Epilepsy Seizure Detection: A Heavy Tail Approach
Epilepsy is a chronic brain disorder that affects the quality of life of many patients even when this disease is being controlled. If we want to improve those lives affected, we need to perform real-time epilepsy detection with constant monitoring of the electroencephalogram (EEG) signal. Typically,...
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| Format: | Article |
| Language: | English |
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IEEE
2020-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/9261381/ |
| _version_ | 1818727458012135424 |
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| author | Jesus G. Servin-Aguilar Luis Rizo-Dominguez Jorge A. Pardinas-Mir Cesar Vargas-Rosales Ivan Padilla-Cantoya |
| author_facet | Jesus G. Servin-Aguilar Luis Rizo-Dominguez Jorge A. Pardinas-Mir Cesar Vargas-Rosales Ivan Padilla-Cantoya |
| author_sort | Jesus G. Servin-Aguilar |
| collection | DOAJ |
| description | Epilepsy is a chronic brain disorder that affects the quality of life of many patients even when this disease is being controlled. If we want to improve those lives affected, we need to perform real-time epilepsy detection with constant monitoring of the electroencephalogram (EEG) signal. Typically, the statistical behavior of the EEG signals presents heavy-tail phenomena, therefore their analysis must be particular in order to define a strong framework based on statistical parameters to detect seizures. In this article, the heavy-tail characterization of EEG signals is studied, a simple real-time epilepsy detection using an alpha-stable estimator is proposed, and the false-positive rate is analyzed. The performance of the proposed estimator is compared to others previously reported in the literature, and we show that one of the signal parameters characterized as an alpha-stable distribution, serves as an indicator of epilepsy episodes more efficiently. Furthermore, the proposed algorithm presents low sensitivity to noise below the 3.8 dB. |
| first_indexed | 2024-12-17T22:14:25Z |
| format | Article |
| id | doaj.art-5b64f7ca27984d46b945c4dd81fe3fea |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-17T22:14:25Z |
| publishDate | 2020-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-5b64f7ca27984d46b945c4dd81fe3fea2022-12-21T21:30:39ZengIEEEIEEE Access2169-35362020-01-01820817020817810.1109/ACCESS.2020.30383979261381Epilepsy Seizure Detection: A Heavy Tail ApproachJesus G. Servin-Aguilar0https://orcid.org/0000-0002-1908-3171Luis Rizo-Dominguez1https://orcid.org/0000-0001-9393-5320Jorge A. Pardinas-Mir2https://orcid.org/0000-0002-0972-3980Cesar Vargas-Rosales3https://orcid.org/0000-0003-1770-471XIvan Padilla-Cantoya4https://orcid.org/0000-0002-6825-706XElectronic, Systems and Informatics Department, ITESO, Jesuit University of Guadalajara, Tlaquepaque, MexicoElectronic, Systems and Informatics Department, ITESO, Jesuit University of Guadalajara, Tlaquepaque, MexicoElectronic, Systems and Informatics Department, ITESO, Jesuit University of Guadalajara, Tlaquepaque, MexicoEscuela de Ingenieria y Ciencias, Tecnologico de Monterrey, Monterrey, MexicoElectronic, Systems and Informatics Department, ITESO, Jesuit University of Guadalajara, Tlaquepaque, MexicoEpilepsy is a chronic brain disorder that affects the quality of life of many patients even when this disease is being controlled. If we want to improve those lives affected, we need to perform real-time epilepsy detection with constant monitoring of the electroencephalogram (EEG) signal. Typically, the statistical behavior of the EEG signals presents heavy-tail phenomena, therefore their analysis must be particular in order to define a strong framework based on statistical parameters to detect seizures. In this article, the heavy-tail characterization of EEG signals is studied, a simple real-time epilepsy detection using an alpha-stable estimator is proposed, and the false-positive rate is analyzed. The performance of the proposed estimator is compared to others previously reported in the literature, and we show that one of the signal parameters characterized as an alpha-stable distribution, serves as an indicator of epilepsy episodes more efficiently. Furthermore, the proposed algorithm presents low sensitivity to noise below the 3.8 dB.https://ieeexplore.ieee.org/document/9261381/Alpha stable parametersepilepsy detectionfalse-positive ratelong tail characterizationreal-time epilepsy detector |
| spellingShingle | Jesus G. Servin-Aguilar Luis Rizo-Dominguez Jorge A. Pardinas-Mir Cesar Vargas-Rosales Ivan Padilla-Cantoya Epilepsy Seizure Detection: A Heavy Tail Approach IEEE Access Alpha stable parameters epilepsy detection false-positive rate long tail characterization real-time epilepsy detector |
| title | Epilepsy Seizure Detection: A Heavy Tail Approach |
| title_full | Epilepsy Seizure Detection: A Heavy Tail Approach |
| title_fullStr | Epilepsy Seizure Detection: A Heavy Tail Approach |
| title_full_unstemmed | Epilepsy Seizure Detection: A Heavy Tail Approach |
| title_short | Epilepsy Seizure Detection: A Heavy Tail Approach |
| title_sort | epilepsy seizure detection a heavy tail approach |
| topic | Alpha stable parameters epilepsy detection false-positive rate long tail characterization real-time epilepsy detector |
| url | https://ieeexplore.ieee.org/document/9261381/ |
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