Qingfang Meng

University of Jinan (Jinan, China), Chi-nan-shih, Shandong Sheng, China

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Publications (20)17.3 Total impact

  • Fenglin Wang · Qingfang Meng · Hong-Bo Xie · Yuehui Chen
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    ABSTRACT: The extraction method of classification feature is primary and core problem in all epileptic EEG detection algorithms, since it can seriously affect the performance of the detection algorithm. In this paper, a novel epileptic EEG feature extraction method based on the statistical parameter of weighted complex network is proposed. The EEG signal is first transformed into weighted network and the weight differences of all the nodes in the network are analyzed. Then the sum of top quintile weight differences is extracted as the classification feature. At last, the extracted feature is applied to classify the epileptic EEG dataset. Experimental results show that the single feature classification based on the extracted feature obtains higher classification accuracy up to 94.75%, which indicates that the extracted feature can distinguish the ictal EEG from interictal EEG and has great potentiality of real-time epileptic seizures detection.
    No preview · Article · Aug 2014
  • Fenglin Wang · Qingfang Meng · Yuehui Chen
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    ABSTRACT: The study of epilepsy detection has great clinical significance. The focus of this study is feature extraction method, which has significant impacts on the performance of epilepsy detection. Recently, the statistic properties of complex network show ability to describe the dynamics of nonlinear time series. In this paper, a feature extraction method of epileptic EEG, based on statistical properties of weighted complex network, is proposed. The weighted network of epileptic EEG is first constructed and the vertex strength distribution of the converted network is studied. Then the weighted mean value of the vertex strength distribution is defined and extracted as the classification feature. Experimental results indicate that the extracted feature can clearly reflect the difference between ictal EEGs and interictal EEGs and the single feature classification based on extracted feature gets higher classification accuracy up to 95.50%.
    No preview · Conference Paper · Jul 2014
  • Bin Yang · Mingyan Jiang · Yuehui Chen · Qingfang Meng · Ajith Abraham
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    ABSTRACT: Accurate models play important roles in capturing the salient characteristics of the network traffic, analyzing and simulating for the network dynamic, and improving the predictive ability for system dynamics. In this study, the ensemble of the flexible neural tree (FNT) and system models expressed by the ordinary differential equations (ODEs) is proposed to further improve the accuracy of time series forecasting. Firstly, the additive tree model is introduced to represent more precisely ODEs for the network dynamics. Secondly, the structures and parameters of FNT and the additive tree model are optimized based on the Genetic Programming (GP) and the Particle Swarm Optimization algorithm (PSO). Finally, the expected level of performance is verified by using the proposed method, which provides a reliable forecast model for small-time scale network traffic. Experimental results reveal that the proposed method is able to estimate the small-time scale network traffic measurement data with decent accuracy.
    No preview · Article · Dec 2013 · Journal of Computers
  • Yu Wang · Weidong Zhou · Qi Yuan · Xueli Li · Qingfang Meng · Xiuhe Zhao · Jiwen Wang
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    ABSTRACT: The feature analysis of epileptic EEG is very significant in diagnosis of epilepsy. This paper introduces two nonlinear features derived from fractal geometry for epileptic EEG analysis. The features of blanket dimension and fractal intercept are extracted to characterize behavior of EEG activities, and then their discriminatory power for ictal and interictal EEGs are compared by means of statistical methods. It is found that there is significant difference of the blanket dimension and fractal intercept between interictal and ictal EEGs, and the difference of the fractal intercept feature between interictal and ictal EEGs is more noticeable than the blanket dimension feature. Furthermore, these two fractal features at multi-scales are combined with support vector machine (SVM) to achieve accuracies of 97.58% for ictal and interictal EEG classification and 97.13% for normal, ictal and interictal EEG classification.
