一级黄色片免费播放|中国黄色视频播放片|日本三级a|可以直接考播黄片影视免费一级毛片

高級搜索

留言板

尊敬的讀者、作者、審稿人, 關(guān)于本刊的投稿、審稿、編輯和出版的任何問題, 您可以本頁添加留言。我們將盡快給您答復(fù)。謝謝您的支持!

姓名
郵箱
手機(jī)號碼
標(biāo)題
留言內(nèi)容
驗(yàn)證碼

面向癲癇腦電圖信號識別的徑向基最小最大概率分類樹

鄧趙紅,  陳俊勇,  劉解放,  王士同

鄧趙紅, 陳俊勇, 劉解放, 王士同. 面向癲癇腦電圖信號識別的徑向基最小最大概率分類樹[J]. 電子與信息學(xué)報, 2016, 38(11): 2848-2855. doi: 10.11999/JEIT160082
引用本文: 鄧趙紅, 陳俊勇, 劉解放, 王士同. 面向癲癇腦電圖信號識別的徑向基最小最大概率分類樹[J]. 電子與信息學(xué)報, 2016, 38(11): 2848-2855. doi: 10.11999/JEIT160082
DENG Zhaohong, CHEN Junyong, LIU Jiefang, WANG Shitong. Radial Basis Minimax Probability Classification Tree for Epilepsy ElectroEncephaloGram Signal Recognition[J]. Journal of Electronics & Information Technology, 2016, 38(11): 2848-2855. doi: 10.11999/JEIT160082
Citation: DENG Zhaohong, CHEN Junyong, LIU Jiefang, WANG Shitong. Radial Basis Minimax Probability Classification Tree for Epilepsy ElectroEncephaloGram Signal Recognition[J]. Journal of Electronics & Information Technology, 2016, 38(11): 2848-2855. doi: 10.11999/JEIT160082

面向癲癇腦電圖信號識別的徑向基最小最大概率分類樹

doi: 10.11999/JEIT160082
基金項(xiàng)目: 

江蘇省杰出青年基金(BK20140001)，上海市科學(xué)技術(shù)委員會揚(yáng)帆項(xiàng)目(14YF1411000)，上海市教委創(chuàng)新項(xiàng)目(14YZ131)

Radial Basis Minimax Probability Classification Tree for Epilepsy ElectroEncephaloGram Signal Recognition

Funds: 

The Youth Fund of Jiangsu Province (BK20140001), YangFan Project of Shanghai Municipal Science and Technology Commission(Grant No. 14YF1411000), The Innovation Program of Shanghai Municipal Education Commission (Grant No. 14YZ131)

