Отрывок: EEG signals are pre-filtered by a 25 Hz notch filter and a second-order Butterworth filter with a passband from 0.5 to 22 Hz. Detection of specific events in EEG signals is carried out with the help of the ridge wavelet spectrograms power spectral density PDS = |W (t, fr)|2 analysis [4]. The decision rule for fixing the event is as follows: 2 3 3 2 3 1, ( ) 2 ( ); 0, ( ) 2...
Полная запись метаданных
Поле DC | Значение | Язык |
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dc.contributor.author | Murashov, D.M. | - |
dc.contributor.author | Obukhov, Y.V. | - |
dc.contributor.author | Kershner, I.A. | - |
dc.contributor.author | Sinkin, M.V. | - |
dc.date.accessioned | 2021-05-11 10:25:58 | - |
dc.date.available | 2021-05-11 10:25:58 | - |
dc.date.issued | 2021-04 | - |
dc.identifier | Dspace\SGAU\20210503\88404 | ru |
dc.identifier.citation | Murashov DM, Obukhov YV, Kershner IA, Sinkin MV. An algorithm for detecting events in video EEG monitoring data of patients with craniocerebral injuries. Computer Optics 2021; 45(2): 301-305. DOI: 10.18287/2412-6179-CO-798. | ru |
dc.identifier.uri | https://dx.doi.org/10.18287/2412-6179-CO-798 | - |
dc.identifier.uri | http://repo.ssau.ru/handle/Zhurnal-Komputernaya-optika/An-algorithm-for-detecting-events-in-video-EEG-monitoring-data-of-patients-with-craniocerebral-injuries-88404 | - |
dc.description.abstract | One of the problems solved by analyzing the data of long-term Video EEG monitoring is the differentiation of epileptic and artifact events. For this, not only multichannel EEG signals are used, but also video data analysis, since traditional methods based on the analysis of EEG wavelet spectrograms cannot reliably distinguish an epileptic seizure from a chewing artifact. In this paper, we propose an algorithm for detecting artifact events based on a joint analysis of the level of the optical flow and the ridges of wavelet spectrograms. The preliminary results of the analysis of real clinical data are given. The results show the possibility in principle of reliable distinguishing non-epileptic events from epileptic seizures. | ru |
dc.description.sponsorship | The work was carried out within the framework of the state task and partially was supported by the Russian Foundation for Basic Research, the project No 18-29-02035. | ru |
dc.language.iso | en_US | ru |
dc.publisher | Самарский национальный исследовательский университет имени акад. С.П. Королева | ru |
dc.relation.ispartofseries | 45;2 | - |
dc.subject | video EEG monitoring data | ru |
dc.subject | epileptic seizure | ru |
dc.subject | optical flow | ru |
dc.subject | wavelets | ru |
dc.subject | ridges of wavelet spectrograms | ru |
dc.subject | clinical applications | ru |
dc.title | An algorithm for detecting events in video EEG monitoring data of patients with craniocerebral injuries | ru |
dc.type | Article | ru |
dc.textpart | EEG signals are pre-filtered by a 25 Hz notch filter and a second-order Butterworth filter with a passband from 0.5 to 22 Hz. Detection of specific events in EEG signals is carried out with the help of the ridge wavelet spectrograms power spectral density PDS = |W (t, fr)|2 analysis [4]. The decision rule for fixing the event is as follows: 2 3 3 2 3 1, ( ) 2 ( ); 0, ( ) 2... | - |
dc.classindex.scsti | 28.00.00 | - |
Располагается в коллекциях: | Журнал "Компьютерная оптика" |
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450218.pdf | Основная статья | 1.75 MB | Adobe PDF | Просмотреть/Открыть |
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