Epilepsy is a very common disease affecting at least 1% of the population, comprising a number of over 50 million people. As many patients suffer from the drug-resistant version, the number of potential treatment methods is very small. However, since not only the treatment of epilepsy, but also its proper diagnosis or observation of brain signals from recordings are important research areas, in this paper, we address this very problem by developing a reliable technique for removing spikes and sharp transients from the baseline of the brain signal using a morphological filter. This allows much more precise identification of the so-called epileptic zone, which can then be resected, which is one of the methods of epilepsy treatment. We used eight patients with 5 KHz data set and depended upon the Staba 2002 algorithm as a reference to detect the ripples. We found that the average sensitivity and false detection rate of our technique are significant, and they are ∼94% and ∼14%, respectively.

• Klíčová slova
• brain signals, dynamic threshold, epilepsy, morphological filter, ripples, spikes,
• MeSH
• algoritmy MeSH
• elektroencefalografie * metody   MeSH
• epilepsie * diagnóza   MeSH
• lidé MeSH
• mapování mozku MeSH
• mozek MeSH
• Check Tag
• lidé MeSH
• Publikační typ
• časopisecké články MeSH

Data comprise intracranial EEG (iEEG) brain activity represented by stereo EEG (sEEG) signals, recorded from over 100 electrode channels implanted in any one patient across various brain regions. The iEEG signals were recorded in epilepsy patients (N = 10) undergoing invasive monitoring and localization of seizures when they were performing a battery of four memory tasks lasting approx. 1 hour in total. Gaze tracking on the task computer screen with estimating the pupil size was also recorded together with behavioral performance. Each dataset comes from one patient with anatomical localization of each electrode contact. Metadata contains labels for the recording channels with behavioral events marked from all tasks, including timing of correct and incorrect vocalization of the remembered stimuli. The iEEG and the pupillometric signals are saved in BIDS data structure to facilitate efficient data sharing and analysis.

• MeSH
• elektrody MeSH
• elektrokortikografie * MeSH
• epilepsie patofyziologie   MeSH
• lidé MeSH
• mozek fyziologie   MeSH
• oční fixace MeSH
• paměť fyziologie   MeSH
• pupila MeSH
• technologie sledování pohybu očí MeSH
• záchvaty patofyziologie   MeSH
• Check Tag
• lidé MeSH
• Publikační typ
• časopisecké články MeSH
• dataset MeSH
• práce podpořená grantem MeSH
• Research Support, N.I.H., Extramural MeSH