  • 摘要: 腦電圖(EEG)信號檢測和識別是癲癇病的重要診斷手段。徑向基函數(shù)神經(jīng)網(wǎng)絡(luò)具有出色的逼近能力和泛化性能，能直接識別出不同狀態(tài)的腦電信號，但其透明性和可解釋性差，忽視了不同類別數(shù)據(jù)間可分性的不同。對此，該文提出一種基于徑向基函數(shù)神經(jīng)網(wǎng)絡(luò)和最小最大概率決策技術(shù)的分類樹，采用一對一策略和排除法，更多考慮了類間可分性的不同。針對腦電信號識別的實(shí)驗(yàn)表明，所提方法結(jié)構(gòu)清晰，分類能力強(qiáng)，可解釋性更好。
    Abstract: ElectroEncephaloGram (EEG) signal detection and recognition is an important diagnostic method for the epilepsy. Radial Basis Function (RBF) neural network has excellent performance on approximation and generalization, and can directly recognize EEG signals in different states. However, its transparency and interpretability are low, and it also ignore the different separabilities between different classes of data. In this paper, a classification tree based on RBF neural networks and minimax probability decision technique is proposed, using one-against-one and exclusive method and paying much attention to the different separabilities among classes. Experiments on EEG signals show that the proposed method has clear structure, strong classification ability and better interpretability.
    • HTML全文
  • VENEMA V, AMENT F, and SIMMER C. A stochastic iterative amplitude adjusted Fourier transform algorithm with improved accuracy[J]. Nonlinear Processes in Geophysics, 2006, 13(3): 321-328. doi: 10.5194/npg-13- 321-2006.
    POLAT K and GNES S. Classification of epileptiform EEG using a hybrid system based on decision tree classifier and fast Fourier transform[J]. Applied Mathematics Computation, 2007, 187(2): 1017-1026. doi: 10.1016/j.amc. 2006.09.022.
    INAN G and ELIF DERYA U. Adaptive neuro-fuzzy inference system for classification of EEG signals using wavelet coefficients[J]. Journal of Neuroscience Methods, 2005, 148(2): 113-121. doi: 10.1016/j.jneumeth.2005.04.013.
    SUBASI A. EEG signal classification using wavelet feature extraction and a mixture of expert model[J]. Expert Systems with Applications, 2007, 32(4): 1084-1093. doi: 10.1016/ j.eswa.2006.02.005.
    王登, 苗奪謙, 王睿智. 一種新的基于小波包分解的EEG特征抽取與識別方法研究[J]. 電子學(xué)報, 2013, 41(1): 193-198. doi: 10.3969/j.issn.0372-2112.2013.01.33.
    WANG Deng, MIAO Duoqian, and WANG Ruizhi. A new method of EEG classification with feature extraction based on wavelet packet decomposition[J]. Acta Electronica Sinica, 2013, 41(1): 193-198. doi: 10.3969/j.issn.0372-2112.2013. 01.33.
    SRINIVASAN V, ESWARAN C, and SRIRAAM A N. Artificial neural network based epileptic detection using time-domain and frequency-domain features[J]. Journal of Medical Systems, 2005, 29(6): 647-660. doi: 10.1007/ s10916-005-6133-1.
    VAIRAVAN S, CHIKKANNAN E, and NATARAJAN S. Approximate entropy-based epileptic EEG detection using artificial neural networks[J]. IEEE Transactions on Information Technology in Biomedicine, 2007, 11(3): 288-295. doi: 10.1109/TITB.2006.884369.
    ORHAN U, HEKIM M, and OZER M. EEG signals classification using the K-means clustering and a multilayer perceptron neural network model[J]. Expert Systems with Applications, 2011, 38(10): 13475-13481. doi: 10.1016/ j.eswa.2011.04.149.
    ASLAN K and HSAHIN B. A radial basis function neural network model for classification of epilepsy using EEG signals[J]. Journal of Medical Systems, 2008, 32(5): 403-408. doi: 10.1007/s10916-008-9145-9.
    連可, 陳世杰, 周建明, 等. 基于遺傳算法的SVM多分類決策樹優(yōu)化算法研究[J]. 控制與決策, 2009, 24(1): 7-12. doi: 10.3321/j.issn:1001-0920.2009.01.002.
    LIAN Ke, CHEN Shijie, ZHOU Jianming , et al. Study on GA-based SVM multi-class classification decision-tree optimization agorithm[J]. Control and Decision, 2009, 24(1): 7-12. doi: 10.3321/j.issn:1001-0920.2009.01.002.
    LANCKRIET G, GHAOUI L E, BHATTACHARYYA C, et al. Minimax probability machine[C]. Advances in Neural Information Processing Systems, Vancouver, British Columbia, Canada. 2001: 801-807.
    LANCKRIET G R G, GHAOUI L E, BHATTACHARYYA C, et al. A robust minimax approach to classification[J]. The Journal of Machine Learning Research, 2002, 3(Dec): 555-582. doi: 10.1162/153244303321897726.
    DENG Z, CAO L, JIANG Y, et al. Minimax probability TSK fuzzy system classifier: A more transparent and highly interpretable classification model[J]. IEEE Transactions on Fuzzy Systems, 2015, 23(4): 813-826. doi: 10.1109/TFUZZ. 2014.2328014.
    RUBIO-SOLIS A and PANOUTSOS G. Interval type-2 radial basis function neural network: A modeling framework[J]. IEEE Transactions on Fuzzy Systems, 2015, 23(2): 457-473. doi: 10.1109/TFUZZ.2014.2315656.
    陳聰, 王士同. 基于模糊分組和監(jiān)督聚類的RBF回歸性能改進(jìn)[J]. 電子與信息學(xué)報, 2009, 31(5): 1157-1160.
    CHEN Cong and WANG Shitong. Improved RBF regression using fuzzy partition and supervised fuzzy custering[J]. Journal of Electronics Information Technology, 2009, 31(5): 1157-1160.
    ROTH P M, HIRZER M, KSTINGER M, et al. Mahalanobis Distance Learning for Person Re-identification [M]. London: Person Re-Identification, 2014: 247-267. doi: 10.1007/978-1-4471-6296-4_12.
    Kang S, Cho S, and Kang P. Constructing a multi-class classifier using one-against-one approach with different binary classifiers[J]. Neurocomputing, 2014, 149, Part B(PB): 677-682. doi: 10.1016/j.neucom.2014.08.006.
    GALAR M, FERN?NDEZ A, and BARRENECHEA E. An overview of ensemble methods for binary classifiers in multi-class problems: Experimental study on one-vs-one and one-vs-all schemes[J]. Pattern Recognition, 2011, 44(8): 1761-1776. doi: 10.1016/j.patcog.2011.01.017.
    ANDRZEJAK R G, LEHNERTZ K, MORMANN F, et al. Indications of nonlinear deterministic and finite-dimensional structures in time series of brain electrical activity: Dependence on recording region and brain state[J]. Physical Review E, 2001, 64(6): 061907. doi: 10.1103/PhysRevE.64. 061907.
    PARVEZ M Z and PAUL M. Epileptic seizure detection by analyzing EEG signals using different transformation techniques[J]. Neurocomputing, 2014, 145(18): 190-200. doi: 10.1016/j.neucom.2014.05.044.
    ROY V and SHUKLA S. Automatic removal of artifacts from EEG signal based on spatially constrained ICA using daubechies wavelet[J]. International Journal of Modern Education and Computer Science (IJMECS), 2014, 6(7): 31-39. doi: 10.5815/ijmecs.2014.07.05.
  • 加載中
計(jì)量
  • 文章訪問數(shù):  1092
  • HTML全文瀏覽量:  119
  • PDF下載量:  471
  • 被引次數(shù): 0
出版歷程
  • 收稿日期:  2016-01-19
  • 修回日期:  2016-06-08
  • 刊出日期:  2016-11-19

目錄

    /

    返回文章
    返回