid dynamic changes in seizure EEG 总被引:2,自引:0,他引:2
Gath I. Feuerstein C. Pham D.T. Rondouin G. 《IEEE transactions on bio-medical engineering》1992,39(9):952-958
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 相似文献
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Rongrui Xiao Chandrasekar V. 《Geoscience and Remote Sensing, IEEE Transactions on》1997,35(1):160-171
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 相似文献
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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. 相似文献
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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… 相似文献