共查询到20条相似文献,搜索用时 15 毫秒
1.
Recurrent neural network based prediction of epileptic seizures in intra- and extracranial EEG 总被引:14,自引:0,他引:14
Arthur Reference to Petrosian Danil Reference to Prokhorov Richard Reference to Homan Richard Reference to Dasheiff Donald Wunsch Reference to II 《Neurocomputing》2000,30(1-4):201-218
Predicting the onset of epileptic seizure is an important and difficult biomedical problem, which has attracted substantial attention of the intelligent computing community over the past two decades. We apply recurrent neural networks (RNN) combined with signal wavelet decomposition to the problem. We input raw EEG and its wavelet-decomposed subbands into RNN training/testing, as opposed to specific signal features extracted from EEG. To the best of our knowledge this approach has never been attempted before. The data used included both scalp and intracranial EEG recordings obtained from two epileptic patients. We demonstrate that the existence of a “preictal” stage (immediately preceding seizure) of some minutes duration is quite feasible. 相似文献
2.
A. Jamali E. Khaleghi I. Gholaminezhad N. Nariman-zadeh 《International journal of systems science》2016,47(7):1675-1688
In this paper, a new multi-objective genetic programming (GP) with a diversity preserving mechanism and a real number alteration operator is presented and successfully used for Pareto optimal modelling of some complex non-linear systems using some input–output data. In this study, two different input–output data-sets of a non-linear mathematical model and of an explosive cutting process are considered separately in three-objective optimisation processes. The pertinent conflicting objective functions that have been considered for such Pareto optimisations are namely, training error (TE), prediction error (PE), and the length of tree (complexity of the network) (TL) of the GP models. Such three-objective optimisation implementations leads to some non-dominated choices of GP-type models for both cases representing the trade-offs among those objective functions. Therefore, optimal Pareto fronts of such GP models exhibit the trade-off among the corresponding conflicting objectives and, thus, provide different non-dominated optimal choices of GP-type models. Moreover, the results show that no significant optimality in TE and PE may occur when the TL of the corresponding GP model exceeds some values. 相似文献
3.
Man Leung Wong 《Genetic Programming and Evolvable Machines》2006,7(1):127-127
The publisher apologizes for an error that occurred in the above mentioned article. The error appears in the printed version,
as well as in the html and pdf version online. Man Leung Wong is the sole author of this article. His affiliation is listed
below.
The online version of the original article can be found at 相似文献
4.
Abdul Majid Choong-Hwan Lee Muhammad Tariq Mahmood Tae-Sun Choi 《Knowledge and Information Systems》2012,31(3):505-526
Sampling-based methods have previously been proposed for the problem of finding interesting associations in data, even for low-support items. While these methods do not guarantee precise results, they can be vastly more efficient than approaches that rely on exact counting. However, for many similarity measures no such methods have been known. In this paper, we show how a wide variety of measures can be supported by a simple biased sampling method. The method also extends to find high-confidence association rules. We demonstrate theoretically that our method is superior to exact methods when the threshold for “interesting similarity/confidence” is above the average pairwise similarity/confidence, and the average support is not too low. Our method is particularly advantageous when transactions contain many items. We confirm in experiments on standard association mining benchmarks that we obtain a significant speedup on real data sets. Reductions in computation time of over an order of magnitude, and significant savings in space, are observed. 相似文献
5.
6.
计算机对人类情绪与情感的识别研究已经成为了脑机接口领域的研究热点。通过分析人类在生活中的各种情感状态,提取脑电信号的特征并对情感状态进行识别、分类是情感智能化领域的重要方向。针对基于音乐视频诱导的情感数据集DEAP进行了研究,提取脑电信号的频域特征后,提出了采用加速近邻梯度算法(APG)和正交匹配算法(OMP)求解稀疏编码的稀疏表示分类模型进行情感分类,并与支持向量机算法(SVM)做效果比较。实验结果表明,APG算法通过L1范数正则近似求解以其快速的收敛速度在情感数据集上有着较好的分类表现,而OMP算法与SVM算法的分类效果相差无几,实现了情感脑电信号的分类。 相似文献
7.
VALENCE is an interactive visualisation controlled by live brainwave monitoring. We used a wireless EEG headset to monitor the player's alpha waves (an indicator of relaxation) and valence (an indicator of emotion or arousal). The game world is an emergent system of attractive and repulsive forces responding to EEG input. 相似文献
8.
In this paper, a novel solving method for speech signal chaotic time series prediction model was proposed. A phase space was reconstructed based on speech signal's chaotic characteristics and the genetic programming (GP) algorithm was introduced for solving the speech chaotic time series prediction models on the phase space with the embedding dimension m and time delay τ. And then, the speech signal's chaotic time series models were built. By standardized processing of these models and optimizing parameters, a speech signal's coding model of chaotic time series with certain generalization ability was obtained. At last, the experimental results showed that the proposed method can get the speech signal chaotic time series prediction models much more effectively, and had a better coding accuracy than linear predictive coding (LPC) algorithms and neural network model. 相似文献
9.
