January  2018, 23(1): 161-172. doi: 10.3934/dcdsb.2018010

Detecting features of epileptogenesis in EEG after TBI using unsupervised diffusion component analysis

1. 

USC Stevens Neuroimaging and Informatics Institute, University of Southern California, 2025 Zonal Ave, Los Angeles, CA, 90033, USA

2. 

Division of Neurosurgery and Department of Neurology, University of California at Los Angeles School of Medicine, 10833 LeConte Avenue, CHS 18-218, Los Angeles, CA, 90024, USA

* Corresponding author: Dominique Duncan

Received  January 2017 Published  January 2018

Epilepsy is among the most common serious disabling disorders of the brain, and the global burden of epilepsy exerts a tremendous cost to society. Most people with epilepsy have acquired forms, and the development of antiepileptogenic interventions could potentially prevent or cure these epilepsies [3,13]. The discovery of potential antiepileptogenic treatments is currently a high research priority. Clinical validation would require a means to identify populations of patients at particular high risk for epilepsy after a potential epileptogenic insult to know when to treat and to document prevention or cure. We investigate the development of post-traumatic epilepsy (PTE) following traumatic brain injury (TBI), because this condition offers the best opportunity to know the time of onset of epileptogenesis in patients. Epileptogenesis is common after TBI, and because much is known about the physical history of PTE, it represents a near-ideal human model in which to study the process of developing seizures.

Using scalp and depth EEG recordings for six patients, the goal of our analysis is to find a way to quantitatively detect features in the EEG that could potentially help predict seizure onset post trauma. Unsupervised Diffusion Component Analysis [5], a novel approach based on the diffusion mapping framework [4], reduces data dimensionality and provides pattern recognition that can be used to distinguish different states of the patient, such as seizures and non-seizure spikes in the EEG. This method is also adapted to the data to enable the extraction of the underlying brain activity. Previous work has shown that such techniques can be useful for seizure prediction [6].

Some new results that demonstrate how this algorithm is used to detect spikes in the EEG data as well as other changes over time are shown. This nonlinear and local network approach has been used to determine if the early occurrences of specific electrical features of epileptogenesis, such as interictal epileptiform activity and morphologic changes in spikes and seizures, during the initial week after TBI predicts the development of PTE.

Citation: Dominique Duncan, Paul Vespa, Arthur W. Toga. Detecting features of epileptogenesis in EEG after TBI using unsupervised diffusion component analysis. Discrete & Continuous Dynamical Systems - B, 2018, 23 (1) : 161-172. doi: 10.3934/dcdsb.2018010
References:
[1]

J. F. AnnegersW. A. HauserS. P. Coan and W. A. Rocca, A population-based study of seizures after traumatic brain injuries, N Engl J Med, 338 (1998), 20-24. Google Scholar

[2]

J. L. ArangoC. P. DeibertD. BrownM. BellI. Dvorchik and P. D. Adelson, Posttraumatic seizures in children with severe traumatic brain injury, Childs Nerv Syst, 28 (2012), 1925-1929. Google Scholar

[3]

C. E. BegleyM. Famulari and J. F. Annegers, The cost of epilepsy in the United States: An estimate from population-based clinical and survey data, Epilepsia, 41 (2000), 342-351. Google Scholar

[4]

R. R. Coifman and S. Lafon, Diffusion maps, Appl. Comp. Harm. Anal., 21 (2006), 5-30. doi: 10.1016/j.acha.2006.04.006. Google Scholar

[5]

D. Duncan and T. Strohmer, Classification of Alzheimer's disease using unsupervised diffusion component analysis, Math Biosci Eng, 13 (2016), 1119-1130. doi: 10.3934/mbe.2016033. Google Scholar

[6]

D. DuncanR. TalmonH. P. Zaveri and R. R. Coifman, Identifying preseizure state in intracranial EEG data using diffusion kernels, Math Biosci Eng, 10 (2013), 579-590. doi: 10.3934/mbe.2013.10.579. Google Scholar

[7]

R. ImmonenI. KharatishviliO. Gröhn and A. Pitkänen, MRI biomarkers for post-traumatic epileptogenesis, Journal of neurotrauma, 30 (2013), 1305-1309. Google Scholar

