Detection of seizures in EEG signal using weighted locally linear embedding and SVM classifier

To diagnose the structural disorders of brain, electroencephalography (EEG) is routinely used for observing the epileptic seizures in neurology clinics, which is one of the major brain disorders till today. In this work, we present a new, EEG-based, brain-state identification method which could form the basis for detecting epileptic seizure. We aim to classify the EEG signals and diagnose the epileptic seizures directly by using weighted locally linear embedding (WLLE) and support vector machine (SVM). Firstly, we use WLLE to do feature extraction of the EEG signal to obtain more compact representations of the internal characteristic and structure in the original data, which captures the information necessary for further manipulations. Then, SVM classifier is used to identify the seizures onset state from normal state of the patients.

[1]  J. Echauz,et al.  A genetic approach to selecting the optimal feature for epileptic seizure prediction , 2001, 2001 Conference Proceedings of the 23rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[2]  Yan Qiu Chen,et al.  Improving nearest neighbor classification with cam weighted distance , 2006, Pattern Recognit..

[3]  Shuzhi Sam Ge,et al.  Robust Human Detection and Identification by Using Stereo and thermal Images in Human Robot Interaction , 2007, Int. J. Inf. Acquis..

[4]  Andrew Krystal,et al.  Stationarity and Redundancy of Multichannel EEG Data Recorded During Generalized Tonic-Clonic Seizures , 1999, Brain Topography.

[5]  R. Racine,et al.  Modification of seizure activity by electrical stimulation. II. Motor seizure. , 1972, Electroencephalography and clinical neurophysiology.

[6]  G. Bergey,et al.  Time-frequency analysis using the matching pursuit algorithm applied to seizures originating from the mesial temporal lobe. , 1998, Electroencephalography and clinical neurophysiology.

[7]  I. Gath,et al.  Prediction of epileptic seizures from two-channel EEG , 1998, Medical and Biological Engineering and Computing.

[8]  Brian Litt,et al.  One-Class Novelty Detection for Seizure Analysis from Intracranial EEG , 2006, J. Mach. Learn. Res..

[9]  G. Vachtsevanos,et al.  Epileptic Seizures May Begin Hours in Advance of Clinical Onset A Report of Five Patients , 2001, Neuron.

[10]  William J. Williams,et al.  Nonlinear Dynamics of Electrocorticographic Data in Temporal Lobe Epilepsy , 1988 .

[11]  Abdullah Al Mamun,et al.  Nonlinear Control of Synaptic Plasticity Model for Constraining Bursting Activity in Epileptic Seizures , 2007, 2007 American Control Conference.

[12]  W. J. Williams,et al.  Phase space topography and the Lyapunov exponent of electrocorticograms in partial seizures , 2005, Brain Topography.

[13]  Lawrence K. Saul,et al.  Think Globally, Fit Locally: Unsupervised Learning of Low Dimensional Manifold , 2003, J. Mach. Learn. Res..

[14]  Abdullah Al Mamun,et al.  Detection of Epileptic Spike-Wave Discharges Using SVM , 2007, 2007 IEEE International Conference on Control Applications.

[15]  F. R. Tang,et al.  Calcium binding protein containing neurons in the gliotic mouse hippocampus with special reference to their afferents from the medial septum and the entorhinal cortex , 2006, Neuroscience.

[16]  J. Burgunder,et al.  Metabotropic glutamate receptor 2/3 in the hippocampus of patients with mesial temporal lobe epilepsy, and of rats and mice after pilocarpine-induced status epilepticus , 2004, Epilepsy Research.

[17]  Shuzhi Sam Ge,et al.  Feature representation based on intrinsic structure discovery in high dimensional space , 2006, Proceedings 2006 IEEE International Conference on Robotics and Automation, 2006. ICRA 2006..

[18]  S T Roweis,et al.  Nonlinear dimensionality reduction by locally linear embedding. , 2000, Science.

[19]  A. Geva,et al.  Forecasting generalized epileptic seizures from the EEG signal by wavelet analysis and dynamic unsupervised fuzzy clustering , 1998, IEEE Transactions on Biomedical Engineering.

[20]  Marc W. Slutzky,et al.  Manipulating epileptiform bursting in the rat hippocampus using chaos control and adaptive techniques , 2003, IEEE Transactions on Biomedical Engineering.