Improving workers’ safety and health is one of the most critical issues in the construction industry. Research attempts have been made to better identify construction hazards on a jobsite by analyzing workers’ physical responses (e.g., stride and balance) or physiological responses (e.g., brain waves and heart rate) collected from the wearable devices. Among them, electroencephalogram (EEG) holds unique potential since it reveals abnormal patterns immediately when a hazard is perceived and recognized. Unfortunately, the unproven capacity of EEG signals for multi-hazard classification is a primary barrier towards ubiquitous hazard identification in real-time on jobsites. This study correlates EEG signal patterns with construction hazard types and develops an EEG classifier based on the experiments conducted in an immersive virtual reality (VR) environment. Hazards of different types (e.g., fall and slip/trip) were simulated in a VR environment. EEG signals were collected from subjects who wore both wearable EEG and VR devices during the experimentation. Two types of EEG features (time-domain/frequency-domain features and cognitive features) were extracted for training and testing. A total of eighteen advanced machine learning algorithms were used to develop the EEG classifier. The initial results showed that the LightGBM classifier achieved 70.1% accuracy based on the cognitive feature set for the 7-class classification. To improve the performance, the input data was relabeled, and three strategies were designed and tested. As a result, the combined approach (two-step ensemble classification) achieved 82.3% accuracy. As such, this study not only demonstrates the feasibility of coupling wearable EEG, VR, and machine learning to differentiate jobsite hazards but also provides strategies to improve multi-class classification performance. The research results support ubiquitous hazard identification and thereby contribute to the safety of the construction workplace. 相似文献
10.
Automatic mineral identification using evolutionary computation technology is discussed. Thin sections of mineral samples
are photographed digitally using a computer-controlled rotating polarizer stage on a petrographic microscope. A suite of image
processing functions is applied to the images. Filtered image data for identified mineral grains is then selected for use
as training data for a genetic programming system, which automatically synthesizes computer programs that identify these grains.
The evolved programs use a decision-tree structure that compares the mineral image values with one other, resulting in a thresholding
analysis of the multi-dimensional colour and textural space of the mineral images.
Received: 18 October 1999 / Accepted: 20 January 2001 相似文献
11.
12.
Epilepsy is one of the most common neurological disorders characterized by transient and unexpected electrical disturbance of the brain. The electroencephalogram (EEG) is an invaluable measurement for the purpose of assessing brain activities, containing information relating to the different physiological states of the brain. It is a very effective tool for understanding the complex dynamical behavior of the brain. This paper presents the application of empirical mode decomposition (EMD) for analysis of EEG signals. The EMD decomposes a EEG signal into a finite set of bandlimited signals termed intrinsic mode functions (IMFs). The Hilbert transformation of IMFs provides analytic signal representation of IMFs. The area measured from the trace of the analytic IMFs, which have circular form in the complex plane, has been used as a feature in order to discriminate normal EEG signals from the epileptic seizure EEG signals. It has been shown that the area measure of the IMFs has given good discrimination performance. Simulation results illustrate the effectiveness of the proposed method. 相似文献
13.
This paper proposed a new method for detecting islanding of distributed generation (DG), using Multi-gene Genetic Programming (MGP). Islanding has been a serious concern among power distribution utilities and distributed generation owners, because it poses risks to the safety of utilities’ workers and consumers, and can cause damage to power distribution systems’ equipment. Therefore, a DG must be disconnected as soon as an islanding is detected. In addition, an islanding detection method must have high degree of dependability to correctly discriminate islanding from other events, such as load switching, in order to avoid unnecessary disconnection of the distributed generator. In this context, the novelty of the proposed method is that the MGP is capable of obtaining a set of mathematical and logic functions employed to detect and classify islanding correctly. This is a new approach among the computational intelligent methods proposed for DG islanding detection. The main idea was to use local voltage measurements as input of the method, eliminating the need of complex and expensive communication infrastructure. The method has been trained with several islanding and non-islanding cases, by using a power distribution system comprising five concentrated loads, a synchronous distributed generator and a wind power plant. The results showed that the proposed method was successful in differentiating the islanding events from other disturbances, revealing its great potential to be applied in anti-islanding protection schemes for distributed generation. 相似文献
14.
15.
