Ant K-Means Clustering Method on Epileptic Spike Detection

Sudden unexpected death in epilepsy (SUDEP) is the top of death rate of epilepsy population. To develop an accurate, realizable, personalized automatic epilepsy spike detection method is valuable for understanding epilepsy and preventing the possible loss. In this research, a novel spike detection method based on ant k-means (AK) clustering is proposed. By compare with other intelligent computing methods, our results show that AK worked successfully well in our epilepsy patient data with100% sensitivity, 96% specificity, and 97.9% accuracy. Although the EEG analysis system still has room for improving, the preliminary results are encouraging for future developments.

[1]  Mehmet Kuntalp,et al.  A study on fuzzy C-means clustering-based systems in automatic spike detection , 2007, Comput. Biol. Medicine.

[2]  Nurettin Acir,et al.  Automatic detection of epileptiform events in EEG by a three-stage procedure based on artificial neural networks , 2005, IEEE Transactions on Biomedical Engineering.

[3]  Thomas Stützle,et al.  The Ant Colony Optimization Metaheuristic: Algorithms, Applications, and Advances , 2003 .

[4]  Lipo Wang,et al.  Data Mining With Computational Intelligence , 2006, IEEE Transactions on Neural Networks.

[5]  Ernst Fernando Lopes Da Silva Niedermeyer,et al.  Electroencephalography, basic principles, clinical applications, and related fields , 1982 .

[6]  R. J. Kuo,et al.  Application of ant K-means on clustering analysis , 2005 .

[7]  E. John,et al.  Electroencephalography: Basic Principles and Applications , 2001 .

[8]  Paulo Cortez,et al.  Data Mining with , 2005 .

[9]  V.S. Tseng,et al.  Efficiently mining gene expression data via a novel parameterless clustering method , 2005, IEEE/ACM Transactions on Computational Biology and Bioinformatics.

[10]  R. Kloster,et al.  Sudden unexpected death in epilepsy (SUD) , 1991 .

[11]  Jing Wang,et al.  A spike detection method in EEG based on improved morphological filter , 2007, Comput. Biol. Medicine.

[12]  Daewon Lee,et al.  Dynamic Characterization of Cluster Structures for Robust and Inductive Support Vector Clustering , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[13]  A. Schulze-Bonhage,et al.  Comparison of three nonlinear seizure prediction methods by means of the seizure prediction characteristic , 2004 .

[14]  Bing Liu,et al.  An efficient semi-unsupervised gene selection method via spectral biclustering , 2006, IEEE Transactions on NanoBioscience.