A Novel Approach for Real-Time Recognition of Epileptic Seizures Using Minimum Variance Modified Fuzzy Entropy

Objective: Validation of epileptic seizures annotations from long-term electroencephalogram (EEG) recordings is a tough and tedious task for the neurological community. It is a well-known fact that computerized qualitative methods thoroughly assess the complex brain dynamics toward seizure detection and proven as one of the acceptable clinical indicators. Methods: This research study suggests a novel approach for real-time recognition of epileptic seizure from EEG recordings by a technique referred as minimum variance modified fuzzy entropy (MVMFzEn). Multichannel EEG recordings of 4.36 h of epileptic seizures and 25.74 h of normal EEG were considered. Signal processing techniques such as filters and independent component analysis were appropriated to reduce noise and artifacts. Unlike, the predefined fuzzy membership function, the modified fuzzy entropy utilizes relative energy as a membership function followed by scaling operation to obtain the feature. Results: Results revealed that MVMFzEn drops abruptly during an epileptic activity and this fact was used to set a threshold. An automated threshold derived from MVMFzEn assesses the classification efficiency of the given data during validation. It was observed from the results that the proposed method yields a classification accuracy of 100% without the use of any classifier. Conclusion: The graphical user interface was designed in MATLAB to automatically label the normal and epileptic segments in the long-term EEG recordings. Significance: The ground truth clinical validation using validation specificity and validation sensitivity confirms the suitability of the proposed technique for automated annotation of epileptic seizures in real time.

[1]  Pasi Luukka,et al.  Feature selection using fuzzy entropy measures with similarity classifier , 2011, Expert Syst. Appl..

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

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

[4]  Natarajan Sriraam,et al.  Classification of Focal and Non Focal Epileptic Seizures Using Multi-Features and SVM Classifier , 2017, Journal of Medical Systems.

[5]  Mohamed Adel Serhani,et al.  An automatic mobile-health based approach for EEG epileptic seizures detection , 2015, Expert Syst. Appl..

[6]  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.

[7]  Natarajan Sriraam,et al.  Optimal configuration of multilayer perceptron neural network classifier for recognition of intracranial epileptic seizures , 2017, Expert Syst. Appl..

[8]  Joseph Picone,et al.  The Temple University Hospital EEG Data Corpus , 2016, Front. Neurosci..

[9]  Duoqian Miao,et al.  Best basis-based wavelet packet entropy feature extraction and hierarchical EEG classification for epileptic detection , 2011, Expert Syst. Appl..

[10]  Antony Selvadoss Thanamani,et al.  An Efficient Feature Selection Technique using Supervised Fuzzy Information Theory , 2014 .

[11]  C. A. Teixeira,et al.  EPILAB: A software package for studies on the prediction of epileptic seizures , 2011, Journal of Neuroscience Methods.

[12]  Arun Kumar Majumdar,et al.  Feature Selection for Automatic Burst Detection in Neonatal Electroencephalogram , 2011, IEEE Journal on Emerging and Selected Topics in Circuits and Systems.

[13]  Jiang Wang,et al.  Investigation of EEG abnormalities in the early stage of Parkinson’s disease , 2013, Cognitive Neurodynamics.

[14]  Begoña Garcia-Zapirain,et al.  EEG artifact removal—state-of-the-art and guidelines , 2015, Journal of neural engineering.

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

[16]  Weidong Zhou,et al.  Epileptic Seizure Detection Using Lacunarity and Bayesian Linear Discriminant Analysis in Intracranial EEG , 2013, IEEE Transactions on Biomedical Engineering.

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

[18]  Zeeshan Syed,et al.  Multi-task seizure detection: addressing intra-patient variation in seizure morphologies , 2016, Machine Learning.

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

[20]  Jean-Francois Cardoso,et al.  Blind signal separation: statistical principles , 1998, Proc. IEEE.

[21]  S. Vorobyov.  APPLICATION OF ICA FOR AUTOMATIC NOISE AND INTERFERENCE CANCELLATION IN MULTISENSORY BIOMEDICAL SIGNALS , 2000 .

[22]  N Sriraam,et al.  Classification of epileptic seizures using wavelet packet log energy and norm entropies with recurrent Elman neural network classifier , 2016, Cognitive Neurodynamics.

[23]  Reza Fazel-Rezai,et al.  A Fuzzy Logic System for Seizure Onset Detection in Intracranial EEG , 2012, Comput. Intell. Neurosci..

[24]  Tapio Saramäki,et al.  Long-term epileptic EEG classification via 2D mapping and textural features , 2015, Expert Syst. Appl..

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

[26]  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.

[27]  Evangelia Pippa,et al.  Improving classification of epileptic and non-epileptic EEG events by feature selection , 2016, Neurocomputing.

[28]  Danny M. W. Hilkman,et al.  Optimal training dataset composition for SVM-based, age-independent, automated epileptic seizure detection , 2016, Medical & Biological Engineering & Computing.

[29]  Weidong Zhou,et al.  Epileptic seizure detection based on improved wavelet neural networks in long-term intracranial EEG , 2016 .

[30]  Amir Rastegarnia,et al.  A Wavelet-Based Artifact Reduction From Scalp EEG for Epileptic Seizure Detection , 2016, IEEE Journal of Biomedical and Health Informatics.

[31]  M. T. Lamata,et al.  Entropy measures associated with a fuzzy basic probability assignment , 1997, Proceedings of 6th International Fuzzy Systems Conference.

[32]  Fakhri Karray,et al.  Fuzzy entropy: a brief survey , 2001, 10th IEEE International Conference on Fuzzy Systems. (Cat. No.01CH37297).

[33]  David Cuesta-Frau,et al.  Comparative Study of Entropy Sensitivity to Missing Biosignal Data , 2014, Entropy.

[34]  M. L. Dewal,et al.  Epileptic seizures detection in EEG using DWT-based ApEn and artificial neural network , 2012, Signal, Image and Video Processing.

[35]  Oleksandr Makeyev,et al.  High-Frequency Oscillations Recorded on the Scalp of Patients With Epilepsy Using Tripolar Concentric Ring Electrodes , 2014, IEEE Journal of Translational Engineering in Health and Medicine.

[36]  Settimo Termini,et al.  A Definition of a Nonprobabilistic Entropy in the Setting of Fuzzy Sets Theory , 1972, Inf. Control..