Alcoholism affects the structure and functioning of brain. Electroencephalogram (EEG) signals can depict the state of brain. The EEG signals are ensemble of various neuronal activity recorded from different scalp regions having different characteristics and very low magnitude in microvolts. These factors make human interpretation difficult and time consuming to analyze these signals. Moreover, these highly varying EEG signals are susceptible to inter/intra variability errors. So, a Computer-Aided Diagnosis (CAD) can be used to identify the alcoholic and normal subjects accurately. However, these EEG signals exhibit nonlinear and non-stationary properties. Therefore, it needs much effort in deciphering the diagnostic evidence from them using linear time and frequency-domain methods. The nonlinear parameters together with time-frequency/scale domain methods can help to detect tiny changes in these signals. The correntropy is nonlinear indicator which characterizes the dynamic behavior of EEG signals in time-scale domain. In this paper, we present a new way for diagnosis of alcoholism using Tunable-Q Wavelet Transform (TQWT) based features derived from EEG signals. The feature extraction is performed using TQWT based decomposition and extracted Centered Correntropy (CC) from the forth decomposed detail sub-band. The Principal Component Analysis (PCA) is used for feature reduction followed by Least Squares-Support Vector Machine (LS-SVM) for classifying normal and alcoholic EEG signals. In order to make sure reliable classification performance, 10-fold cross-validation scheme is adopted. Our proposed system is able to diagnose the alcoholic and normal EEG signals, with an average accuracy of 97.02%, sensitivity of 96.53%, specificity of 97.50% and Matthews correlation coefficient of 0.9494 for Q-factor (Q) varying between 3 and 8 using Radial Basis Function (RBF) kernel function. Also, we have established a novel Alcoholism Risk Index (ARI) using three clinically significant features to discriminate the given classes by means of a single number. This system can be used for automated diagnosis and monitoring of alcoholic subjects to evaluate the effect of treatment. 相似文献
16.
Carl P. Schmertmann 《Computational Economics》1996,9(4):275-298
This paper discusses economic applications of a recently developed artificial intelligence technique-Koza's genetic programming (GP). GP is an evolutionary search method related to genetic algorithms. In GP, populations of potential solutions consist of executable computer algorithms, rather than coded strings. The paper provides an overview of how GP works, and illustrates with two applications: solving for the policy function in a simple optimal growth model, and estimating an unusual regression function. Results suggest that the GP search method can be an interesting and effective tool for economists. 相似文献
17.
Oltean M 《Evolutionary computation》2005,13(3):387-410
A new model for evolving Evolutionary Algorithms is proposed in this paper. The model is based on the Linear Genetic Programming (LGP) technique. Every LGP chromosome encodes an EA which is used for solving a particular problem. Several Evolutionary Algorithms for function optimization, the Traveling Salesman Problem and the Quadratic Assignment Problem are evolved by using the considered model. Numerical experiments show that the evolved Evolutionary Algorithms perform similarly and sometimes even better than standard approaches for several well-known benchmarking problems. 相似文献
18.
基于小波包技术的EEG信号特征波提取分析 总被引:1,自引:0,他引:1
为了更有效地提取脑电信号特征波,结合小波包技术,提出了一种脑电特征波提取方法。首先对脑电信号进行小波包分解,然后进行相关频段信号的重构,从而提取出特征波,并对其进行功率谱分析和能量计算。实验结果表明,小波包技术能有效地提取脑电信号特征波。 相似文献
19.
James Alfred Walker Katharina Völk Stephen L. Smith Julian Francis Miller 《Genetic Programming and Evolvable Machines》2009,10(4):417-445
Parallel and distributed methods for evolutionary algorithms have concentrated on maintaining multiple populations of genotypes,
where each genotype in a population encodes a potential solution to the problem. In this paper, we investigate the parallelisation
of the genotype itself into a collection of independent chromosomes which can be evaluated in parallel. We call this multi-chromosomal evolution
(MCE). We test this approach using Cartesian Genetic Programming and apply MCE to a series of digital circuit design problems
to compare the efficacy of MCE with a conventional single chromosome approach (SCE). MCE can be readily used for many digital
circuits because they have multiple outputs. In MCE, an independent chromosome is assigned to each output. When we compare
MCE with SCE we find that MCE allows us to evolve solutions much faster. In addition, in some cases we were able to evolve
solutions with MCE that we unable to with SCE. In a case-study, we investigate how MCE can be applied to to a single objective
problem in the domain of image classification, namely, the classification of breast X-rays for cancer. To apply MCE to this
problem, we identify regions of interest (RoI) from the mammograms, divide the RoI into a collection of sub-images and use
a chromosome to classify each sub-image. This problem allows us to evaluate various evolutionary mutation operators which
can pairwise swap chromosomes either randomly or topographically or reuse chromosomes in place of other chromosomes. 相似文献
20.
运动想象M I是基于想象的脑机交互BCI中常用的任务,但M I不易习得和控制,且存在"BCI盲"现象,使得该类BCI的实用化受限.针对较易习得和控制的视觉想象VI任务进行识别,旨在构建基于VI的BCI(VI-BCI).招募了15名被试者参加2种动态图像的视觉想象任务并采集脑电EEG数据;然后采用EEG微状态方法研究了这... 相似文献