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  Blood-brain barrier permeability changes: nonlinear analysis of ECoG based on wavelet and machine learning approaches

Semenova, N., Segreev, K., Slepnev, A., Runnova, A., Zhuravlev, M., Blokhina, I., Dubrovsky, A., Klimova, M., Terskov, A., Semyachkina-Glushkovskaya, O., Kurths, J. (2021): Blood-brain barrier permeability changes: nonlinear analysis of ECoG based on wavelet and machine learning approaches. - European Physical Journal Plus, 136, 7, 736.
https://doi.org/10.1140/epjp/s13360-021-01715-2

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Semenova2021_Article_Blood-brainBarrierPermeability.pdf (Verlagsversion), 863KB
 
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Semenova, Nadezhda1, Autor
Segreev, Konstantin1, Autor
Slepnev, Andrei1, Autor
Runnova, Anastasiya1, Autor
Zhuravlev, Maxim1, Autor
Blokhina, Inna1, Autor
Dubrovsky, Alexander1, Autor
Klimova, Maria1, Autor
Terskov, Andrey1, Autor
Semyachkina-Glushkovskaya, Oxana1, Autor
Kurths, Jürgen2, Autor              
Affiliations:
1External Organizations, ou_persistent22              
2Potsdam Institute for Climate Impact Research, ou_persistent13              

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 Zusammenfassung: The blood-brain barrier plays a decisive role in protecting the brain from toxins and pathogens. The ability to analyze the BBB opening (OBBB) is crucial for the treatment of many brain diseases, but it is very difficult to noninvasively monitor OBBB. In this paper we analyze the EEG series of healthy rats in free behaviour and after music-induced OBBB. The research is performed using two completely different methods based on wavelet analysis and machine learning approach. The wavelet-approach demonstrates quantitative changes in the oscillatory structure in EEG signals after music listening, namely, a decrease in the number of patterns to the frequency band Δf[1;2.5] Hz. Using methods of machine learning we analyze the number of fragments of EEG realizations recognized as OBBB. After the music impact the number of recognized OBBB is increased in about 50%. Both methods enable us to recognize OBBB and are in a good agreement with each other. The comparative analysis was carried out using F-measures and ROC-curves.

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 Datum: 2021-07-092021-07
 Publikationsstatus: Final veröffentlicht
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 Ort, Verlag, Ausgabe: -
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 Art der Begutachtung: Expertenbegutachtung
 Identifikatoren: DOI: 10.1140/epjp/s13360-021-01715-2
MDB-ID: No data to archive
PIKDOMAIN: RD4 - Complexity Science
Organisational keyword: RD4 - Complexity Science
Research topic keyword: Health
Model / method: Nonlinear Data Analysis
 Art des Abschluß: -

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Titel: European Physical Journal Plus
Genre der Quelle: Zeitschrift, SCI, Scopus, p3
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Seiten: - Band / Heft: 136 (7) Artikelnummer: 736 Start- / Endseite: - Identifikator: CoNE: https://publications.pik-potsdam.de/cone/journals/resource/journals139
Publisher: Springer