Seizure Prediction and Detection via Phase and Amplitude Lock Values

A robust seizure prediction methodology would enable a “closed-loop” system that would only activate as impending seizure activity is detected. Such a system would eliminate ongoing stimulation to the brain, thereby eliminating such side effects as coughing, hoarseness, voice alteration, and paresthesias (Murphy et al., 1998; Ben-Menachem, 2001), while preserving overall battery life of the system. The seizure prediction and detection algorithm uses Phase/Amplitude Lock Values (PLV/ALV) which calculate the difference of phase and amplitude between electroencephalogram (EEG) electrodes local and remote to the epileptic event. PLV is used as the seizure prediction marker and signifies the emergence of abnormal neuronal activations through local neuron populations. PLV/ALVs are used as seizure detection markers to demarcate the seizure event, or when the local seizure event has propagated throughout the brain turning into a grand-mal event. We verify the performance of this methodology against the “CHB-MIT Scalp EEG Database” which features seizure attributes for testing. Through this testing, we can demonstrate a high degree of sensivity and precision of our methodology between pre-ictal and ictal events.

[1]  J. Martinerie,et al.  Toward a Neurodynamical Understanding of Ictogenesis , 2003, Epilepsia.

[2]  J. Martinerie,et al.  Preictal state identification by synchronization changes in long-term intracranial EEG recordings , 2005, Clinical Neurophysiology.

[3]  Theoden Netoff,et al.  Seizure prediction with spectral power of EEG using cost‐sensitive support vector machines , 2011, Epilepsia.

[4]  Robert Kozma,et al.  Basic principles of the KIV model and its application to the navigation problem. , 2003, Journal of integrative neuroscience.

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

[6]  Brian Litt,et al.  One-Class Novelty Detection for Seizure Analysis from Intracranial EEG , 2006, J. Mach. Learn. Res..

[7]  Michael L. Levy,et al.  Vagus Nerve Stimulation , 2008, Proceedings of the IEEE.

[8]  H. Aghazarian,et al.  Computational Aspects of Cognition and Consciousness in Intelligent Devices , 2007, IEEE Computational Intelligence Magazine.

[9]  E. Ben-Menachem,et al.  Vagus Nerve Stimulation, Side Effects, and Long-Term Safety , 2001, Journal of clinical neurophysiology : official publication of the American Electroencephalographic Society.

[10]  Florian Mormann,et al.  Seizure prediction , 2008, Scholarpedia.

[11]  F. Mormann,et al.  Mean phase coherence as a measure for phase synchronization and its application to the EEG of epilepsy patients , 2000 .

[12]  Kurths,et al.  Phase synchronization of chaotic oscillators. , 1996, Physical review letters.

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

[14]  J. Martinerie,et al.  Epileptic seizures can be anticipated by non-linear analysis , 1998, Nature Medicine.

[15]  K. Tsakalis,et al.  Long-term prospective on-line real-time seizure prediction , 2005, Clinical Neurophysiology.

[16]  J. Martinerie,et al.  Anticipating epileptic seizures in real time by a non-linear analysis of similarity between EEG recordings. , 1999, Neuroreport.

[17]  I. Osorio,et al.  Real‐Time Automated Detection and Quantitative Analysis of Seizures and Short‐Term Prediction of Clinical Onset , 1998, Epilepsia.

[18]  K. Tsakalis,et al.  Control of Epileptic Seizures: Models of Chaotic Oscillator Networks , 2005, Proceedings of the 44th IEEE Conference on Decision and Control.

[19]  J. Martinerie,et al.  Characterizing Neurodynamic Changes Before Seizures , 2001, Journal of clinical neurophysiology : official publication of the American Electroencephalographic Society.

[20]  Klaus Lehnertz,et al.  Testing the null hypothesis of the nonexistence of a preseizure state. , 2003, Physical review. E, Statistical, nonlinear, and soft matter physics.

[21]  Brian Litt,et al.  The statistics of a practical seizure warning system , 2008, Journal of neural engineering.

[22]  F. Mormann,et al.  Seizure prediction: the long and winding road. , 2007, Brain : a journal of neurology.

[23]  W. Freeman,et al.  How brains make chaos in order to make sense of the world , 1987, Behavioral and Brain Sciences.

[24]  A. Kraskov,et al.  On the predictability of epileptic seizures , 2005, Clinical Neurophysiology.

[25]  Ali H. Shoeb,et al.  Application of machine learning to epileptic seizure onset detection and treatment , 2009 .

[26]  Liang-Gee Chen,et al.  Seizure prediction based on classification of EEG synchronization patterns with on-line retraining and post-processing scheme , 2011, 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[27]  Jürgen Kurths,et al.  Detection of n:m Phase Locking from Noisy Data: Application to Magnetoencephalography , 1998 .

[28]  J. Murphy,et al.  Adverse events in children receiving intermittent left vagal nerve stimulation. , 1998, Pediatric neurology.

[29]  David M. Himes,et al.  Prediction of seizure likelihood with a long-term, implanted seizure advisory system in patients with drug-resistant epilepsy: a first-in-man study , 2013, The Lancet Neurology.

[30]  J. Martinerie,et al.  Statistical assessment of nonlinear causality: application to epileptic EEG signals , 2003, Journal of Neuroscience Methods.

[31]  R D Walter Progress in epilepsy , 1973, California medicine.

[32]  Jayadev Misra,et al.  Phase Synchronization , 1991, Inf. Process. Lett..

[33]  D. Spencer,et al.  Ictal spikes: a marker of specific hippocampal cell loss. , 1992, Electroencephalography and clinical neurophysiology.

[34]  A. Schulze-Bonhage,et al.  The seizure prediction characteristic: a general framework to assess and compare seizure prediction methods , 2003, Epilepsy & Behavior.

[35]  B. Hjorth EEG analysis based on time domain properties. , 1970, Electroencephalography and clinical neurophysiology.

[36]  M. Le Van Quyen,et al.  Loss of phase synchrony in an animal model of partial status epilepticus , 2007, Neuroscience.

[37]  J. Sackellares,et al.  EPILEPSY – WHEN CHAOS FAILS , 2000 .

[38]  Thomas Mercer Hursh Biostatistics: A Foundation for Analysis in the Health Sciences Wayne W. Daniel , 1979 .

[39]  Andreas Schulze-Bonhage,et al.  Testing statistical significance of multivariate time series analysis techniques for epileptic seizure prediction. , 2006, Chaos.

[40]  Eric Panken,et al.  A micropower support vector machine based seizure detection architecture for embedded medical devices , 2009, 2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[41]  P. Pardalos,et al.  Performance of a seizure warning algorithm based on the dynamics of intracranial EEG , 2005, Epilepsy Research.

[42]  Walter J. Freeman,et al.  Petit mal seizure spikes in olfactory bulb and cortex caused by runaway inhibition after exhaustion of excitation , 1986, Brain Research Reviews.