[8]

C. D. LamarR. A. HurleyJ. A. Rowland and K. H. Taber, Post-traumatic epilepsy: review of risks, pathophysiology, and potential biomarkers, J Neuropsychiatry Clin Neurosci, 26 (2014), ⅳ-113. Google Scholar

[9]

J. J. LinM. Mula and B. P. Hermann, Uncovering the neurobehavioural comorbidities of epilepsy over the lifespan, Lancet, 380 (2012), 1180-1192. Google Scholar

[10]

W. Loscher and C. Brandt, Prevention or modification of epileptogenesis after brain insults: experimental approaches and translational research, Pharmacol Rev, 62 (2010), 668-700. Google Scholar

[11]

E. S. LutkenhoffD. L. McArthurX. HuaP. M. ThompsonP. M. Vespa and M. M. Monti, Thalamic atrophy in antero-medial and dorsal nuclei correlates with six-month outcome after severe brain injury, Neuroimage Clin, 3 (2013), 396-404. Google Scholar

[12]

A. M. Mishra, X. Bai, B. G. Sanganahalli, S. G. Waxman, O. Shatillo, O. Grohn, F. Hyder, A. Pitkänen and H. Blumenfeld, Decreased resting functional connectivity after traumatic brain injury in the rat, PloS one, 9 (2014), e95280.Google Scholar

[13]

C. J. L. Murray and A. D. Lopez, Global Comparative Assessment in the Health Sector; Disease Burden, Expenditures, and Intervention Packages, Geneva: World Health Organization, 1994.Google Scholar

[14]

T. OstergardJ. SweetD. KusykE. Herring and J. Miller, Animal models of post-traumatic epilepsy. Journal of neuroscience methods, Journal of neuroscience methods, 272 (2016), 50-55. Google Scholar

[15]

R. OttmanC. Barker-CummingsC. L. LeibsonV. M. VasoliW. A. Hauser and J. R. Buchhalter, Validation of a brief screening instrument for the ascertainment of epilepsy, Epilepsia, 51 (2010), 191-197. Google Scholar

[16]

A. Y. ReidA. BraginC. C. GizaR. J. Staba and J. Engel, The progression of electrophysiologic abnormalities during epileptogenesis after experimental traumatic brain injury, Epilepsia, 47 (2016), 1558-1567. Google Scholar

[17]

Y. RubnerC. Tomasi and L. J. Guibas, A metric for distributions with applications to image databases, IEEE 6th International Conference on Computer Vision, (1998), 59-66. Google Scholar

[18]

A. M. SalazarB. JabbariS. C. VanceJ. GrafmanD. Amin and J. D. Dillon, Epilepsy after penetrating head injury. 1. Clinical correlates: A report of the Vietnam Head Injury Study, Neurology, 35 (1985), 1406-1414. Google Scholar

[19]

C. Syms, Principal components analysis, Elsevier, (2008), 2940-2949.Google Scholar

[20]

J. P. SzaflarskiY. Nazzal and L. E. Dreer, Post-traumatic epilepsy: Current and emerging treatment options, Neuropsychiatr Dis Treat, 10 (2014), 1469-1477. Google Scholar

[21]

R. Talmon and R. R. Coifman, Differential stochastic sensing: Intrinsic modeling of random time series with applications to nonlinear tracking, PNAS, (2012), 1-14. Google Scholar

[22]

R. TalmonD. KushnirR. R. CoifmanI. Cohen and S. Gannot, Parametrization of linear systems using diffusion kernels, IEEE Transactions on Signal Processing, 60 (2012), 1159-1173. doi: 10.1109/TSP.2011.2177973. Google Scholar

[23]

R. TalmonD. KushnirR. R. CoifmanI. Cohen and S. Gannot, Parametrization of linear systems using diffusion kernels, IEEE Trans. Signal Process, 60 (2012), 1159-1173. doi: 10.1109/TSP.2011.2177973. Google Scholar

[24]