    No preview · Article · Dec 2013 · International Journal of Neural Systems
  • Shasha Yuan · Weidong Zhou · Qi Yuan · Yanli Zhang · Qingfang Meng
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    ABSTRACT: Approximately 1% of the world's population suffers from epilepsy. An automatic seizure detection system is of great significance in the monitoring and diagnosis of epilepsy. In this paper, a novel method is proposed for automatic seizure detection in intracranial EEG recordings. The EEG recordings are divided into 4-s epochs, and then wavelet decomposition with five scales is performed to the EEG epochs. Detail signals at scales 3, 4, and 5 are selected to form a signal distribution. The diffusion distances are extracted as features, and Bayesian linear discriminant analysis (BLDA) is used as the classifier. A total of 193.75h of intracranial EEG recordings from 21 patients having 87 seizures are employed to evaluate the system, and the average sensitivity of 94.99%, specificity of 98.74%, and false-detection rate of 0.24/h are achieved. The seizure detection system based on diffusion distance yields a high sensitivity as well as a low false-detection rate for long-term EEG recordings.
    No preview · Article · Nov 2013 · Epilepsy & Behavior
  • Qingfang Meng · Fenglin Wang · Weidong Zhou · Shanshan Chen
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    ABSTRACT: The electroencephalogram (EEG) signals with different brain states show different nonlinear dynamics. Recently the statistical properties of complex networks theory have been applied to explore the nonlinear dynamics of time series, which studies the dynamics of time series via its organization. This study combines the complex networks theory with epileptic EEG analysis and applies the statistical properties of complex networks to the automatic epileptic EEG detection. We construct the complex networks from the epileptic EEG series and then calculate the entropy of the degree distribution of the network (NDDE). The NDDE corresponding to the ictal EEG is lower than interictal EEG's. The experiment result shows that the approach using the NDDE as a classification feature obtains robust performance of epileptic seizure detection and the accuracy is up to 95.75%.
    No preview · Conference Paper · Oct 2013
  • Shanshan Chen · Qingfang Meng · Weidong Zhou · Xinghai Yang
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    ABSTRACT: Recurrence Quantification Analysis (RQA) was a nonlinear analysis method and widely used to analyze EEG signals. In this work, a feature extraction method based on the RQA measures was proposed to detect the epileptic EEG from EEG recordings. To combine the time-frequency characteristic of epileptic EEG, variation coefficient and fluctuation index were used to analyze epileptic EEG. The multi-feature combination of RQA and linear parameters had better performance in analyzing the nonlinear dynamic characteristics and time-frequency characteristic of epileptic EEG. For features selection and improving the classification accuracy, a support vector machine (SVM) classifier was used. The experimental results showed that the proposed method could classify the ictal EEG and interictal EEG with accuracy of 97.98%.
    No preview · Conference Paper · Jul 2013
  • Fenglin Wang · Qingfang Meng · Weidong Zhou · Shanshan Chen
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    ABSTRACT: The nonlinear time series analysis method based on complex networks theory gives a novel perspective to understand the dynamics of the nonlinear time series. Considering the electroencephalogram (EEG) signals showing different nonlinear dynamics under different brain states, this study proposes an epileptic EEG analysis approach based on statistical properties of complex networks and applies the approach to epileptic EEGs automatic detection. Firstly, the complex network is constructed from the epileptic EEG signals and the degree distribution (DDF) of the resulting networks is calculated. Then the entropy of the degree distribution (NDDE) is used as a feature to classify the ictal EEGs and the interictal EEGs. The experiment results show that the NDDE of the ictal EEG is lower than interictal EEG's and the classification accuracy, taking the NDDE as a classification feature, is up to 96.25%.
    No preview · Conference Paper · Jul 2013
  • Qingfang Meng · Yuehui Chen · Qiang Zhang · Xinghai Yang
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    ABSTRACT: In the reconstructed phase space, based on the nonlinear time series local prediction method and the relevance vector machine model, the local relevance vector machine prediction method was proposed in this paper, which was applied to predict the small scale traffic measurements data. The experiment results show that the local relevance vector machine prediction method could effectively predict the small scale traffic measurements data, the prediction error mainly concentrated on the vicinity of zero, and the prediction accuracy of the local relevance vector machine regression model was superior to that of the feedforward neural network optimized by PSO.
