The classification of EEG signals using discretization-based entropy and the adaptive neuro-fuzzy inference system

A novel feature extraction called discretization-based entropy is proposed for use in the classification of EEG signals. To this end, EEG signals are decomposed into frequency subbands using the discrete wavelet transform (DWT), the coefficients of these subbands are discretized into the desired number of intervals using the discretization method, the entropy values of the discretized subbands are calculated using the Shannon entropy method, and these are then ?used as the inputs of the adaptive neuro-fuzzy inference system (ANFIS). The equal width discretization (EWD) and equal frequency discretization (EFD) methods are used for the discretization. In order to evaluate their performances in terms of classification accuracy, three different experiments are implemented using different combinations of healthy segments, epileptic seizure-free segments, and epileptic seizure segments. The experiments show that the EWD-based entropy approach achieves higher classification accuracy rates than the EFD-based entropy approach.

[1]  Elif Derya Übeyli,et al.  Adaptive neuro-fuzzy inference system for classification of EEG signals using wavelet coefficients , 2005, Journal of Neuroscience Methods.

[2]  A. Aarabi,et al.  A fuzzy rule-based system for epileptic seizure detection in intracranial EEG , 2009, Clinical Neurophysiology.

[3]  Abdulhamit Subasi,et al.  EEG signal classification using wavelet feature extraction and a mixture of expert model , 2007, Expert Syst. Appl..

[4]  Rafael C. González,et al.  Local Determination of a Moving Contrast Edge , 1985, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[5]  U. Rajendra Acharya,et al.  Author's Personal Copy Biomedical Signal Processing and Control Automated Diagnosis of Epileptic Eeg Using Entropies , 2022 .

[6]  Abdulhamit Subasi,et al.  Application of adaptive neuro-fuzzy inference system for epileptic seizure detection using wavelet feature extraction , 2007, Comput. Biol. Medicine.

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

[8]  Chun-Nan Hsu,et al.  Implications of the Dirichlet Assumption for Discretization of Continuous Variables in Naive Bayesian Classifiers , 2004, Machine Learning.

[9]  Ahmad Reza Naghsh-Nilchi,et al.  Epilepsy seizure detection using eigen-system spectral estimation and Multiple Layer Perceptron neural network , 2010, Biomed. Signal Process. Control..

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

[11]  Ahmet Alkan,et al.  Classification of EEG Recordings by Using Fast Independent Component Analysis and Artificial Neural Network , 2008, Journal of Medical Systems.

[12]  Yusuf Uzzaman Khan,et al.  Brain-computer interface for single-trial eeg classification for wrist movement imagery using spatial filtering in the gamma band , 2010 .

[13]  Osman Erogul,et al.  Epileptic EEG detection using the linear prediction error energy , 2010, Expert Syst. Appl..

[14]  V. Srinivasan,et al.  Artificial Neural Network Based Epileptic Detection Using Time-Domain and Frequency-Domain Features , 2005, Journal of Medical Systems.

[15]  Daniel Rivero,et al.  Using genetic algorithms and k-nearest neighbour for automatic frequency band selection for signal classification , 2012, IET Signal Process..

[16]  V. Srinivasan,et al.  Approximate Entropy-Based Epileptic EEG Detection Using Artificial Neural Networks , 2007, IEEE Transactions on Information Technology in Biomedicine.

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

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

[19]  Mahmut Ozer,et al.  Epileptic Seizure Detection Using Probability Distribution Based On Equal Frequency Discretization , 2012, Journal of Medical Systems.

[20]  Heekuck Oh,et al.  Neural Networks for Pattern Recognition , 1993, Adv. Comput..

[21]  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.

[22]  Jyh-Shing Roger Jang,et al.  ANFIS: adaptive-network-based fuzzy inference system , 1993, IEEE Trans. Syst. Man Cybern..

[23]  Natarajan Sriraam,et al.  Entropies based detection of epileptic seizures with artificial neural network classifiers , 2010, Expert Syst. Appl..

[24]  Elif Derya íbeyli.  Analysis of EEG signals by combining eigenvector methods and multiclass support vector machines , 2008 .

[25]  Hasan Ocak,et al.  Automatic detection of epileptic seizures in EEG using discrete wavelet transform and approximate entropy , 2009, Expert Syst. Appl..

[26]  Elif Derya Übeyli.  Analysis of EEG signals by combining eigenvector methods and multiclass support vector machines , 2008, Comput. Biol. Medicine.

[27]  Mahmut Ozer,et al.  EEG signals classification using the K-means clustering and a multilayer perceptron neural network model , 2011, Expert Syst. Appl..