Energy Distribution of EEG Signals: EEG Signal Wavelet-Neural Network Classifier

In this paper, a wavelet-based neural network (WNN) classifier for recognizing EEG signals is implemented and tested under three sets EEG signals (healthy subjects, patients with epilepsy and patients with epileptic syndrome during the seizure). First, the Discrete Wavelet Transform (DWT) with the Multi-Resolution Analysis (MRA) is applied to decompose EEG signal at resolution levels of the components of the EEG signal (delta, theta, alpha, beta and gamma) and the Parsevals theorem are employed to extract the percentage distribution of energy features of the EEG signal at different resolution levels. Second, the neural network (NN) classifies these extracted features to identify the EEGs type according to the percentage distribution of energy features. The performance of the proposed algorithm has been evaluated using in total 300 EEG signals. The results showed that the proposed classifier has the ability of recognizing and classifying EEG signals efficiently.

[1]  Ibrahim Turkoglu,et al.  A new approach for diagnosing epilepsy by using wavelet transform and neural networks , 2001, 2001 Conference Proceedings of the 23rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[2]  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.

[3]  Praveen Settipalli,et al.  AUTOMATED CLASSIFICATION OF POWER QUALITY DISTURBANCES USING SIGNAL PROCESSING TECHNIQUES AND NEURAL NETWORKS , 2007 .

[4]  Tong Zhang,et al.  A multistage, multimethod approach for automatic detection and classification of epileptiform EEG , 2002, IEEE Transactions on Biomedical Engineering.

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

[6]  G. G. Stokes.  "J." , 1890, The New Yale Book of Quotations.

[7]  I A Basheer,et al.  Artificial neural networks: fundamentals, computing, design, and application. , 2000, Journal of microbiological methods.

[8]  M Unser,et al.  Fast wavelet transformation of EEG. , 1994, Electroencephalography and clinical neurophysiology.

[9]  Kaspar Anton Schindler,et al.  Wavelet-denoising of electroencephalogram and the absolute slope method: A new tool to improve electroencephalographic localization and lateralization , 2009, Clinical Neurophysiology.

[10]  H. He,et al.  A self-organizing learning array system for power quality classification based on wavelet transform , 2006, IEEE Transactions on Power Delivery.

[11]  A. Prochazka,et al.  Wavelet transform use for feature extraction and EEG signal segments classification , 2008, 2008 3rd International Symposium on Communications, Control and Signal Processing.

[12]  Ingrid Daubechies,et al.  Ten Lectures on Wavelets , 1992 .

[13]  Maan M. Shaker,et al.  EEG Waves Classifier using Wavelet Transform and Fourier Transform , 2007 .

[14]  L. Senhadji,et al.  Wavelet analysis of EEG for three-dimensional mapping of epileptic events , 1995, Annals of Biomedical Engineering.

[15]  Lucila Ohno-Machado,et al.  Logistic regression and artificial neural network classification models: a methodology review , 2002, J. Biomed. Informatics.

[16]  Reza Tafreshi,et al.  A novel wavelet-based index to detect epileptic seizures using scalp EEG signals , 2008, 2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[17]  K Asaduzzaman,et al.  A study on discrete wavelet-based noise removal from EEG signals. , 2010, Advances in experimental medicine and biology.

[18]  S. Mallat.  A wavelet tour of signal processing , 1998 .

[19]  Kenneth Revett,et al.  EEG Signal Classification Using Wavelet Feature Extraction and Neural Networks , 2006, IEEE John Vincent Atanasoff 2006 International Symposium on Modern Computing (JVA'06).

[20]  Carlos E. D'Attellis,et al.  Detection of epileptic events in electroencephalograms using wavelet analysis , 2007, Annals of Biomedical Engineering.

[21]  Lalit M. Patnaik,et al.  Epileptic EEG detection using neural networks and post-classification , 2008, Comput. Methods Programs Biomed..

[22]  Bidyut Baran Chaudhuri,et al.  Efficient training and improved performance of multilayer perceptron in pattern classification , 2000, Neurocomputing.

[23]  B. Kotchoubey,et al.  Recognition of affective prosody: continuous wavelet measures of event-related brain potentials to emotional exclamations. , 2004, Psychophysiology.

[24]  Abdulhamit Subasi,et al.  Classification of EEG signals using neural network and logistic regression , 2005, Comput. Methods Programs Biomed..

[25]  Sazali Yaacob,et al.  EEG feature extraction for classifying emotions using FCM and FKM , 2008 .

[26]  Daniel Rivero,et al.  Automatic epileptic seizure detection in EEGs based on line length feature and artificial neural networks , 2010, Journal of Neuroscience Methods.

[27]  John B. Shoven,et al.  I , Edinburgh Medical and Surgical Journal.

[28]  Junzhong Zou,et al.  Feature extraction and recognition of epileptiform activity in EEG by combining PCA with ApEn , 2010, Cognitive Neurodynamics.

[29]  Abdulhamit Subasi,et al.  Wavelet neural network classification of EEG signals by using AR model with MLE preprocessing , 2005, Neural Networks.

[30]  Ehsan Mesbahi,et al.  Artificial neural networks: fundamentals , 2003 .

[31]  -. Bus10,et al.  Identifications and Monitoring of Power System Dynamics Based on the PMUs and Wavelet Technique , 2010 .

[32]  Reza Tafreshi,et al.  Automated Real-Time Epileptic Seizure Detection in Scalp EEG Recordings Using an Algorithm Based on Wavelet Packet Transform , 2010, IEEE Transactions on Biomedical Engineering.

[33]  Hojjat Adeli,et al.  A Wavelet-Chaos Methodology for Analysis of EEGs and EEG Subbands to Detect Seizure and Epilepsy , 2007, IEEE Transactions on Biomedical Engineering.

[34]  Yann LeCun,et al.  Classification of patterns of EEG synchronization for seizure prediction , 2009, Clinical Neurophysiology.

[35]  Daniel Rivero,et al.  Classification of EEG signals using relative wavelet energy and artificial neural networks , 2009, GEC '09.