Learning Vector Quantization and Permutation Entropy to analyse epileptic electroencephalography

In this paper, we address the issue of dealing with huge amounts of data from recordings of an Electroencephalogram (EEG) in epileptic patients. In particular, the attention is focused on the development of tools to support the neurophysiologists in the time consuming and challenging task of reviewing the EEG to identify critical events that are worth of inspection for diagnostic purposes. A novel methodology is proposed for the automatic estimation of descriptors of EEG complexity and the subsequent classification of critical events. Based on the estimation of Permutation Entropy (PE) profiles from the EEG traces, the methodology relies on Learning Vector Quantization (LVQ) to cluster the electrodes in a competitive way according to their PE levels and to classify the cerebral state accordingly. An absence seizure EEG of 15.5 minutes was processed and a 93.94% sensitivity together with a 100% specificity were obtained.

[1]  Teuvo Kohonen,et al.  Learning vector quantization , 1998 .

[2]  B. Pompe,et al.  Permutation entropy: a natural complexity measure for time series. , 2002, Physical review letters.

[3]  L M Hively,et al.  Detecting dynamical changes in time series using the permutation entropy. , 2004, Physical review. E, Statistical, nonlinear, and soft matter physics.

[4]  Roberto Hornero,et al.  Analysis of EEG background activity in Alzheimer's disease patients with Lempel-Ziv complexity and central tendency measure. , 2006, Medical engineering & physics.

[5]  F.C. Morabito,et al.  Visualization of the Short Term Maximum Lyapunov Exponent Topography in the Epileptic Brain , 2006, 2006 International Conference of the IEEE Engineering in Medicine and Biology Society.

[6]  G. Ouyang,et al.  Predictability analysis of absence seizures with permutation entropy , 2007, Epilepsy Research.

[7]  J. Sleigh,et al.  Permutation entropy of the electroencephalogram: a measure of anaesthetic drug effect. , 2008, British journal of anaesthesia.

[8]  N. Birbaumer,et al.  Permutation entropy to detect vigilance changes and preictal states from scalp EEG in epileptic patients. A preliminary study , 2008, Neurological Sciences.

[9]  Francesco Carlo Morabito,et al.  Clustering of entropy topography in epileptic electroencephalography , 2009, Neural Computing and Applications.

[10]  Nadia Mammone,et al.  Visualization and modelling of STLmax topographic brain activity maps , 2010, Journal of Neuroscience Methods.

[11]  Francesco Carlo Morabito,et al.  Clustering of entropy topography in epileptic electroencephalography , 2010, Neural Computing and Applications.

[12]  R. Hornero,et al.  Entropy and Complexity Analyses in Alzheimer’s Disease: An MEG Study , 2010, The open biomedical engineering journal.

[13]  Tarmo Lipping,et al.  Entropy of the EEG in transition to burst suppression in deep anesthesia: Surrogate analysis , 2010, 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology.

[14]  Zhenhu Liang,et al.  Multiscale permutation entropy analysis of EEG recordings during sevoflurane anesthesia , 2010, Journal of neural engineering.

[15]  Francesco Carlo Morabito,et al.  Analysis of absence seizure EEG via Permutation Entropy spatio-temporal clustering , 2011, The 2011 International Joint Conference on Neural Networks.

[16]  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.

[17]  Aura Silva,et al.  Performance of electroencephalogram-derived parameters in prediction of depth of anaesthesia in a rabbit model. , 2011, British journal of anaesthesia.

[18]  Aimé Lay-Ekuakille,et al.  Analysis of Absence Seizure Generation using EEG Spatial-Temporal Regularity Measures , 2012, Int. J. Neural Syst..

[19]  R. E. Madsen,et al.  Automatic detection of childhood absence epilepsy seizures: toward a monitoring device. , 2012, Pediatric neurology.

[20]  R. E. Madsen,et al.  Channel selection for automatic seizure detection , 2012, Clinical Neurophysiology.

[21]  Massimiliano Zanin,et al.  Permutation Entropy and Its Main Biomedical and Econophysics Applications: A Review , 2012, Entropy.

[22]  Julius Georgiou,et al.  Detection of epileptic electroencephalogram based on Permutation Entropy and Support Vector Machines , 2012, Expert Syst. Appl..

[23]  Francesco Carlo Morabito,et al.  Multivariate Multi-Scale Permutation Entropy for Complexity Analysis of Alzheimer's Disease EEG , 2012, Entropy.

[24]  Francesco Carlo Morabito,et al.  Measures of Brain Connectivity through Permutation Entropy in Epileptic Disorders , 2012, WIRN.

[25]  Jing Li,et al.  Dynamic characteristics of absence EEG recordings with multiscale permutation entropy analysis , 2013, Epilepsy Research.

[26]  Zhilin Zhang,et al.  EEG Complexity Modifications and Altered Compressibility in Mild Cognitive Impairment and Alzheimer's Disease , 2013, WIRN.

[27]  Francesco Carlo Morabito,et al.  Entropic Measures of EEG Complexity in Alzheimer's Disease Through a Multivariate Multiscale Approach , 2013, IEEE Sensors Journal.

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

[29]  Francesco Carlo Morabito,et al.  Enhanced Compressibility of EEG Signal in Alzheimer's Disease Patients , 2013, IEEE Sensors Journal.

[30]  Francesco Carlo Morabito,et al.  Permutation entropy of scalp EEG: A tool to investigate epilepsies Suggestions from absence epilepsies , 2014, Clinical Neurophysiology.

[31]  Diego M. Mateos,et al.  Permutation Entropy Applied to the Characterization of the Clinical Evolution of Epileptic Patients under PharmacologicalTreatment , 2014, Entropy.

[32]  Yan Li,et al.  Classifying epileptic EEG signals with delay permutation entropy and Multi-Scale K-means. , 2015, Advances in experimental medicine and biology.

[33]  Barbara Cimatti,et al.  Smart Innovation, Systems and Technologies , 2017 .