Genetic programming and frequent itemset mining to identify feature selection patterns of iEEG and fMRI epilepsy data.
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| Title: | Genetic programming and frequent itemset mining to identify feature selection patterns of iEEG and fMRI epilepsy data. |
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| Authors: | Smart, Otis1 otissmart@gmail.com, Burrell, Lauren1 lauren.s.burrell@gmail.com |
| Source: | Engineering Applications of Artificial Intelligence. Mar2015, Vol. 39, p198-214. 17p. |
| Subjects: | Genetic programming, Data mining, Feature selection, Functional magnetic resonance imaging, Data analysis, Electroencephalography, Epilepsy |
| Abstract: | Pattern classification for intracranial electroencephalogram (iEEG) and functional magnetic resonance imaging (fMRI) signals has furthered epilepsy research toward understanding the origin of epileptic seizures and localizing dysfunctional brain tissue for treatment. Prior research has demonstrated that implicitly selecting features with a genetic programming (GP) algorithm more effectively determined the proper features to discern biomarker and non-biomarker interictal iEEG and fMRI activity than conventional feature selection approaches. However for each the iEEG and fMRI modalities, it is still uncertain whether the stochastic properties of indirect feature selection with a GP yield (a) consistent results within a patient data set and (b) features that are specific or universal across multiple patient data sets. We examined the reproducibility of implicitly selecting features to classify interictal activity using a GP algorithm by performing several selection trials and subsequent frequent itemset mining (FIM) for separate iEEG and fMRI epilepsy patient data. We observed within-subject consistency and across-subject variability with some small similarity for selected features, indicating a clear need for patient-specific features and possible need for patient-specific feature selection or/and classification. For the fMRI, using nearest-neighbor classification and 30 GP generations, we obtained over 60% median sensitivity and over 60% median selectivity. For the iEEG, using nearest-neighbor classification and 30 GP generations, we obtained over 65% median sensitivity and over 65% median selectivity except one patient. [ABSTRACT FROM AUTHOR] |
| Copyright of Engineering Applications of Artificial Intelligence is the property of Pergamon Press - An Imprint of Elsevier Science and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 100796919 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Genetic programming and frequent itemset mining to identify feature selection patterns of iEEG and fMRI epilepsy data. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Smart%2C+Otis%22">Smart, Otis</searchLink><relatesTo>1</relatesTo><i> otissmart@gmail.com</i><br /><searchLink fieldCode="AR" term="%22Burrell%2C+Lauren%22">Burrell, Lauren</searchLink><relatesTo>1</relatesTo><i> lauren.s.burrell@gmail.com</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Engineering+Applications+of+Artificial+Intelligence%22">Engineering Applications of Artificial Intelligence</searchLink>. Mar2015, Vol. 39, p198-214. 17p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Genetic+programming%22">Genetic programming</searchLink><br /><searchLink fieldCode="DE" term="%22Data+mining%22">Data mining</searchLink><br /><searchLink fieldCode="DE" term="%22Feature+selection%22">Feature selection</searchLink><br /><searchLink fieldCode="DE" term="%22Functional+magnetic+resonance+imaging%22">Functional magnetic resonance imaging</searchLink><br /><searchLink fieldCode="DE" term="%22Data+analysis%22">Data analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Electroencephalography%22">Electroencephalography</searchLink><br /><searchLink fieldCode="DE" term="%22Epilepsy%22">Epilepsy</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Pattern classification for intracranial electroencephalogram (iEEG) and functional magnetic resonance imaging (fMRI) signals has furthered epilepsy research toward understanding the origin of epileptic seizures and localizing dysfunctional brain tissue for treatment. Prior research has demonstrated that implicitly selecting features with a genetic programming (GP) algorithm more effectively determined the proper features to discern biomarker and non-biomarker interictal iEEG and fMRI activity than conventional feature selection approaches. However for each the iEEG and fMRI modalities, it is still uncertain whether the stochastic properties of indirect feature selection with a GP yield (a) consistent results within a patient data set and (b) features that are specific or universal across multiple patient data sets. We examined the reproducibility of implicitly selecting features to classify interictal activity using a GP algorithm by performing several selection trials and subsequent frequent itemset mining (FIM) for separate iEEG and fMRI epilepsy patient data. We observed within-subject consistency and across-subject variability with some small similarity for selected features, indicating a clear need for patient-specific features and possible need for patient-specific feature selection or/and classification. For the fMRI, using nearest-neighbor classification and 30 GP generations, we obtained over 60% median sensitivity and over 60% median selectivity. For the iEEG, using nearest-neighbor classification and 30 GP generations, we obtained over 65% median sensitivity and over 65% median selectivity except one patient. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Engineering Applications of Artificial Intelligence is the property of Pergamon Press - An Imprint of Elsevier Science and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1016/j.engappai.2014.12.008 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 17 StartPage: 198 Subjects: – SubjectFull: Genetic programming Type: general – SubjectFull: Data mining Type: general – SubjectFull: Feature selection Type: general – SubjectFull: Functional magnetic resonance imaging Type: general – SubjectFull: Data analysis Type: general – SubjectFull: Electroencephalography Type: general – SubjectFull: Epilepsy Type: general Titles: – TitleFull: Genetic programming and frequent itemset mining to identify feature selection patterns of iEEG and fMRI epilepsy data. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Smart, Otis – PersonEntity: Name: NameFull: Burrell, Lauren IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 03 Text: Mar2015 Type: published Y: 2015 Identifiers: – Type: issn-print Value: 09521976 Numbering: – Type: volume Value: 39 Titles: – TitleFull: Engineering Applications of Artificial Intelligence Type: main |
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