Discrimination and classification of focal and non-focal EEG signals using entropy-based features in the EMD-DWT domain

Abstract In this paper, a comprehensive analysis of focal and non-focal electroencephalography is carried out in the empirical mode decomposition and discrete wavelet transform domains. A number of spectral entropy-based features such as the Shannon entropy, log-energy entropy and Renyi entropy are calculated in the empirical mode decomposition and discrete wavelet transform domains and their efficacy in discriminating the focal and non-focal EEG signals is investigated. The electroencephalogram signals are obtained from a publicly available electroencephalography database that consists of 7500 signal pairs which contain over 80 h of electroencephalogram data collected from five epilepsy patients. It is shown that in the log-energy entropy when calculated in the combined empirical mode decomposition–discrete wavelet transform domain gives a better discrimination of these signals as compared to that of the other entropy measures that is the Shannon and quadratic Renyi entropy as well as to that obtained in empirical mode decomposition or discrete wavelet transform domain. When the log-energy entropy values are utilized as features in a K-nearest neighbor classifier to classify the signals, it provides 89.4% accuracy (with 90.7% sensitivity), which is higher than that of the state-of-the-art methods. Overall, the proposed classification method reports a significant improvement in terms of sensitivity, specificity and accuracy in comparison to the existing techniques. Besides, for being computationally fast, the proposed method has the potential for identifying the epileptogenic zones, which is an important step prior to resective surgery usually performed on patients with low responsiveness to anti-epileptic medications.

[1]  Shufang Li,et al.  Seizure Prediction Using Spike Rate of Intracranial EEG , 2013, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[2]  F.C. Morabito,et al.  Brain Activity Investigation by EEG Processing: Wavelet Analysis, Kurtosis and Renyi's Entropy for Artifact Detection , 2007, 2007 International Conference on Information Acquisition.

[3]  Anindya Bijoy Das,et al.  Classification of EEG signals using normal inverse Gaussian parameters in the dual-tree complex wavelet transform domain for seizure detection , 2016, Signal Image Video Process..

[4]  Sheng-Fu Liang,et al.  A hierarchical approach for online temporal lobe seizure detection in long-term intracranial EEG recordings , 2013, Journal of neural engineering.

[5]  Elham Parvinnia,et al.  Classification of EEG Signals using adaptive weighted distance nearest neighbor algorithm , 2014, J. King Saud Univ. Comput. Inf. Sci..

[6]  S. Kara,et al.  Log Energy Entropy-Based EEG Classification with Multilayer Neural Networks in Seizure , 2009, Annals of Biomedical Engineering.

[7]  U. Rajendra Acharya,et al.  An Integrated Index for the Identification of Focal Electroencephalogram Signals Using Discrete Wavelet Transform and Entropy Measures , 2015, Entropy.

[8]  Tao Zou,et al.  Hilbert marginal spectrum analysis for automatic seizure detection in EEG signals , 2015, Biomed. Signal Process. Control..

[9]  Ahmed El-Kishky,et al.  Assessing entropy and fractal dimensions as discriminants of seizures in EEG time series , 2012, 2012 11th International Conference on Information Science, Signal Processing and their Applications (ISSPA).

[10]  Steve McLaughlin,et al.  Development of EMD-Based Denoising Methods Inspired by Wavelet Thresholding , 2009, IEEE Transactions on Signal Processing.

[11]  Eric Laciar,et al.  An epileptic seizures detection algorithm based on the empirical mode decomposition of EEG , 2009, 2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[12]  W. Hauser,et al.  Comment on Epileptic Seizures and Epilepsy: Definitions Proposed by the International League Against Epilepsy (ILAE) and the International Bureau for Epilepsy (IBE) , 2005, Epilepsia.

[13]  Walter Besio,et al.  Seizure detection using empirical mode decomposition and time-frequency energy concentration , 2013, 2013 IEEE Signal Processing in Medicine and Biology Symposium (SPMB).

[14]  G. Bergey,et al.  Characterization of early partial seizure onset: Frequency, complexity and entropy , 2012, Clinical Neurophysiology.

[15]  Paul Boon,et al.  EEG source localization of the epileptogenic focus in patients with refractory temporal lobe epilepsy, dipole modelling revisited. , 2007, Acta neurologica Belgica.

