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Journal Article

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


Semenova,  Nadezhda
External Organizations;

Segreev,  Konstantin
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Slepnev,  Andrei
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Runnova,  Anastasiya
External Organizations;

Zhuravlev,  Maxim
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Blokhina,  Inna
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Dubrovsky,  Alexander
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Klimova,  Maria
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Terskov,  Andrey
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Semyachkina-Glushkovskaya,  Oxana
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Kurths,  Jürgen
Potsdam Institute for Climate Impact Research;

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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.

Cite as: https://publications.pik-potsdam.de/pubman/item/item_26415
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.