EEG analysis – automatic spike detection
In the diagnosis and treatment of epilepsy, an electroencephalography (EEG) is one of the main tools. However visual inspection of EEG is very time consuming. Automatic extraction of important EEG features saves not only a lot of time for neurologist, but also enables a whole new level for EEG analy...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Vilnius University Press
2012-01-01
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| Series: | Nonlinear Analysis |
| Subjects: | |
| Online Access: | http://www.zurnalai.vu.lt/nonlinear-analysis/article/view/14083 |
| _version_ | 1818759588945592320 |
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| author | Algimantas Juozapavičius Gytis Bacevičius Dmitrijus Bugelskis Rūta Samaitienė |
| author_facet | Algimantas Juozapavičius Gytis Bacevičius Dmitrijus Bugelskis Rūta Samaitienė |
| author_sort | Algimantas Juozapavičius |
| collection | DOAJ |
| description | In the diagnosis and treatment of epilepsy, an electroencephalography (EEG) is one of the main tools. However visual inspection of EEG is very time consuming. Automatic extraction of important EEG features saves not only a lot of time for neurologist, but also enables a whole new level for EEG analysis, by using data mining methods. In this work we present and analyse methods to extract some of these features of EEG – drowsiness score and centrotemporal spikes. For spike detection, a method based on morphological filters is used. Also a database design is proposed in order to allow easy EEG analysis and provide data accessibility for data mining algorithms developed in the future. |
| first_indexed | 2024-12-18T06:45:07Z |
| format | Article |
| id | doaj.art-c9a565b547e743c58fc68a00e47710bc |
| institution | Directory Open Access Journal |
| issn | 1392-5113 2335-8963 |
| language | English |
| last_indexed | 2024-12-18T06:45:07Z |
| publishDate | 2012-01-01 |
| publisher | Vilnius University Press |
| record_format | Article |
| series | Nonlinear Analysis |
| spelling | doaj.art-c9a565b547e743c58fc68a00e47710bc2022-12-21T21:17:32ZengVilnius University PressNonlinear Analysis1392-51132335-89632012-01-01164EEG analysis – automatic spike detectionAlgimantas Juozapavičius0Gytis Bacevičius1Dmitrijus Bugelskis2Rūta Samaitienė3Vilnius University, LithuaniaVilnius University, LithuaniaVilnius University, LithuaniaVilnius University, LithuaniaIn the diagnosis and treatment of epilepsy, an electroencephalography (EEG) is one of the main tools. However visual inspection of EEG is very time consuming. Automatic extraction of important EEG features saves not only a lot of time for neurologist, but also enables a whole new level for EEG analysis, by using data mining methods. In this work we present and analyse methods to extract some of these features of EEG – drowsiness score and centrotemporal spikes. For spike detection, a method based on morphological filters is used. Also a database design is proposed in order to allow easy EEG analysis and provide data accessibility for data mining algorithms developed in the future.http://www.zurnalai.vu.lt/nonlinear-analysis/article/view/14083electroencephalogramrolandic epilepsyepileptic spikesmorphological filtersanalysis |
| spellingShingle | Algimantas Juozapavičius Gytis Bacevičius Dmitrijus Bugelskis Rūta Samaitienė EEG analysis – automatic spike detection Nonlinear Analysis electroencephalogram rolandic epilepsy epileptic spikes morphological filters analysis |
| title | EEG analysis – automatic spike detection |
| title_full | EEG analysis – automatic spike detection |
| title_fullStr | EEG analysis – automatic spike detection |
| title_full_unstemmed | EEG analysis – automatic spike detection |
| title_short | EEG analysis – automatic spike detection |
| title_sort | eeg analysis automatic spike detection |
| topic | electroencephalogram rolandic epilepsy epileptic spikes morphological filters analysis |
| url | http://www.zurnalai.vu.lt/nonlinear-analysis/article/view/14083 |
| work_keys_str_mv | AT algimantasjuozapavicius eeganalysisautomaticspikedetection AT gytisbacevicius eeganalysisautomaticspikedetection AT dmitrijusbugelskis eeganalysisautomaticspikedetection AT rutasamaitiene eeganalysisautomaticspikedetection |