Epileptic seizure prediction based on ratio and differential linear univariate features
Bivariate features, obtained from multichannel electroencephalogram recordings, quantify the relation between different brain regions. Studies based on bivariate features have shown optimistic results for tackling epileptic seizure prediction problem in patients suffering from refractory epilepsy. A...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Wolters Kluwer Medknow Publications
2015-01-01
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| Series: | Journal of Medical Signals and Sensors |
| Subjects: | |
| Online Access: | http://www.jmss.mui.ac.ir/article.asp?issn=2228-7477;year=2015;volume=5;issue=1;spage=1;epage=11;aulast=Rasekhi |
| _version_ | 1819116838139723776 |
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| author | Jalil Rasekhi Mohammad Reza Karami Mollaei Mojtaba Bandarabadi César A Teixeira António Dourado |
| author_facet | Jalil Rasekhi Mohammad Reza Karami Mollaei Mojtaba Bandarabadi César A Teixeira António Dourado |
| author_sort | Jalil Rasekhi |
| collection | DOAJ |
| description | Bivariate features, obtained from multichannel electroencephalogram recordings, quantify the relation between different brain regions. Studies based on bivariate features have shown optimistic results for tackling epileptic seizure prediction problem in patients suffering from refractory epilepsy. A new bivariate approach using univariate features is proposed here. Differences and ratios of 22 linear univariate features were calculated using pairwise combination of 6 electroencephalograms channels, to create 330 differential, and 330 relative features. The feature subsets were classified using support vector machines separately, as one of the two classes of preictal and nonpreictal. Furthermore, minimum Redundancy Maximum Relevance feature reduction method is employed to improve the predictions and reduce the number of false alarms. The studies were carried out on features obtained from 10 patients. For reduced subset of 30 features and using differential approach, the seizures were on average predicted in 60.9% of the cases (28 out of 46 in 737.9 h of test data), with a low false prediction rate of 0.11 h−1. Results of bivariate approaches were compared with those achieved from original linear univariate features, extracted from 6 channels. The advantage of proposed bivariate features is the smaller number of false predictions in comparison to the original 22 univariate features. In addition, reduction in feature dimension could provide a less complex and the more cost-effective algorithm. Results indicate that applying machine learning methods on a multidimensional feature space resulting from relative/differential pairwise combination of 22 univariate features could predict seizure onsets with high performance. |
| first_indexed | 2024-12-22T05:23:27Z |
| format | Article |
| id | doaj.art-438b2b2b55ad4939b028c2fdd0992d88 |
| institution | Directory Open Access Journal |
| issn | 2228-7477 |
| language | English |
| last_indexed | 2024-12-22T05:23:27Z |
| publishDate | 2015-01-01 |
| publisher | Wolters Kluwer Medknow Publications |
| record_format | Article |
| series | Journal of Medical Signals and Sensors |
| spelling | doaj.art-438b2b2b55ad4939b028c2fdd0992d882022-12-21T18:37:40ZengWolters Kluwer Medknow PublicationsJournal of Medical Signals and Sensors2228-74772015-01-0151111Epileptic seizure prediction based on ratio and differential linear univariate featuresJalil RasekhiMohammad Reza Karami MollaeiMojtaba BandarabadiCésar A TeixeiraAntónio DouradoBivariate features, obtained from multichannel electroencephalogram recordings, quantify the relation between different brain regions. Studies based on bivariate features have shown optimistic results for tackling epileptic seizure prediction problem in patients suffering from refractory epilepsy. A new bivariate approach using univariate features is proposed here. Differences and ratios of 22 linear univariate features were calculated using pairwise combination of 6 electroencephalograms channels, to create 330 differential, and 330 relative features. The feature subsets were classified using support vector machines separately, as one of the two classes of preictal and nonpreictal. Furthermore, minimum Redundancy Maximum Relevance feature reduction method is employed to improve the predictions and reduce the number of false alarms. The studies were carried out on features obtained from 10 patients. For reduced subset of 30 features and using differential approach, the seizures were on average predicted in 60.9% of the cases (28 out of 46 in 737.9 h of test data), with a low false prediction rate of 0.11 h−1. Results of bivariate approaches were compared with those achieved from original linear univariate features, extracted from 6 channels. The advantage of proposed bivariate features is the smaller number of false predictions in comparison to the original 22 univariate features. In addition, reduction in feature dimension could provide a less complex and the more cost-effective algorithm. Results indicate that applying machine learning methods on a multidimensional feature space resulting from relative/differential pairwise combination of 22 univariate features could predict seizure onsets with high performance.http://www.jmss.mui.ac.ir/article.asp?issn=2228-7477;year=2015;volume=5;issue=1;spage=1;epage=11;aulast=RasekhiClassificationepilepsyepileptic seizure predictionfeatures selectionsupport vector machine |
| spellingShingle | Jalil Rasekhi Mohammad Reza Karami Mollaei Mojtaba Bandarabadi César A Teixeira António Dourado Epileptic seizure prediction based on ratio and differential linear univariate features Journal of Medical Signals and Sensors Classification epilepsy epileptic seizure prediction features selection support vector machine |
| title | Epileptic seizure prediction based on ratio and differential linear univariate features |
| title_full | Epileptic seizure prediction based on ratio and differential linear univariate features |
| title_fullStr | Epileptic seizure prediction based on ratio and differential linear univariate features |
| title_full_unstemmed | Epileptic seizure prediction based on ratio and differential linear univariate features |
| title_short | Epileptic seizure prediction based on ratio and differential linear univariate features |
| title_sort | epileptic seizure prediction based on ratio and differential linear univariate features |
| topic | Classification epilepsy epileptic seizure prediction features selection support vector machine |
| url | http://www.jmss.mui.ac.ir/article.asp?issn=2228-7477;year=2015;volume=5;issue=1;spage=1;epage=11;aulast=Rasekhi |
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