Reference v intrakraniálním EEG: implementace a analýza
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A
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Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií
Abstract
PĹ™edstava záznamu mozkovĂ© aktivity bez zkreslujĂcĂch artefaktĹŻ koluje ve vÄ›deckĂ˝ch kruzĂch jiĹľ nÄ›kolik desĂtek let. ParazitnĂ jevy a nežádoucĂ sloĹľky dokážà vĂ˝raznÄ› komplikovat analĂ˝zu pacientskĂ©ho záznamu intrakraniálnĂho elektroencefalografu (iEEG). S nástupem modernĂ technologie však zaÄŤaly pĹ™ibĂ˝vat novĂ© metody pro preciznĂ odstranÄ›nĂ zkreslujĂcĂho šumu. Zde nastupuje koncept virtuálnĂch referenÄŤnĂch signálĹŻ, jakoĹľto nástroj pro eliminaci nežádoucĂch komponent. V tĂ©to práci, metoda zaloĹľená na prĹŻmÄ›rovánĂ spolu s modernÄ›jšĂmi metodami zaloĹľenĂ˝ch na analĂ˝ze nezávislĂ˝ch komponent (ICA) byly realizovány a testovány na rĹŻznĂ˝ch iEEG záznamech. Bylo zjištÄ›no, Ĺľe algoritmy zaloĹľenĂ© na ICA umoĹľĹujĂ lepšà a pĹ™esnÄ›jšà odhad referenÄŤnĂho signálu v porovnánĂ s prĹŻmÄ›rovacĂ metodou. Na závÄ›r byly všechny navrĹľenĂ© metody implementovány do open-source Python knihovny đť‘źđť‘’đť‘“đť‘ đť‘–đť‘”, která je veĹ™ejnÄ› dostupná, jednoduše instalovatelná a pĹ™ipravena k pouĹľitĂ.
The idea of a artifact-free brain activity recording has been circling around the scientific world for a few decades. Parasitic phenomenons and unwanted components may significatntly complicate the analysis of intracranial electroencephalographic (iEEG) recordings. However, with the rise of modern technology, new methods for precise removal of noise artifacts started to emerge. Here we use the concept of virtual reference signals for the elimination of such unwanted components. In this work, the algorithms for reference signal estimation using common average based method as well as more recent methods based on independent component analysis (ICA) were realized and evaluated on a variety of iEEG data. It was found that the ICA-based algorithms allow obtaining more accurate estimation of the reference signal as compared to the average-based one. Finally, all the methods were implemented into a open-source Python package đť‘źđť‘’đť‘“đť‘ đť‘–đť‘”, which is publicly available, easy to install and ready to use.
The idea of a artifact-free brain activity recording has been circling around the scientific world for a few decades. Parasitic phenomenons and unwanted components may significatntly complicate the analysis of intracranial electroencephalographic (iEEG) recordings. However, with the rise of modern technology, new methods for precise removal of noise artifacts started to emerge. Here we use the concept of virtual reference signals for the elimination of such unwanted components. In this work, the algorithms for reference signal estimation using common average based method as well as more recent methods based on independent component analysis (ICA) were realized and evaluated on a variety of iEEG data. It was found that the ICA-based algorithms allow obtaining more accurate estimation of the reference signal as compared to the average-based one. Finally, all the methods were implemented into a open-source Python package đť‘źđť‘’đť‘“đť‘ đť‘–đť‘”, which is publicly available, easy to install and ready to use.
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Citation
UHER, D. Reference v intrakraniálním EEG: implementace a analýza [online]. Brno: Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií. 2018.
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Document version
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cs
Study field
Biomedicínská technika a bioinformatika
Comittee
prof. Ing. Ivo Provazník, Ph.D. (předseda)
doc. Ing. Martin Černý, Ph.D. (místopředseda)
Ing. Marina Ronzhina, Ph.D. (člen)
Ing. Oto Janoušek, Ph.D. (člen)
Ing. Vratislav Čmiel, Ph.D. (člen)
MUDr. Marie Nováková, Ph.D. (člen)
Date of acceptance
2018-06-14
Defence
Student prezentoval výsledky své práce a komise byla seznámena s posudky. Ing. Janoušek položil otázku: Proč chcete restaurovat výslednou amplitudu signálu? Student obhájil bakalářskou práci a odpověděl na otázky členů komise a oponenta.
Result of defence
práce byla úspěšně obhájena
Document licence
Standardní licenční smlouva - přístup k plnému textu bez omezení