Interictal epileptic discharge EEG detection based on wavelet and multiresolution analysis

Epileptologists use interictal epileptic discharge (lED) as a marker for epilepsy. The present conventional method to distinguish normal and I ED by an epileptologist's visual screening is tedious and operator dependent. The focus of this paper is to distinguish normal and IED in clinically recorded electroencephalogram (EEG) using discrete wavelet transform. Wavelet multiresolution analysis has been adopted in this study looking into wavelet energy, wavelet entropy and amplitude dispersion in every sub-band. The extracted features were classified using support vector machine (SVM). EEG data were obtained from both online database and Pusat Perubatan Universiti Kebangsaan Malaysia (PPUKM) Neurology database. The ability of the proposed algorithm in detecting the presence of IED is 96.5% of accuracy, 100% of sensitivity and 95.5% of specificity. The algorithm has good potential to be used in clinical practice for IED detection with validation against the present clinical detection method.

[1]  Belaid Bouikhalene,et al.  Epilepsy Seizure Detection Using Autoregressive Modelling and Multiple Layer Perceptron Neural Network , 2015 .

[2]  H. Khamis,et al.  Frequency–moment signatures: A method for automated seizure detection from scalp EEG , 2013, Clinical Neurophysiology.

[3]  A. Navia-Vazquez,et al.  EEG Signal Processing for Epilepsy , 2012 .

[4]  C. Torrence,et al.  A Practical Guide to Wavelet Analysis. , 1998 .

[5]  A. Tzallas,et al.  Automated Epileptic Seizure Detection Methods: A Review Study , 2012 .

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

[7]  Jason Weston,et al.  A user's guide to support vector machines. , 2010, Methods in molecular biology.

[8]  Samir Avdakovic,et al.  Energy Distribution of EEG Signals: EEG Signal Wavelet-Neural Network Classifier , 2010, ArXiv.

[9]  C. Elger,et al.  Epileptic Seizures and Epilepsy: Definitions Proposed by the International League Against Epilepsy (ILAE) and the International Bureau for Epilepsy (IBE) , 2005, Epilepsia.

[10]  Shuli Huang,et al.  The Application of EEG related , 2011 .

[11]  S. Smith.  EEG in the diagnosis, classification, and management of patients with epilepsy , 2005, Journal of Neurology, Neurosurgery & Psychiatry.

[12]  Fathi E. Abd El-Samie,et al.  EEG seizure detection and prediction algorithms: a survey , 2014, EURASIP J. Adv. Signal Process..

[13]  Dimitrios I. Fotiadis,et al.  A Kalman filter based methodology for EEG spike enhancement , 2007, Comput. Methods Programs Biomed..

[14]  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.

[15]  F. Dudek,et al.  Interictal Spikes and Epileptogenesis , 2006, Epilepsy currents.

[16]  Yung-Nien Sun,et al.  Model-Based Spike Detection of Epileptic EEG Data , 2013, Sensors.

[17]  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.

[18]  Kenneth Sundaraj,et al.  Running head : EEG analysis of Parkinson ’ s disease during emotion processing 1 On the analysis of EEG power , frequency and asymmet ry in Parkinson ’ s disease during emotion processing , 2014 .

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

[20]  Shujuan Geng,et al.  EEG non-linear feature extraction using correlation dimension and Hurst exponent , 2011, Neurological research.

[21]  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.

[22]  Aneta Stefanovska,et al.  Reconstructing Time-Dependent Dynamics , 2016, Proceedings of the IEEE.

[23]  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.

[24]  Brian Litt,et al.  Continuous energy variation during the seizure cycle: towards an on-line accumulated energy , 2005, Clinical Neurophysiology.

[25]  Justin Dauwels,et al.  SpikeGUI: Software for rapid interictal discharge annotation via template matching and online machine learning , 2014, 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[26]  O. Farooq,et al.  Automated seizure detection in scalp EEG using multiple wavelet scales , 2012, 2012 IEEE International Conference on Signal Processing, Computing and Control.