Identifying preseizure state in intracranial EEG data using diffusion kernels
The goal of this study is to identify preseizure changes in intracranial EEG (icEEG). A novel approach based on the recently developed diffusion map framework, which is considered to be one of the leading manifold learning methods, is proposed. Diffusion mapping provides dimensionality reduction of...
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| Format: | Article |
| Language: | English |
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AIMS Press
2013-03-01
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| Series: | Mathematical Biosciences and Engineering |
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| Online Access: | https://www.aimspress.com/article/doi/10.3934/mbe.2013.10.579 |
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| author | Dominique Duncan Ronen Talmon Hitten P. Zaveri Ronald R. Coifman |
| author_facet | Dominique Duncan Ronen Talmon Hitten P. Zaveri Ronald R. Coifman |
| author_sort | Dominique Duncan |
| collection | DOAJ |
| description | The goal of this study is to identify preseizure changes in intracranial EEG (icEEG). A novel approach based on the recently developed diffusion map framework, which is considered to be one of the leading manifold learning methods, is proposed. Diffusion mapping provides dimensionality reduction of the data as well as pattern recognition that can be used to distinguish different states of the patient, for example, interictal and preseizure. A new algorithm, which is an extension of diffusion maps, is developed to construct coordinates that generate efficient geometric representations of the complex structures in the icEEG data. In addition, this method is adapted to the icEEG data and enables the extraction of the underlying brain activity. The algorithm is tested on icEEG data recorded from several electrode contacts from a patient being evaluated for possible epilepsy surgery at the Yale-New Haven Hospital. Numerical results show that the proposed approach provides a distinction between interictal and preseizure states. |
| format | Article |
| id | doaj-art-8524355866294db594fa0947ba9138f0 |
| institution | Kabale University |
| issn | 1551-0018 |
| language | English |
| publishDate | 2013-03-01 |
| publisher | AIMS Press |
| record_format | Article |
| series | Mathematical Biosciences and Engineering |
| spelling | doaj-art-8524355866294db594fa0947ba9138f02025-01-24T02:26:12ZengAIMS PressMathematical Biosciences and Engineering1551-00182013-03-0110357959010.3934/mbe.2013.10.579Identifying preseizure state in intracranial EEG data using diffusion kernelsDominique Duncan0Ronen Talmon1Hitten P. Zaveri2Ronald R. Coifman3101 AKW, 51 Prospect St. New Haven, CT 06511101 AKW, 51 Prospect St. New Haven, CT 06511101 AKW, 51 Prospect St. New Haven, CT 06511101 AKW, 51 Prospect St. New Haven, CT 06511The goal of this study is to identify preseizure changes in intracranial EEG (icEEG). A novel approach based on the recently developed diffusion map framework, which is considered to be one of the leading manifold learning methods, is proposed. Diffusion mapping provides dimensionality reduction of the data as well as pattern recognition that can be used to distinguish different states of the patient, for example, interictal and preseizure. A new algorithm, which is an extension of diffusion maps, is developed to construct coordinates that generate efficient geometric representations of the complex structures in the icEEG data. In addition, this method is adapted to the icEEG data and enables the extraction of the underlying brain activity. The algorithm is tested on icEEG data recorded from several electrode contacts from a patient being evaluated for possible epilepsy surgery at the Yale-New Haven Hospital. Numerical results show that the proposed approach provides a distinction between interictal and preseizure states.https://www.aimspress.com/article/doi/10.3934/mbe.2013.10.579epilepsydiffusion mapsintracranial eegseizure predictionnonlinear independent component analysis |
| spellingShingle | Dominique Duncan Ronen Talmon Hitten P. Zaveri Ronald R. Coifman Identifying preseizure state in intracranial EEG data using diffusion kernels Mathematical Biosciences and Engineering epilepsy diffusion maps intracranial eeg seizure prediction nonlinear independent component analysis |
| title | Identifying preseizure state in intracranial EEG data using diffusion kernels |
| title_full | Identifying preseizure state in intracranial EEG data using diffusion kernels |
| title_fullStr | Identifying preseizure state in intracranial EEG data using diffusion kernels |
| title_full_unstemmed | Identifying preseizure state in intracranial EEG data using diffusion kernels |
| title_short | Identifying preseizure state in intracranial EEG data using diffusion kernels |
| title_sort | identifying preseizure state in intracranial eeg data using diffusion kernels |
| topic | epilepsy diffusion maps intracranial eeg seizure prediction nonlinear independent component analysis |
| url | https://www.aimspress.com/article/doi/10.3934/mbe.2013.10.579 |
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