Permutation entropy of scalp EEG: A tool to investigate epilepsies Suggestions from absence epilepsies

OBJECTIVE We used permutation entropy (PE) to disclose abnormalities of cerebral activity in patients with typical absences (TAs). METHODS We evaluated 24 EEG of TA patients and 40 EEG of healthy subjects. PE was estimated channel by channel, with electrodes being divided into high-PE cluster (high randomness), low-PE cluster (low randomness), and neutral cluster. We compared PE between EEG of patients and controls, and between interictal and ictal EEG of patients. RESULTS Patients showed a recurrent behavior of PE topography, with anterior brain regions constantly associated to high PE levels and posterior brain regions constantly associated to low PE levels, during both interictal and ictal phases. On the contrary, healthy controls had a random distribution of PE topography. CONCLUSIONS In patients with TAs, a higher randomness in fronto-temporal areas and a lower randomness in posterior areas occur during both interictal and ictal phases. Such abnormalities are in keeping with evidences from different morphological and functional studies showing multifocal brain changes in TA patients. SIGNIFICANCE PE seems to be a useful tool to disclose abnormalities of cerebral electric activity not revealed by conventional EEG recordings, opening interesting prospective for future studies.

[1]  C P Panayiotopoulos,et al.  Differentiation of typical absence seizures in epileptic syndromes. A video EEG study of 224 seizures in 20 patients. , 1989, Brain : a journal of neurology.

[2]  S L Free,et al.  Quantitative MRI in patients with idiopathic generalized epilepsy. Evidence of widespread cerebral structural changes. , 1997, Brain : a journal of neurology.

[3]  G L Shulman,et al.  INAUGURAL ARTICLE by a Recently Elected Academy Member:A default mode of brain function , 2001 .

[4]  D. Kim,et al.  Spectral Power of 1–4 Hz Frequency in the Ictal Phase of Childhood Absence Epilepsy , 2011, Journal of clinical neurophysiology : official publication of the American Electroencephalographic Society.

[5]  Alexandre Andrade,et al.  Correlation Dimension Maps of EEG from Epileptic Absences , 1999, Brain Topography.

[6]  Francesca Benuzzi,et al.  Increased cortical BOLD signal anticipates generalized spike and wave discharges in adolescents and adults with idiopathic generalized epilepsies , 2012, Epilepsia.

[7]  Frederick Andermann,et al.  Cortical triggers in generalized reflex seizures and epilepsies. , 2005, Brain : a journal of neurology.

[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]  S. P. Lloyd,et al.  Least squares quantization in PCM , 1982, IEEE Trans. Inf. Theory.

[10]  J. Gotman,et al.  Absence seizures: Individual patterns revealed by EEG‐fMRI , 2010, Epilepsia.

[11]  P. Ossenblok,et al.  Onset and propagation of spike and slow wave discharges in human absence epilepsy: A MEG study , 2009, Epilepsia.

[12]  F. H. Lopes da Silva,et al.  Evolving concepts on the pathophysiology of absence seizures: the cortical focus theory. , 2005, Archives of neurology.

[13]  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.

[14]  O. Blanke,et al.  Distinct Behavioral and EEG Topographic Correlates of Loss of Consciousness in Absences , 2000, Epilepsia.

[15]  D. Janz,et al.  Neuropathological Findings in Primary Generalized Epilepsy: A Study of Eight Cases , 1984, Epilepsia.

[16]  F. Woermann,et al.  Abnormal cerebral structure in juvenile myoclonic epilepsy demonstrated with voxel-based analysis of MRI. , 1999, Brain : a journal of neurology.

[17]  I Savic,et al.  MR Spectroscopy Shows Reduced Frontal Lobe Concentrations of N‐Acetyl Aspartate in Patients with Juvenile Myoclonic Epilepsy , 2000, Epilepsia.

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

[19]  I. Scheffer,et al.  Electroclinical features of absence seizures in childhood absence epilepsy , 2006, Neurology.

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

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

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

[23]  Suresh Gurbani,et al.  Frontal and temporal volumes in Childhood Absence Epilepsy , 2009, Epilepsia.

[24]  D. Tsiptsios,et al.  Focal and generalized EEG paroxysms in childhood absence epilepsy: Topographic associations and distinctive behaviors during the first cycle of non‐REM sleep , 2012, Epilepsia.

[25]  Richard N. Harner,et al.  18 – Clinical Application of Computed EEG Topography , 1986 .

[26]  D. Tucker,et al.  Are “Generalized” Seizures Truly Generalized? Evidence of Localized Mesial Frontal and Frontopolar Discharges in Absence , 2004, Epilepsia.

[27]  Arnaud Delorme,et al.  EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis , 2004, Journal of Neuroscience Methods.

[28]  J. Duncan,et al.  Positron emission tomography in idiopathic generalized epilepsy: imaging beyond structure , 2000 .

[29]  B. Clemens,et al.  EEG frequency profiles of idiopathic generalised epilepsy syndromes , 2000, Epilepsy Research.

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