Tunable-Q Wavelet Transform Based Multivariate Sub-Band Fuzzy Entropy with Application to Focal EEG Signal Analysis

This paper analyses the complexity of multivariate electroencephalogram (EEG) signals in different frequency scales for the analysis and classification of focal and non-focal EEG signals. The proposed multivariate sub-band entropy measure has been built based on tunable-Q wavelet transform (TQWT). In the field of multivariate entropy analysis, recent studies have performed analysis of biomedical signals with a multi-level filtering approach. This approach has become a useful tool for measuring inherent complexity of the biomedical signals. However, these methods may not be well suited for quantifying the complexity of the individual multivariate sub-bands of the analysed signal. In this present study, we have tried to resolve this difficulty by employing TQWT for analysing the sub-band signals of the analysed multivariate signal. It should be noted that higher value of Q factor is suitable for analysing signals with oscillatory nature, whereas the lower value of Q factor is suitable for analysing signals with non-oscillatory transients in nature. Moreover, with an increased number of sub-bands and a higher value of Q-factor, a reasonably good resolution can be achieved simultaneously in high and low frequency regions of the considered signals. Finally, we have employed multivariate fuzzy entropy (mvFE) to the multivariate sub-band signals obtained from the analysed signal. The proposed Q-based multivariate sub-band entropy has been studied on the publicly available bivariate Bern Barcelona focal and non-focal EEG signals database to investigate the statistical significance of the proposed features in different time segmented signals. Finally, the features are fed to random forest and least squares support vector machine (LS-SVM) classifiers to select the best classifier. Our method has achieved the highest classification accuracy of 84.67% in classifying focal and non-focal EEG signals with LS-SVM classifier. The proposed multivariate sub-band fuzzy entropy can also be applied to measure complexity of other multivariate biomedical signals.

[1]  Danilo P Mandic,et al.  Multivariate multiscale entropy: a tool for complexity analysis of multichannel data. , 2011, Physical review. E, Statistical, nonlinear, and soft matter physics.

[2]  J. Richman,et al.  Physiological time-series analysis using approximate entropy and sample entropy. , 2000, American journal of physiology. Heart and circulatory physiology.

[3]  Hamed Azami,et al.  MEMD-enhanced multivariate fuzzy entropy for the evaluation of complexity in biomedical signals , 2016, 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

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

[5]  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..

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

[7]  Ram Bilas Pachori,et al.  Discrimination between Ictal and Seizure-Free EEG Signals Using Empirical Mode Decomposition , 2008, J. Electr. Comput. Eng..

[8]  Pradip Sircar,et al.  A novel approach for automated detection of focal EEG signals using empirical wavelet transform , 2016, Neural Computing and Applications.

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

[10]  Ram Bilas Pachori,et al.  Cross-terms reduction in the Wigner-Ville distribution using tunable-Q wavelet transform , 2016, Signal Process..

[11]  Christian Vollmar,et al.  Clinical MRI in children and adults with focal epilepsy: A critical review , 2009, Epilepsy & Behavior.

[12]  A. Mees,et al.  Dynamics from multivariate time series , 1998 .

[13]  Li Zhang,et al.  Wavelet support vector machine , 2004, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[14]  Pradip Sircar,et al.  EEG signal analysis using FB expansion and second-order linear TVAR process , 2008, Signal Process..

[15]  Ron Kohavi,et al.  A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection , 1995, IJCAI.

[16]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[17]  William J. Wilson,et al.  Statistical methods , 1993 .

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

[19]  Yu. Pogoreltsev,et al.  The Application , 2020, How to Succeed in the Academic Clinical Interview.

[20]  A. Vannucci,et al.  BICS Bath Institute for Complex Systems A note on time-dependent DiPerna-Majda measures , 2008 .

[21]  Dingchang Zheng,et al.  Analysis of heart rate variability using fuzzy measure entropy , 2013, Comput. Biol. Medicine.

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

[23]  Weiting Chen,et al.  Measuring complexity using FuzzyEn, ApEn, and SampEn. , 2009, Medical engineering & physics.

[24]  N. Huang,et al.  The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis , 1998, Proceedings of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences.

[25]  M G Marciani,et al.  Lateralization of the epileptogenic focus by computerized EEG study and neuropsychological evaluation. , 1992, The International journal of neuroscience.

[26]  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.