    No preview · Conference Paper · Jul 2013
  • Qingfang Meng · Shanshan Chen · Weidong Zhou · Xinghai Yang
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    ABSTRACT: Considering the EEG signals are nonlinear and nonstationary, the nonlinear dynamical methods have been widely applied to analyze the EEG signals. Directly extracted the approximate entropy and sample entropy as features are efficient methods to analysis the EEG signals of epileptic parents. To detect the epilepsy seizure signals from epileptic EEG, choose an appropriate threshold value as the discrimination criteria is simplest. The experiment indicated the approximate entropy provide a higher accuracy in distinguishing the epileptic seizure signals from the EEG than sample entropy. To improve the accuracy of sample entropy, empirical mode decomposition (EMD) is used to decompose EEG into multiple frequency subbands, and then calculate sample entropy for each component. The results show that the accuracy is up to 91%, which could be used to discriminate epileptic seizure signals from epileptic EEG.
    No preview · Conference Paper · Jul 2013
  • Source
    Yuehui Chen · Bin Yang · Qingfang Meng · Yaou Zhao · Ajith Abraham
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    ABSTRACT: This paper presents a hybrid evolutionary method for identifying a system of ordinary differential equations (ODEs) to predict the small-time scale traffic measurements data. We used the tree-structure based evolutionary algorithm to evolve the architecture and a particle swarm optimization (PSO) algorithm to fine tune the parameters of the additive tree models for the system of ordinary differential equations. We also illustrate some experimental comparisons with genetic programming, gene expression programming and a feedforward neural network optimized using PSO algorithm. Experimental results reveal that the proposed method is feasible and efficient for forecasting the small-scale traffic measurements data.
    Full-text · Article · Jan 2011 · Information Sciences
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    Yuehui Chen · Bin Yang · Qingfang Meng
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    ABSTRACT: In this paper, the Flexible Neural Tree (FNT) model is employed to predict the small-time scale traffic measurements data. Based on the pre-defined instruction/operator sets, the FNT model can be created and evolved. This framework allows input variables selection, over-layer connections and different activation functions for the various nodes involved. The FNT structure is developed using the Genetic Programming (GP) and the parameters are optimized by the Particle Swarm Optimization algorithm (PSO). The experimental results indicate that the proposed method is efficient for forecasting small-time scale traffic measurements and can reproduce the statistical features of real traffic measurements. We also compare the performance of the FNT model with the feed-forward neural network optimized by PSO for the same problem
    Full-text · Article · Jan 2011 · Applied Soft Computing
  • Qingfang Meng · Weidong Zhou · Yuehui Chen · Jin Zhou
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    ABSTRACT: We propose a feature extraction method based on the Volterra autoregressive model's prediction power and the data's predictability for the EEG signals to automatically detect the epileptic EEG signals from the EEG recordings. The method of determining the embedding dimension based on nonlinear prediction is applied to choose the embedding dimension of the EEG data. The proposed feature extraction method is used to extract the feature for three groups of EEG time series composing epileptic seizure. We divide the EEG data into segments, and respectively compute the feature values of each segment, where the length of data segment respectively takes the value of 250, 500, 1000 points. To investigate the robustness of our method under noises, we also analyze the three EEG time series with additive white Gaussian noise. The experiment results show that the feature values extracted with the proposed method could obviously distinguish the epileptic EEG signals from the normal EEG signals. The proposed method is effective for short time series, insensitive to the length of data segment, and robust to the additive white noise, and it could differentiate the epileptic EEG from the normal EEG when the signal-to-noise ratio is low.
    No preview · Article · Aug 2010 · Conference proceedings: ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference
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    Bin Yang · Yuehui Chen · Qingfang Meng
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    ABSTRACT: This paper presents an evolutionary method for identifying the gene regulatory network from the observed time series data of gene expression using a system of ordinary differential equations (ODEs) as a model of network. The structure of ODE is inferred by the Multi Expression Programming (MEP) and the ODE’s parameters are optimized by using particle swarm optimization (PSO). The proposed method can acquire the best structure of the ODE only by a small population, and also by partitioning the search space of system of ODEs can be reduced significantly. The effectiveness and accuracy of the proposed method are demonstrated by using synthesis data from the artificial genetic networks.
