Classification of focal and nonfocal EEG signals using ANFIS classifier for epilepsy detection

The electroencephalogram (EEG) is the frequently used signal to detect epileptic seizures in the brain. For a successful epilepsy surgery, it is very essential to localize epileptogenic area in the brain. The signals from the epileptogenic area are focal signals and signals from other area of the brain region nonfocal signals. Hence, the classification of focal and nonfocal signals is important for locating the epileptogenic area for epilepsy surgery. In this article, we present a computer aided automatic detection and classification method for focal and nonfocal EEG signal. The EEG signal is decomposed by Dual Tree Complex Wavelet Transform (DT‐CWT) and the features are computed from the decomposed coefficients. These features are trained and classified using Adaptive Neuro Fuzzy Inference System (ANFIS) classifier. The proposed system achieves 98% sensitivity, 100% specificity, and 99% accuracy for EEG signal classification. The experimental results are presented to show the effectiveness of the proposed classification method to classify the focal and nonfocal EEG signals. © 2016 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 26, 277–283, 2016

[1]  Anindya Bijoy Das,et al.  Discrimination and classification of focal and non-focal EEG signals using entropy-based features in the EMD-DWT domain , 2016, Biomed. Signal Process. Control..

[2]  Stefano Di Gennaro,et al.  Detection of epileptiform activity in EEG signals based on time-frequency and non-linear analysis , 2015, Front. Comput. Neurosci..

[3]  W J McKay,et al.  SPECT in the localisation of extratemporal and temporal seizure foci. , 1995, Journal of neurology, neurosurgery, and psychiatry.

[4]  Richard Kempter,et al.  State-dependencies of learning across brain scales , 2015, Front. Comput. Neurosci..

[5]  A. Aertsen,et al.  Detecting Epileptic Seizures in Long-term Human EEG: A New Approach to Automatic Online and Real-Time Detection and Classification of Polymorphic Seizure Patterns , 2008, Journal of clinical neurophysiology : official publication of the American Electroencephalographic Society.

[6]  Damir Sersic,et al.  Signal Decomposition Methods for Reducing Drawbacks of the DWT , 2012 .

[7]  A J Gabor,et al.  Seizure detection using a self-organizing neural network: validation and comparison with other detection strategies. , 1998, Electroencephalography and clinical neurophysiology.

[8]  Rajeev Sharma,et al.  Empirical Mode Decomposition Based Classification of Focal and Non-focal Seizure EEG Signals , 2014, 2014 International Conference on Medical Biometrics.

[9]  Sadiq Ali,et al.  Classification of EEG Signals Using Adaptive Time-Frequency Distributions , 2016 .

[10]  Ralph G Andrzejak,et al.  Nonrandomness, nonlinear dependence, and nonstationarity of electroencephalographic recordings from epilepsy patients. , 2012, Physical review. E, Statistical, nonlinear, and soft matter physics.

[11]  Ali H. Shoeb,et al.  Patient-specific seizure onset detection , 2004, Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[12]  Junjie Chen,et al.  The detection of epileptic seizure signals based on fuzzy entropy , 2015, Journal of Neuroscience Methods.

[13]  R. Harner,et al.  Patient-Specific Early Seizure Detection From Scalp Electroencephalogram , 2010, Journal of clinical neurophysiology : official publication of the American Electroencephalographic Society.

[14]  Nick Kingsbury,et al.  The dual-tree complex wavelet transform: a new technique for shift invariance and directional filters , 1998 .

[15]  Ahmad Taher Azar,et al.  Performance analysis of support vector machines classifiers in breast cancer mammography recognition , 2013, Neural Computing and Applications.

[16]  A J Gabor,et al.  Automated seizure detection using a self-organizing neural network. , 1996, Electroencephalography and clinical neurophysiology.

[17]  Stefano Di Gennaro,et al.  CLASSIFICATION OF EEG SIGNALS FOR DETECTION OF EPILEPTIC SEIZURES BASED ON WAVELETS AND STATISTICAL PATTERN RECOGNITION , 2014 .

[18]  Ivanka Savic,et al.  [11C]Flumazenil Positron Emission Tomography Visualizes Frontal Epileptogenic Regions , 1995, Epilepsia.

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

[20]  Hee Don Seo,et al.  Seizure Detection in Temporal Lobe Epileptic EEGs Using the Best Basis Wavelet Functions , 2010, Journal of Medical Systems.

[21]  C. M. Lim,et al.  Automatic identification of epilepsy by HOS and power spectrum parameters using EEG signals: A comparative study , 2008, 2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[22]  Weidong Zhou,et al.  Epileptic EEG classification based on extreme learning machine and nonlinear features , 2011, Epilepsy Research.

[23]  U. Rajendra Acharya,et al.  Application of Entropy Measures on Intrinsic Mode Functions for the Automated Identification of Focal Electroencephalogram Signals , 2015, Entropy.

[24]  Marta Canuti,et al.  Influenza and Other Respiratory Viruses Involved in Severe Acute Respiratory Disease in Northern Italy during the Pandemic and Postpandemic Period (2009–2011) , 2014, BioMed research international.

[25]  M. L. Dewal,et al.  Epileptic seizure detection using DWT based fuzzy approximate entropy and support vector machine , 2014, Neurocomputing.

[26]  T Landis,et al.  Non-invasive epileptic focus localization using EEG-triggered functional MRI and electromagnetic tomography. , 1998, Electroencephalography and clinical neurophysiology.

[27]  Yan Li,et al.  Epileptogenic focus detection in intracranial EEG based on delay permutation entropy , 2013 .

[28]  Thasneem Fathima,et al.  Detection of Epileptic Seizure Event and Onset Using EEG , 2014, BioMed research international.