P. M. VespaM. R. NuwerV. NenovE. Ronne-EngstromD. A. HovdaN. A. Martin and D. P. Becker, Increased incidence and impact of nonconvulsive and convulsive seizures after traumatic brain injury as detected by continuous EEG in the intensive care unit, J Neurosurg, 91 (1999), 750-760. Google Scholar

[25]

P. M. Vespa, M. Tubi, J. Claassen, M. Blanco, D. McArthur, A. G. Velazquez, B. Tu, M. Prins and M. Nuwer, Metabolic Crisis Occurs with Seizures and Periodic Discharges after Brain Trauma, Ann Neurol, 2016.Google Scholar

[26]

P. M. VespaD. L. McArthurY. XuM. EliseoM. EtchepareI. DinovJ. AlgerT. P. Glenn and D. Hovda, Nonconvulsive seizures after traumatic brain injury are associated with hippocampal atrophy, Ann Neurol, 75 (2010), 792-798. Google Scholar

show all references

References:
[1]

J. F. AnnegersW. A. HauserS. P. Coan and W. A. Rocca, A population-based study of seizures after traumatic brain injuries, N Engl J Med, 338 (1998), 20-24. Google Scholar

[2]

J. L. ArangoC. P. DeibertD. BrownM. BellI. Dvorchik and P. D. Adelson, Posttraumatic seizures in children with severe traumatic brain injury, Childs Nerv Syst, 28 (2012), 1925-1929. Google Scholar

[3]

C. E. BegleyM. Famulari and J. F. Annegers, The cost of epilepsy in the United States: An estimate from population-based clinical and survey data, Epilepsia, 41 (2000), 342-351. Google Scholar

[4]

R. R. Coifman and S. Lafon, Diffusion maps, Appl. Comp. Harm. Anal., 21 (2006), 5-30. doi: 10.1016/j.acha.2006.04.006. Google Scholar

[5]

D. Duncan and T. Strohmer, Classification of Alzheimer's disease using unsupervised diffusion component analysis, Math Biosci Eng, 13 (2016), 1119-1130. doi: 10.3934/mbe.2016033. Google Scholar

[6]

D. DuncanR. TalmonH. P. Zaveri and R. R. Coifman, Identifying preseizure state in intracranial EEG data using diffusion kernels, Math Biosci Eng, 10 (2013), 579-590. doi: 10.3934/mbe.2013.10.579. Google Scholar

[7]

R. ImmonenI. KharatishviliO. Gröhn and A. Pitkänen, MRI biomarkers for post-traumatic epileptogenesis, Journal of neurotrauma, 30 (2013), 1305-1309. Google Scholar

[8]

C. D. LamarR. A. HurleyJ. A. Rowland and K. H. Taber, Post-traumatic epilepsy: review of risks, pathophysiology, and potential biomarkers, J Neuropsychiatry Clin Neurosci, 26 (2014), ⅳ-113. Google Scholar

[9]

J. J. LinM. Mula and B. P. Hermann, Uncovering the neurobehavioural comorbidities of epilepsy over the lifespan, Lancet, 380 (2012), 1180-1192. Google Scholar

[10]

W. Loscher and C. Brandt, Prevention or modification of epileptogenesis after brain insults: experimental approaches and translational research, Pharmacol Rev, 62 (2010), 668-700. Google Scholar

[11]

E. S. LutkenhoffD. L. McArthurX. HuaP. M. ThompsonP. M. Vespa and M. M. Monti, Thalamic atrophy in antero-medial and dorsal nuclei correlates with six-month outcome after severe brain injury, Neuroimage Clin, 3 (2013), 396-404. Google Scholar

[12]

A. M. Mishra, X. Bai, B. G. Sanganahalli, S. G. Waxman, O. Shatillo, O. Grohn, F. Hyder, A. Pitkänen and H. Blumenfeld, Decreased resting functional connectivity after traumatic brain injury in the rat, PloS one, 9 (2014), e95280.Google Scholar

[13]

C. J. L. Murray and A. D. Lopez, Global Comparative Assessment in the Health Sector; Disease Burden, Expenditures, and Intervention Packages, Geneva: World Health Organization, 1994.Google Scholar

[14]