[27]  Ram Bilas Pachori,et al.  Automatic diagnosis of septal defects based on tunable-Q wavelet transform of cardiac sound signals , 2015, Expert Syst. Appl..

[28]  Johan A. K. Suykens,et al.  Least Squares Support Vector Machine Classifiers , 1999, Neural Processing Letters.

[29]  U. Rajendra Acharya,et al.  Automated EEG analysis of epilepsy: A review , 2013, Knowl. Based Syst..

[30]  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.

[31]  Sandipan Pati,et al.  Pharmacoresistant epilepsy: From pathogenesis to current and emerging therapies , 2010, Cleveland Clinic Journal of Medicine.

[32]  Wangxin Yu,et al.  Characterization of Surface EMG Signal Based on Fuzzy Entropy , 2007, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[33]  Ram Bilas Pachori,et al.  Classification of cardiac sound signals using constrained tunable-Q wavelet transform , 2014, Expert Syst. Appl..

[34]  U. Rajendra Acharya,et al.  An automatic detection of focal EEG signals using new class of time-frequency localized orthogonal wavelet filter banks , 2017, Knowl. Based Syst..

[35]  Ram Bilas Pachori,et al.  egmentation of cardiac sound signals by removing murmurs using onstrained tunable-Q wavelet transform , 2013 .

[36]  U. Rajendra Acharya,et al.  Automated diagnosis of coronary artery disease using tunable-Q wavelet transform applied on heart rate signals , 2015, Knowl. Based Syst..

[37]  Jin Chen,et al.  Feature extraction of rolling bearing’s early weak fault based on EEMD and tunable Q-factor wavelet transform , 2014 .

[38]  B. Litt,et al.  High-frequency oscillations and seizure generation in neocortical epilepsy. , 2004, Brain : a journal of neurology.

[39]  Ivan W. Selesnick,et al.  Wavelet Transform With Tunable Q-Factor , 2011, IEEE Transactions on Signal Processing.

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

[41]  J Gotman,et al.  Asymmetry in delta activity in patients with focal epilepsy. , 1990, Electroencephalography and clinical neurophysiology.

[42]  Ram Bilas Pachori,et al.  A Multivariate Approach for Patient-Specific EEG Seizure Detection Using Empirical Wavelet Transform , 2017, IEEE Transactions on Biomedical Engineering.

[43]  U. Rajendra Acharya,et al.  An efficient automated technique for CAD diagnosis using flexible analytic wavelet transform and entropy features extracted from HRV signals , 2016, Expert Syst. Appl..

[44]  J Gutiérrez,et al.  Analysis and localization of epileptic events using wavelet packets. , 2001, Medical engineering & physics.

[45]  H. Azami,et al.  Refined composite multivariate generalized multiscale fuzzy entropy: A tool for complexity analysis of multichannel signals , 2017 .

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

[47]  Shoushui Wei,et al.  Determination of Sample Entropy and Fuzzy Measure Entropy Parameters for Distinguishing Congestive Heart Failure from Normal Sinus Rhythm Subjects , 2015, Entropy.

[48]  Ming Liang,et al.  A kurtosis-guided adaptive demodulation technique for bearing fault detection based on tunable-Q wavelet transform , 2013 .

[49]  U. Rajendra Acharya,et al.  An integrated alcoholic index using tunable-Q wavelet transform based features extracted from EEG signals for diagnosis of alcoholism , 2017, Appl. Soft Comput..

[50]  Jinde Zheng,et al.  A rolling bearing fault diagnosis approach based on LCD and fuzzy entropy , 2013 .

[51]  Shoushui Wei,et al.  Multivariable Fuzzy Measure Entropy Analysis for Heart Rate Variability and Heart Sound Amplitude Variability , 2016, Entropy.

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

[53]  Brian Litt,et al.  Special issue on epileptic seizure prediction , 2003, IEEE Trans. Biomed. Eng..

[54]  Danilo P. Mandic,et al.  Multivariate Multiscale Entropy Analysis , 2012, IEEE Signal Processing Letters.

[55]  Ron Kohavi,et al.  Wrappers for Feature Subset Selection , 1997, Artif. Intell..

[56]  Jérôme Gilles,et al.  Empirical Wavelet Transform , 2013, IEEE Transactions on Signal Processing.

[57]  Ian H. Witten,et al.  The WEKA data mining software: an update , 2009, SKDD.