An algorithm for on-line detection of high frequency oscillations related to epilepsy

Recent studies suggest that the appearance of signals with high frequency oscillations components in specific regions of the brain is related to the incidence of epilepsy. These oscillations are in general small in amplitude and short in duration, making them difficult to identify. The analysis of these oscillations are particularly important in epilepsy and their study could lead to the development of better medical treatments. Therefore, the development of algorithms for detection of these high frequency oscillations is of great importance. In this work, a new algorithm for automatic detection of high frequency oscillations is presented. This algorithm uses approximate entropy and artificial neural networks to extract features in order to detect and classify high frequency components in electrophysiological signals. In contrast to the existing algorithms, the one proposed here is fast and accurate, and can be implemented on-line, thus reducing the time employed to analyze the experimental electrophysiological signals.

[1]  Roger D. Traub,et al.  A Model of High-Frequency Ripples in the Hippocampus Based on Synaptic Coupling Plus Axon–Axon Gap Junctions between Pyramidal Neurons , 2000, The Journal of Neuroscience.

[2]  Alejandro F. Bujan,et al.  High-Frequency Network Activity, Global Increase in Neuronal Activity, and Synchrony Expansion Precede Epileptic Seizures In Vitro , 2010, The Journal of Neuroscience.

[3]  M. Steriade,et al.  Neocortical very fast oscillations (ripples, 80-200 Hz) during seizures: intracellular correlates. , 2003, Journal of neurophysiology.

[4]  M. Steriade.  Neuronal Substrates of Sleep and Epilepsy , 2003 .

[5]  Patrick R Hof,et al.  Gap junctions on hippocampal mossy fiber axons demonstrated by thin-section electron microscopy and freeze–fracture replica immunogold labeling , 2007, Proceedings of the National Academy of Sciences.

[6]  George Manis,et al.  Fast computation of approximate entropy , 2008, Comput. Methods Programs Biomed..

[7]  Brian Litt,et al.  Human and automated detection of high-frequency oscillations in clinical intracranial EEG recordings , 2007, Clinical Neurophysiology.

[8]  S M Pincus,et al.  Approximate entropy as a measure of system complexity. , 1991, Proceedings of the National Academy of Sciences of the United States of America.

[9]  John G. R. Jefferys,et al.  Epileptic high-frequency network activity in a model of non-lesional temporal lobe epilepsy , 2010, Brain : a journal of neurology.

[10]  Sheng-Fu Liang,et al.  Fast computation of sample entropy and approximate entropy in biomedicine , 2011, Comput. Methods Programs Biomed..

[11]  Andrew J. Trevelyan,et al.  Cellular mechanisms of high frequency oscillations in epilepsy: On the diverse sources of pathological activities , 2011, Epilepsy Research.

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

[13]  Simon Haykin,et al.  Nonlinear Sequential State Estimation for Solving PatternClassification Problems , 2010 .

[14]  R. Traub,et al.  A nonsynaptic mechanism underlying interictal discharges in human epileptic neocortex , 2009, Proceedings of the National Academy of Sciences.

[15]  Simon Haykin,et al.  Nonlinear Bayesian Filters for Training Recurrent Neural Networks , 2008, MICAI.

[16]  Roger D. Traub,et al.  Chemical synaptic and gap junctional interactions between principal neurons: Partners in epileptogenesis , 2011, Neural Networks.

[17]  F. Mormann,et al.  Nonlinear EEG analysis in epilepsy: its possible use for interictal focus localization, seizure anticipation, and prevention. , 2001, Journal of clinical neurophysiology : official publication of the American Electroencephalographic Society.

[18]  B. Litt,et al.  High-frequency oscillations in human temporal lobe: simultaneous microwire and clinical macroelectrode recordings. , 2008, Brain : a journal of neurology.

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

[20]  S. Haykin,et al.  Cubature Kalman Filters , 2009, IEEE Transactions on Automatic Control.

[21]  Anatol Bragin,et al.  High-frequency oscillations and other electrophysiological biomarkers of epilepsy: underlying mechanisms. , 2011, Biomarkers in medicine.

[22]  Kevin J. Staley,et al.  Mechanisms of Fast Ripples in the Hippocampus , 2004, The Journal of Neuroscience.

[23]  G. Buzsáki,et al.  Sharp wave-associated high-frequency oscillation (200 Hz) in the intact hippocampus: network and intracellular mechanisms , 1995, The Journal of neuroscience : the official journal of the Society for Neuroscience.

[24]  J. Parra,et al.  Epilepsies as Dynamical Diseases of Brain Systems: Basic Models of the Transition Between Normal and Epileptic Activity , 2003, Epilepsia.

[25]  Ayako Ochi,et al.  Focal resection of fast ripples on extraoperative intracranial EEG improves seizure outcome in pediatric epilepsy , 2011, Epilepsia.

[26]  G. Buzsáki.  Rhythms of the brain , 2006 .

[27]  W. Stacey,et al.  Technology Insight: neuroengineering and epilepsy—designing devices for seizure control , 2008, Nature Clinical Practice Neurology.

[28]  R. Traub,et al.  High-frequency population oscillations are predicted to occur in hippocampal pyramidal neuronal networks interconnected by axoaxonal gap junctions , 1999, Neuroscience.

[29]  Farshad Kheiri,et al.  Further evidence that pathologic high‐frequency oscillations are bursts of population spikes derived from recordings of identified cells in dentate gyrus , 2011, Epilepsia.

[30]  Keiko Usui,et al.  Very high frequency oscillations (over 1000Hz) in human epilepsy , 2010, Clinical Neurophysiology.

[31]  Guglielmo Foffani,et al.  Reduced Spike-Timing Reliability Correlates with the Emergence of Fast Ripples in the Rat Epileptic Hippocampus , 2007, Neuron.

[32]  M. Steriade,et al.  Focal synchronization of ripples (80-200 Hz) in neocortex and their neuronal correlates. , 2001, Journal of neurophysiology.

[33]  G. Buzsáki.  Two-stage model of memory trace formation: A role for “noisy” brain states , 1989, Neuroscience.

[34]  I. Módy,et al.  High-frequency oscillations : What is normal and what is not ? , 2008 .

[35]  Fernando Lopes da Silva,et al.  Comprar Niedermeyer's Electroencephalography, 6/e (Basic Principles, Clinical Applications, and Related Fields ) | Fernando Lopes Da Silva | 9780781789424 | Lippincott Williams & Wilkins , 2010 .

[36]  J. Jefferys,et al.  Nonsynaptic modulation of neuronal activity in the brain: electric currents and extracellular ions. , 1995, Physiological reviews.

[37]  Charles L. Wilson,et al.  Quantitative analysis of high-frequency oscillations (80-500 Hz) recorded in human epileptic hippocampus and entorhinal cortex. , 2002, Journal of neurophysiology.

[38]  Charles L. Wilson,et al.  Hippocampal and Entorhinal Cortex High‐Frequency Oscillations (100–500 Hz) in Human Epileptic Brain and in Kainic Acid‐Treated Rats with Chronic Seizures , 1999, Epilepsia.