[16]  Touradj Ebrahimi,et al.  Classification of EEG signals using Dempster Shafer theory and a k-nearest neighbor classifier , 2009, 2009 4th International IEEE/EMBS Conference on Neural Engineering.

[17]  Mohammed Imamul Hassan Bhuiyan,et al.  Automatic sleep scoring using statistical features in the EMD domain and ensemble methods , 2016 .

[18]  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.

[19]  M. N. Shanmukha Swamy,et al.  Morphology-Based Automatic Seizure Detector for Intracerebral EEG Recordings , 2012, IEEE Transactions on Biomedical Engineering.

[20]  Ram Bilas Pachori,et al.  EEG Signal Classification Using Empirical Mode Decomposition and Support Vector Machine , 2011, SocProS.

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

[22]  Amin Janghorbani,et al.  EEG-based emotion recognition using Recurrence Plot analysis and K nearest neighbor classifier , 2013, 2013 20th Iranian Conference on Biomedical Engineering (ICBME).

[23]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.

[24]  S. M. Shafiul Alam,et al.  Detection of epileptic seizures using chaotic and statistical features in the EMD domain , 2011, 2011 Annual IEEE India Conference.

[25]  Dimitrios I. Fotiadis,et al.  Epileptic Seizure Detection in EEGs Using Time–Frequency Analysis , 2009, IEEE Transactions on Information Technology in Biomedicine.

[26]  Anindya Bijoy Das,et al.  Discrimination of scalp EEG signals in wavelet transform domain and channel selection for the patient-invariant seizure detection , 2015, 2015 International Conference on Electrical & Electronic Engineering (ICEEE).

[27]  M. N. Shanmukha Swamy,et al.  Model-Based Seizure Detection for Intracranial EEG Recordings , 2012, IEEE Transactions on Biomedical Engineering.

[28]  Mohammed Imamul Hassan Bhuiyan,et al.  Computer-aided sleep staging using Complete Ensemble Empirical Mode Decomposition with Adaptive Noise and bootstrap aggregating , 2016, Biomed. Signal Process. Control..

[29]  Ram Bilas Pachori,et al.  Classification of Seizure and Nonseizure EEG Signals Using Empirical Mode Decomposition , 2012, IEEE Transactions on Information Technology in Biomedicine.

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

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

[32]  Weidong Zhou,et al.  Automatic Seizure Detection Using Wavelet Transform and SVM in Long-Term Intracranial EEG , 2012, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[33]  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.

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

[35]  A. See,et al.  A study on sleep EEG Using sample entropy and power spectrum analysis , 2011, 2011 Defense Science Research Conference and Expo (DSR).

[36]  Jianfeng Hu,et al.  Classification of Motor Imagery EEG Signals Based on Energy Entropy , 2009, 2009 International Symposium on Intelligent Ubiquitous Computing and Education.

[37]  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.

[38]  Yann LeCun,et al.  Comparing SVM and convolutional networks for epileptic seizure prediction from intracranial EEG , 2008, 2008 IEEE Workshop on Machine Learning for Signal Processing.

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

[40]  Wahidah Mansor,et al.  Wavelet packet analysis of EEG signals from dyslexic children with writing disability , 2013, 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

[41]  Anindya Bijoy Das,et al.  A subband correlation-based method for the automatic detection of epilepsy and seizure in the dual tree complex wavelet transform domain , 2014, 2014 IEEE Conference on Biomedical Engineering and Sciences (IECBES).

[42]  Guoliang Chang,et al.  Wavelet Packet Entropy in the Analysis of EEG Signals , 2006, 2006 8th international Conference on Signal Processing.

[43]  Mohammed Imamul Hassan Bhuiyan,et al.  Detection of Seizure and Epilepsy Using Higher Order Statistics in the EMD Domain , 2013, IEEE Journal of Biomedical and Health Informatics.

[44]  R. Panda,et al.  Classification of EEG signal using wavelet transform and support vector machine for epileptic seizure diction , 2010, 2010 International Conference on Systems in Medicine and Biology.

[45]  Celia Shahnaz,et al.  Denoising of ECG signals based on noise reduction algorithms in EMD and wavelet domains , 2012, Biomed. Signal Process. Control..