T. OstergardJ. SweetD. KusykE. Herring and J. Miller, Animal models of post-traumatic epilepsy. Journal of neuroscience methods, Journal of neuroscience methods, 272 (2016), 50-55. Google Scholar

[15]

R. OttmanC. Barker-CummingsC. L. LeibsonV. M. VasoliW. A. Hauser and J. R. Buchhalter, Validation of a brief screening instrument for the ascertainment of epilepsy, Epilepsia, 51 (2010), 191-197. Google Scholar

[16]

A. Y. ReidA. BraginC. C. GizaR. J. Staba and J. Engel, The progression of electrophysiologic abnormalities during epileptogenesis after experimental traumatic brain injury, Epilepsia, 47 (2016), 1558-1567. Google Scholar

[17]

Y. RubnerC. Tomasi and L. J. Guibas, A metric for distributions with applications to image databases, IEEE 6th International Conference on Computer Vision, (1998), 59-66. Google Scholar

[18]

A. M. SalazarB. JabbariS. C. VanceJ. GrafmanD. Amin and J. D. Dillon, Epilepsy after penetrating head injury. 1. Clinical correlates: A report of the Vietnam Head Injury Study, Neurology, 35 (1985), 1406-1414. Google Scholar

[19]

C. Syms, Principal components analysis, Elsevier, (2008), 2940-2949.Google Scholar

[20]

J. P. SzaflarskiY. Nazzal and L. E. Dreer, Post-traumatic epilepsy: Current and emerging treatment options, Neuropsychiatr Dis Treat, 10 (2014), 1469-1477. Google Scholar

[21]

R. Talmon and R. R. Coifman, Differential stochastic sensing: Intrinsic modeling of random time series with applications to nonlinear tracking, PNAS, (2012), 1-14. Google Scholar

[22]

R. TalmonD. KushnirR. R. CoifmanI. Cohen and S. Gannot, Parametrization of linear systems using diffusion kernels, IEEE Transactions on Signal Processing, 60 (2012), 1159-1173. doi: 10.1109/TSP.2011.2177973. Google Scholar

[23]

R. TalmonD. KushnirR. R. CoifmanI. Cohen and S. Gannot, Parametrization of linear systems using diffusion kernels, IEEE Trans. Signal Process, 60 (2012), 1159-1173. doi: 10.1109/TSP.2011.2177973. Google Scholar

[24]

P. M. VespaM. R. NuwerV. NenovE. Ronne-EngstromD. A. HovdaN. A. Martin and D. P. Becker, Increased incidence and impact of nonconvulsive and convulsive seizures after traumatic brain injury as detected by continuous EEG in the intensive care unit, J Neurosurg, 91 (1999), 750-760. Google Scholar

[25]

P. M. Vespa, M. Tubi, J. Claassen, M. Blanco, D. McArthur, A. G. Velazquez, B. Tu, M. Prins and M. Nuwer, Metabolic Crisis Occurs with Seizures and Periodic Discharges after Brain Trauma, Ann Neurol, 2016.Google Scholar

[26]

P. M. VespaD. L. McArthurY. XuM. EliseoM. EtchepareI. DinovJ. AlgerT. P. Glenn and D. Hovda, Nonconvulsive seizures after traumatic brain injury are associated with hippocampal atrophy, Ann Neurol, 75 (2010), 792-798. Google Scholar

Figure 1.  Raw EEG data showing epileptiform spike activity at two time points
Figure 2.  The corresponding embedding using Unsupervised Diffusion Component Analysis and eigenvectors 2, 3, and 5
Figure 3.  An example of raw EEG data from 4 electrode contacts from 1 patient with epileptiform activity at one time point
Figure 4.  The corresponding embedding using Unsupervised Diffusion Component Analysis (color represents time), and the yellow/orange points are separated from the other embedded points
Figure 5.  An example of EEG data from 3 electrode contacts from another patient with more subtle spikes that are not clear from examining the raw data
Figure 6.  The corresponding embedding using Unsupervised Diffusion Component Analysis
Figure 7.  An example of raw EEG data from 3 electrode contacts from 1 patient with no epileptiform activity
Figure 8.  The corresponding embedding using Unsupervised Diffusion Component Analysis
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