For most individuals living with epilepsy, seizures are relatively infrequent events occupying a small fraction of their life. Despite spending as little a 0.01% of their lives having seizures (typically only minutes per month), people with epilepsy take anti-epileptic drugs (AED) daily, suffer AED related side effects, and spend their lives dreading when the next seizure will strike. The apparent randomness of seizures is associated with significant psychological consequences. In addition, despite daily AED approximately 1/3 of patients continue to have seizures. We hypothesize that epilepsy can be more effectively treated, both the seizures and their psychological impact, by providing patients with real-time seizure forecasting. Periods of low seizure probability would not require AEDs, or at least lower doses of AEDs, thus reducing AED exposure and their side effects. Periods of high seizure probability may respond to acute AED and patients could alter their activities to avoid injury. Patients would be empowered to manage their medications and life activities using reliable seizure forecasts. In this grant we investigate the hypothesis that seizures are predictable events, and pursue accurate, clinically relevant seizure forecasting using recent advances in support vector machines (SVM), data-analytic models, and Universum-SVM applied to continuous intracranial EEG (iEEG) in focal canine epilepsy. This is an initial step in establishin a new treatment paradigm for focal epilepsy, whereby the probability of seizure occurrence is continuously tracked for patient warning and intelligent responsive therapies. Naturally occurring focal canine epilepsy is an excellent model for investigation of seizure forecasting because of the clinical and electrophsyiological similarity to focal human epilepsy. This study provides a unique opportunity to study seizure forecasting in naturally occurring canine epilepsy under uniform conditions (the same environment). Importantly, dogs are large enough to accommodate devices designed for human use. The hypotheses driving this proposal are that focal seizures are not random events and there are brain states associated with low or high probability of seizure occurrence, and that these states can be reliably classified using machine learning approaches (SVM & Universum-SVM) that combine features from iEEG, behavioral state tracking, and electrocardiogram (ECG) heart rate variability. The goal of this proposal is to develop reliable seizure forecasting (when possible) and improved understanding (data characterization) when good forecasting is not possible.

Public Health Relevance

This grant proposes to develop the capability for accurate, reliable seizure forecasting using recent advances in support vector machines, data-analytic models, and Universum-SVM applied to continuous intracranial EEG canines with naturally occurring epilepsy. Naturally occurring focal canine epilepsy is an excellent model for investigation of seizure forecasting because of the clinical and electrophsyiological similarity to focal human epilepsy. This is an initial step in establishing a new treatment paradigm for focal epilepsy, whereby the probability of seizure occurrence is continuously tracked for patient warning and intelligent responsive neurostimulative or pharmacological therapies.

Agency
National Institute of Health (NIH)
Institute
National Institute of Neurological Disorders and Stroke (NINDS)
Type
Research Project (R01)
Project #
3R01NS092882-02S1
Application #
9212935
Study Section
Acute Neural Injury and Epilepsy Study Section (ANIE)
Program Officer
Stewart, Randall R
Project Start
2015-05-15
Project End
2020-03-31
Budget Start
2016-04-01
Budget End
2017-03-31
Support Year
2
Fiscal Year
2016
Total Cost
Indirect Cost
Name
Mayo Clinic, Rochester
Department
Type
DUNS #
006471700
City
Rochester
State
MN
Country
United States
Zip Code
55905
Bruinsma, Tyler J; Sarma, Vidur V; Oh, Yoonbae et al. (2018) The Relationship Between Dopamine Neurotransmitter Dynamics and the Blood-Oxygen-Level-Dependent (BOLD) Signal: A Review of Pharmacological Functional Magnetic Resonance Imaging. Front Neurosci 12:238
Kuhlmann, Levin; Karoly, Philippa; Freestone, Dean R et al. (2018) Epilepsyecosystem.org: crowd-sourcing reproducible seizure prediction with long-term human intracranial EEG. Brain 141:2619-2630
Varatharajah, Yogatheesan; Berry, Brent; Cimbalnik, Jan et al. (2018) Integrating artificial intelligence with real-time intracranial EEG monitoring to automate interictal identification of seizure onset zones in focal epilepsy. J Neural Eng 15:046035
Gliske, Stephen V; Irwin, Zachary T; Chestek, Cynthia et al. (2018) Variability in the location of high frequency oscillations during prolonged intracranial EEG recordings. Nat Commun 9:2155
Hernan, Amanda E; Schevon, Catherine A; Worrell, Gregory A et al. (2017) Methodological standards and functional correlates of depth in vivo electrophysiological recordings in control rodents. A TASK1-WG3 report of the AES/ILAE Translational Task Force of the ILAE. Epilepsia 58 Suppl 4:28-39
Khadjevand, Fatemeh; Cimbalnik, Jan; Worrell, Gregory A (2017) Progress and Remaining Challenges in the Application of High Frequency Oscillations as Biomarkers of Epileptic Brain. Curr Opin Biomed Eng 4:87-96
Varatharajah, Yogatheesan; Iyer, Ravishankar K; Berry, Brent M et al. (2017) Seizure Forecasting and the Preictal State in Canine Epilepsy. Int J Neural Syst 27:1650046
Baldassano, Steven N; Brinkmann, Benjamin H; Ung, Hoameng et al. (2017) Crowdsourcing seizure detection: algorithm development and validation on human implanted device recordings. Brain 140:1680-1691
Kremen, Vaclav; Duque, Juliano J; Brinkmann, Benjamin H et al. (2017) Behavioral state classification in epileptic brain using intracranial electrophysiology. J Neural Eng 14:026001
Shiao, Han-Tai; Cherkassky, Vladimir; Lee, Jieun et al. (2017) SVM-Based System for Prediction of Epileptic Seizures From iEEG Signal. IEEE Trans Biomed Eng 64:1011-1022

Showing the most recent 10 out of 14 publications