Statistical-Hypothesis-Aided Tests for Epilepsy Classification
In this paper, an efficient, accurate, and nonparametric epilepsy detection and classification approach based on electroencephalogram (EEG) signals is proposed. The proposed approach mainly depends on a feature extraction process that is conducted using a set of statistical tests. Among the many exi...
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| Format: | Article |
| Language: | English |
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MDPI AG
2019-11-01
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| Series: | Computers |
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| Online Access: | https://www.mdpi.com/2073-431X/8/4/84 |
| _version_ | 1811304995086139392 |
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| author | Alaa Alqatawneh Rania Alhalaseh Ahmad Hassanat Mohammad Abbadi |
| author_facet | Alaa Alqatawneh Rania Alhalaseh Ahmad Hassanat Mohammad Abbadi |
| author_sort | Alaa Alqatawneh |
| collection | DOAJ |
| description | In this paper, an efficient, accurate, and nonparametric epilepsy detection and classification approach based on electroencephalogram (EEG) signals is proposed. The proposed approach mainly depends on a feature extraction process that is conducted using a set of statistical tests. Among the many existing tests, those fit with processed data and for the purpose of the proposed approach were used. From each test, various output scalars were extracted and used as features in the proposed detection and classification task. Experiments that were conducted on the basis of a Bonn University dataset showed that the proposed approach had very accurate results (<inline-formula> <math display="inline"> <semantics> <mrow> <mn>98</mn> <mo>.</mo> <mn>4</mn> <mo>%</mo> </mrow> </semantics> </math> </inline-formula>) in the detection task and outperformed state-of-the-art methods in a similar task on the same dataset. The proposed approach also had accurate results (<inline-formula> <math display="inline"> <semantics> <mrow> <mn>94</mn> <mo>.</mo> <mn>0</mn> <mo>%</mo> </mrow> </semantics> </math> </inline-formula>) in the classification task, but it did not outperform state-of-the-art methods in a similar task on the same dataset. However, the proposed approach had less time complexity in comparison with those methods that achieved better results. |
| first_indexed | 2024-04-13T08:17:23Z |
| format | Article |
| id | doaj.art-dfa240b2e27c49e1887811f4d988b3fb |
| institution | Directory Open Access Journal |
| issn | 2073-431X |
| language | English |
| last_indexed | 2024-04-13T08:17:23Z |
| publishDate | 2019-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Computers |
| spelling | doaj.art-dfa240b2e27c49e1887811f4d988b3fb2022-12-22T02:54:44ZengMDPI AGComputers2073-431X2019-11-01848410.3390/computers8040084computers8040084Statistical-Hypothesis-Aided Tests for Epilepsy ClassificationAlaa Alqatawneh0Rania Alhalaseh1Ahmad Hassanat2Mohammad Abbadi3Computer Science Department, Mutah University, Karak 61710, JordanComputer Science Department, Mutah University, Karak 61710, JordanComputer Department, Community College University of Tabuk, Tabuk 71491, Saudi ArabiaComputer Science Department, Mutah University, Karak 61710, JordanIn this paper, an efficient, accurate, and nonparametric epilepsy detection and classification approach based on electroencephalogram (EEG) signals is proposed. The proposed approach mainly depends on a feature extraction process that is conducted using a set of statistical tests. Among the many existing tests, those fit with processed data and for the purpose of the proposed approach were used. From each test, various output scalars were extracted and used as features in the proposed detection and classification task. Experiments that were conducted on the basis of a Bonn University dataset showed that the proposed approach had very accurate results (<inline-formula> <math display="inline"> <semantics> <mrow> <mn>98</mn> <mo>.</mo> <mn>4</mn> <mo>%</mo> </mrow> </semantics> </math> </inline-formula>) in the detection task and outperformed state-of-the-art methods in a similar task on the same dataset. The proposed approach also had accurate results (<inline-formula> <math display="inline"> <semantics> <mrow> <mn>94</mn> <mo>.</mo> <mn>0</mn> <mo>%</mo> </mrow> </semantics> </math> </inline-formula>) in the classification task, but it did not outperform state-of-the-art methods in a similar task on the same dataset. However, the proposed approach had less time complexity in comparison with those methods that achieved better results.https://www.mdpi.com/2073-431X/8/4/84biomedical signal processingelectromyographymultiple-signal processingeegmachine learningepilepsy |
| spellingShingle | Alaa Alqatawneh Rania Alhalaseh Ahmad Hassanat Mohammad Abbadi Statistical-Hypothesis-Aided Tests for Epilepsy Classification Computers biomedical signal processing electromyography multiple-signal processing eeg machine learning epilepsy |
| title | Statistical-Hypothesis-Aided Tests for Epilepsy Classification |
| title_full | Statistical-Hypothesis-Aided Tests for Epilepsy Classification |
| title_fullStr | Statistical-Hypothesis-Aided Tests for Epilepsy Classification |
| title_full_unstemmed | Statistical-Hypothesis-Aided Tests for Epilepsy Classification |
| title_short | Statistical-Hypothesis-Aided Tests for Epilepsy Classification |
| title_sort | statistical hypothesis aided tests for epilepsy classification |
| topic | biomedical signal processing electromyography multiple-signal processing eeg machine learning epilepsy |
| url | https://www.mdpi.com/2073-431X/8/4/84 |
| work_keys_str_mv | AT alaaalqatawneh statisticalhypothesisaidedtestsforepilepsyclassification AT raniaalhalaseh statisticalhypothesisaidedtestsforepilepsyclassification AT ahmadhassanat statisticalhypothesisaidedtestsforepilepsyclassification AT mohammadabbadi statisticalhypothesisaidedtestsforepilepsyclassification |