Novel feature extraction method based on weight difference of weighted network for epileptic seizure detection

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.

[1]  H. Stanley,et al.  Quantification of scaling exponents and crossover phenomena in nonstationary heartbeat time series. , 1995, Chaos.

[2]  Pietro Liò,et al.  A new approach for epileptic seizure detection: sample entropy based feature extraction and extreme learning machine , 2010 .

[3]  Cai Dong.  Approach of Epileptic EEG Detection Based on Hurst Exponent and SVM , 2010 .

[4]  Lucas Lacasa,et al.  From time series to complex networks: The visibility graph , 2008, Proceedings of the National Academy of Sciences.

[5]  Elif Derya Übeyli.  Combined neural network model employing wavelet coefficients for EEG signals classification , 2009, Digit. Signal Process..

[6]  Md Nurujjaman,et al.  Comparative study of nonlinear properties of EEG signals of normal persons and epileptic patients , 2007, Nonlinear biomedical physics.

[7]  S. Osowski,et al.  Lyapunov exponent of EEG signal for epileptic seizure characterization , 2005, Proceedings of the 2005 European Conference on Circuit Theory and Design, 2005..

[8]  U. Rajendra Acharya,et al.  Automated EEG analysis of epilepsy: A review , 2013, Knowl. Based Syst..

[9]  Weidong Zhou,et al.  Epileptic seizure detection with linear and nonlinear features , 2012, Epilepsy & Behavior.

[10]  Weidong Zhou,et al.  Epileptic EEG classification based on extreme learning machine and nonlinear features , 2011, Epilepsy Research.

[11]  Hongkui Jing,et al.  Topographic analysis of dimension estimates of EEG and filtered rhythms in epileptic patients with complex partial seizures , 2000, Biological Cybernetics.

[12]  Weidong Zhou,et al.  Feature analysis of epileptic EEG using nonlinear prediction method , 2010, 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology.

[13]  U. Rajendra Acharya,et al.  Entropies for detection of epilepsy in EEG , 2005, Comput. Methods Programs Biomed..

[14]  Ziyou Gao,et al.  Mapping to Complex Networks from Chaos Time Series in the Car Following Model , 2008 .

[15]  Jinjun Tang,et al.  Characterizing traffic time series based on complex network theory , 2013 .

[16]  Yue Yang,et al.  Complex network-based time series analysis , 2008 .

[17]  K Lehnertz,et al.  Indications of nonlinear deterministic and finite-dimensional structures in time series of brain electrical activity: dependence on recording region and brain state. , 2001, Physical review. E, Statistical, nonlinear, and soft matter physics.

[18]  Bijaya K. Panigrahi,et al.  Expert model for detection of epileptic activity in EEG signature , 2010, Expert Syst. Appl..

[19]  M Small,et al.  Complex network from pseudoperiodic time series: topology versus dynamics. , 2006, Physical review letters.