Features extracted by eigenvector methods for detecting variability of EEG signals

In this paper, we present the expert systems for detecting variability of electroencephalogram (EEG) signals. In practical applications of pattern recognition, there are often diverse features extracted from raw data which needs recognizing. Methods of combining multiple classifiers with diverse features are viewed as a general problem in various application areas of pattern recognition. Because of the importance of making the right decision, we are looking for better classification procedures for EEG signals. The mixture of experts (ME) and modified mixture of experts (MME) were tested and benchmarked for their performance on the classification of the studied EEG signals. Decision making was performed in two stages: feature extraction by eigenvector methods and classification using the classifiers trained on the extracted features. The inputs of these expert systems composed of diverse or composite features were chosen according to the network structures. The present study was conducted with the purpose of answering the question of whether the expert system with diverse features (MME) or composite feature (ME) improve the capability of classification of the EEG signals. Our research demonstrated that the MME trained on diverse features achieved accuracy rates which were higher than that of the ME.

[1]  Daniel Graupe,et al.  A neural-network-based detection of epilepsy , 2004, Neurological research.

[2]  Elif Derya Übeyli,et al.  Recurrent neural networks employing Lyapunov exponents for EEG signals classification , 2005, Expert Syst. Appl..

[3]  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.

[4]  Geoffrey E. Hinton,et al.  Adaptive Mixtures of Local Experts , 1991, Neural Computation.

[5]  J Gotman,et al.  Automatic EEG analysis during long-term monitoring in the ICU. , 1998, Electroencephalography and clinical neurophysiology.

[6]  Ke Chen.  A connectionist method for pattern classification with diverse features , 1998, Pattern Recognit. Lett..

[7]  J. Semmlow,et al.  Noninvasive detection of coronary stenoses before and after angioplasty using eigenvector methods , 1990, IEEE Transactions on Biomedical Engineering.

[8]  Xia Hong,et al.  A Mixture of Experts Network Structure Construction Algorithm for Modelling and Control , 2001, Applied Intelligence.

[9]  Ke Chen,et al.  Improved learning algorithms for mixture of experts in multiclass classification , 1999, Neural Networks.

[10]  Michael I. Jordan,et al.  Hierarchical Mixtures of Experts and the EM Algorithm , 1994, Neural Computation.

[11]  David West,et al.  An improved neural classification network for the two-group problem , 1999, Comput. Oper. Res..

[12]  Elif Derya Übeyli,et al.  A mixture of experts network structure for modelling Doppler ultrasound blood flow signals , 2005, Comput. Biol. Medicine.

[13]  Elif Derya Übeyli,et al.  A modified mixture of experts network structure for ECG beats classification with diverse features , 2005, Eng. Appl. Artif. Intell..

[14]  Elif Derya Übeyli,et al.  Feature extraction from Doppler ultrasound signals for automated diagnostic systems , 2005, Comput. Biol. Medicine.

[15]  R. O. Schmidt,et al.  Multiple emitter location and signal Parameter estimation , 1986 .

[16]  R. Kumaresan,et al.  Estimating the Angles of Arrival of Multiple Plane Waves , 1983, IEEE Transactions on Aerospace and Electronic Systems.

[17]  Elif Derya Übeyli,et al.  Comparison of eigenvector methods with classical and model-based methods in analysis of internal carotid arterial Doppler signals , 2003, Comput. Biol. Medicine.

[18]  H. Adeli,et al.  Analysis of EEG records in an epileptic patient using wavelet transform , 2003, Journal of Neuroscience Methods.

[19]  Chong-Ho Choi,et al.  Input feature selection for classification problems , 2002, IEEE Trans. Neural Networks.

[20]  Thomas Kailath,et al.  Detection of signals by information theoretic criteria , 1985, IEEE Trans. Acoust. Speech Signal Process..

[21]  V. Pisarenko.  The Retrieval of Harmonics from a Covariance Function , 1973 .

[22]  Ah Chung Tsoi,et al.  Classification of EEG signals using the wavelet transform , 1997, Proceedings of 13th International Conference on Digital Signal Processing.

[23]  W.J. Tompkins,et al.  A patient-adaptable ECG beat classifier using a mixture of experts approach , 1997, IEEE Transactions on Biomedical Engineering.