Performance Analysis of Entropy Methods in Detecting Epileptic Seizure from Surface Electroencephalograms

Physiological signals like Electrocardiography (ECG) and Electroencephalography (EEG) are complex and nonlinear in nature. To retrieve diagnostic information from these, we need the help of nonlinear methods of analysis. Entropy estimation is a very popular approach in the nonlinear category, where entropy estimates are used as features for signal classification and analysis. In this study, we analyze and compare the performances of four entropy methods; namely Distribution entropy (DistEn), Shannon entropy (ShanEn), Renyi entropy (RenEn) and LempelZiv complexity (LempelZiv) as classification features to detect epileptic seizure (ES) from surface Electroencephalography (sEEG) signal. Experiments were conducted on sEEG data from 23 subjects, obtained from the CHB-MIT database of PhysioNet. ShanEn, RenEn and LempelZiv entropy are found to be potential features for accurate and consistent detection of ES from sEEG, across multiple channels and subjects.

[1]  Manish Sharma,et al.  A NOVEL APPROACH FOR EPILEPSY DETECTION USING TIME–FREQUENCY LOCALIZED BI-ORTHOGONAL WAVELET FILTER , 2019, Journal of Mechanics in Medicine and Biology.

[2]  U. Rajendra Acharya,et al.  Characterization of focal EEG signals: A review , 2019, Future Gener. Comput. Syst..

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

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

[5]  João Luís Garcia Rosa,et al.  Classification for EEG report generation and epilepsy detection , 2019, Neurocomputing.

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

[7]  Sang Joon Kim,et al.  A Mathematical Theory of Communication , 2006 .

[8]  Marimuthu Palaniswami,et al.  Detection of epileptic seizure based on entropy analysis of short-term EEG , 2018, PloS one.

[9]  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.

[10]  R. Acharya U,et al.  Nonlinear analysis of EEG signals at different mental states , 2004, Biomedical engineering online.

[11]  C B Dodrill,et al.  Is the left cerebral hemisphere more prone to epileptogenesis than the right? , 2001, Epileptic disorders : international epilepsy journal with videotape.

[12]  G. Crooks.  On Measures of Entropy and Information , 2015 .

[13]  R. Hornero,et al.  Non-linear Analysis of Intracranial Electroencephalogram Recordings with Approximate Entropy and Lempel-Ziv Complexity for Epileptic Seizure Detection , 2007, 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[14]  Dingchang Zheng,et al.  Assessing the complexity of short-term heartbeat interval series by distribution entropy , 2014, Medical & Biological Engineering & Computing.

[15]  Deng-Shan Shiau,et al.  Predictability Analysis for an Automated Seizure Prediction Algorithm , 2006, Journal of clinical neurophysiology : official publication of the American Electroencephalographic Society.

[16]  J. E. Jacob,et al.  Epileptic seizure detection using non linear analysis of EEG , 2016, 2016 International Conference on Inventive Computation Technologies (ICICT).

[17]  Taiyong Li,et al.  Detecting Epileptic Seizures in EEG Signals with Complementary Ensemble Empirical Mode Decomposition and Extreme Gradient Boosting , 2020, Entropy.

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

[19]  Si Thu Aung,et al.  Modified-Distribution Entropy as the Features for the Detection of Epileptic Seizures , 2020, Frontiers in Physiology.