Multivariate Bayesian classification of epilepsy EEG signals

The classification of epileptic seizure events in EEG signals is an important problem in biomedical engineering. In this paper we propose a Bayesian classification method for multivariate EEG signals. The method is based on a multilevel 2D wavelet decomposition that captures the distribution of energy across the different brain rhythms and regions, coupled with a generalised Gaussian statistical representation and a multivariate Bayesian classification scheme. The proposed approach is demonstrated on a challenging paediatric dataset containing both epileptic events and normal brain function signals, where it outperforms a state-of-the-art method both in terms of classification sensitivity and specificity.

[1]  R. Ilmoniemi,et al.  Magnetoencephalography-theory, instrumentation, and applications to noninvasive studies of the working human brain , 1993 .

[2]  Robert J. Schalkoff,et al.  Standardized database development for EEG epileptiform transient detection: EEGnet scoring system and machine learning analysis , 2013, Journal of Neuroscience Methods.

[3]  Ernst Fernando Lopes Da Silva Niedermeyer,et al.  Electroencephalography, basic principles, clinical applications, and related fields , 1982 .

[4]  Pablo Valenti,et al.  Automatic detection of interictal spikes using data mining models , 2006, Journal of Neuroscience Methods.

[5]  P. Laguna,et al.  Signal Processing , 2002, Yearbook of Medical Informatics.

[6]  Jean-Yves Tourneret,et al.  Parameter Estimation For Multivariate Generalized Gaussian Distributions , 2013, IEEE Transactions on Signal Processing.

[7]  Ali Shoeb,et al.  Patient-specific seizure onset detection. , 2004, Epilepsy & behavior : E&B.

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

[9]  Mark J. Cook,et al.  Epileptic Seizures and the EEG: Measurement, Models, Detection and Prediction , 2010 .

[10]  V. Samar,et al.  Wavelet Analysis of Neuroelectric Waveforms: A Conceptual Tutorial , 1999, Brain and Language.

[11]  Minh N. Do,et al.  Wavelet-based texture retrieval using generalized Gaussian density and Kullback-Leibler distance , 2002, IEEE Trans. Image Process..

[12]  S. Mallat.  A wavelet tour of signal processing , 1998 .

[13]  H. Adeli,et al.  Analysis of EEG records in an epileptic patient using wavelet transform , 2003, Journal of Neuroscience Methods.

[14]  J. Gotman,et al.  A system for automatic artifact removal in ictal scalp EEG based on independent component analysis and Bayesian classification , 2006, Clinical Neurophysiology.

[15]  Hadj Batatia,et al.  A new algorithm for epilepsy seizure onset detection and spread estimation from EEG signals , 2016 .

[16]  Hadj Batatia,et al.  Detection of onset in epilepsy signals using generalized Gaussian distribution , 2014 .

[17]  B. Schelter.  Seizure Prediction in Epilepsy: From Basic Mechanisms to Clinical Applications , 2008 .

[18]  Nir Friedman,et al.  Bayesian Network Classifiers , 1997, Machine Learning.

[19]  Jeffrey M. Hausdorff,et al.  Physionet: Components of a New Research Resource for Complex Physiologic Signals". Circu-lation Vol , 2000 .

[20]  Andrew Nicolson,et al.  Atlas of Epilepsy , 2003 .

[21]  Christopher M. Bishop,et al.  Pattern Recognition and Machine Learning (Information Science and Statistics) , 2006 .

[22]  S. Nadarajah.  A generalized normal distribution , 2005 .

[23]  J. Gotman,et al.  A system to detect the onset of epileptic seizures in scalp EEG , 2005, Clinical Neurophysiology.

[24]  Anil K. Jain.  Fundamentals of Digital Image Processing , 2018, Control of Color Imaging Systems.