    Full-text · Conference Paper · Sep 2009
  • Bin Yang · Yuehui Chen · Qingfang Meng
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    ABSTRACT: This paper presents an evolutionary method for identifying a system of ordinary differential equations (ODEs) from the observed time series data. The structure of ODE is inferred by the Multi Expression Programming (MEP) and the ODE’s parameters are optimized by using particle swarm optimization (PSO). The experimental results on chemical reaction modeling problems show effectiveness of the proposed method.
    No preview · Conference Paper · May 2009
  • Qingfang Meng · Yuehui Chen · Yuhua Peng · Wei Li
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    ABSTRACT: In this paper we apply the nonlinear time series prediction method to the traffic measurements data. Based on the phase space reconstruction, the support vector machine prediction method is used to predict the traffic measurements data, and the neighbor point selection method is used to choose the number of nearest neighbor points for the support vector machine regression model. The experiment results show that the nonlinear time series prediction method can effectively predict the traffic measurements data and the prediction error mainly concentrates on the vicinity of zero.
    No preview · Conference Paper · Apr 2009
  • Peng Wu · Yuehui Chen · Qingfang Meng · Zhen Liu
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    ABSTRACT: The self-similar and nonlinear nature of network traffic makes high accurate prediction difficult. Various technology, including Autoregressive Integrated Moving Average (ARIMA), Local Approximation (LA), Neural Network (NN) etc., have been applied to internet traffic prediction. In this paper, Complex Network based on genetic programming and particle swarm optimization is proposed to predict the time series of internet traffic.We propose an automatic method for constructing and evolving our complex network model. The structure of complex network is evolved using genetic programming, and the fine tuning of the parameters encoded in the structure is accomplished using particle swarm optimization algorithm. The relative performances of our model are reported. The results show that our model has high prediction accuracy and can characterize real network traffic well.
    No preview · Conference Paper · Jan 2009
  • Yuehui Chen · Wei Li · Xiao Xie · Qingfang Meng
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    ABSTRACT: Protein secondary structure prediction is a bridge between amino acid sequence and tertiary prediction. Various methods have been used to improve the prediction accuracy and have been developed greatly. Protein classification is a multi-class classification problem. For traditional method, the three structure are predicted in the same time. But it can be degraded to a set of binary classification problem, where one classifier is designed for each class. However, how to combine them again is a difficult question. We found that different prediction's order can get different result. Here we propose a new method named signal series-wound method to predict the three structures signally by different order, and then by voting we get the best prediction result.This article propose a new method to predict protein secondary structure, to invalidate the effect of signal output. First, we predict the three structures separately, classifiers trained in different feature spaces.The proteins in one class are seen as negative example while those outside the class are seen as negative example. Then the three structure result are combined together by Series-wound method. The experiments on Database 396 show good result and prove the method is effectively.
    No preview · Article · Jan 2009
  • Qingfang Meng · Yuhua Peng
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    ABSTRACT: A new local linear prediction model is proposed to predict chaotic time series in this Letter. We propose that the parameters—the embedding dimension and the time delay of the local linear prediction model—can take values which are different to those of the state space reconstruction in the procedure of finding the nearest neighbor points. We propose a criterion based on prediction power to determine the optimal parameters of the new local linear prediction model. Simulation results show that the new local linear prediction model can effectively predict chaotic time series and the prediction performance of the new local linear prediction model is superior to that of the local linear prediction.
    No preview · Article · Oct 2007 · Physics Letters A
  • Source
    Yuehui Chen · Bin Yang · Yaou Zhao · Qingfang Meng
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    ABSTRACT: This paper presents a hybrid evolutionary method for identifying a system of ordinary differential equations (ODEs) from the observed time series. In this approach, the tree-structure based evolution algorithm and particle swarm optimization (PSO) are employed to evolve the ar- chitecture and the parameters of the additive tree models for the problem of system identification. Experimental results on modeling biochemical system show that the proposed method is more fea- sible and effective than other related works.
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Publication Stats

98 Citations
17.30 Total Impact Points

Institutions

  • 2009-2014
    • University of Jinan (Jinan, China)
      Chi-nan-shih, Shandong Sheng, China
  • 2007-2013
    • Shandong University
      • School of Information Science and Engineering
      Chi-nan-shih, Shandong Sheng, China