https://doi.org/10.1140/epjp/s13360-021-01715-2
Regular Article
Blood-brain barrier permeability changes: nonlinear analysis of ECoG based on wavelet and machine learning approaches
1
Saratov State University, Astrakhanskaya str., 83, 410012, Saratov, Russia
2
Département d’Optique P. M. Duffieux, Institut FEMTO-ST, Université Bourgogne-Franche-Comté CNRS UMR 6174, Besançon, France
3
State Medical University, B. Kazachaya str., 112, 410012, Saratov, Russia
4
Physics Department, Humboldt University, Newtonstrasse 15, 12489, Berlin, Germany
5
Potsdam Institute for Climate Impact Research, Telegrafenberg A31, 14473, Potsdam, Germany
Received:
12
January
2021
Accepted:
1
July
2021
Published online:
9
July
2021
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 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.
© The Author(s), under exclusive licence to Società Italiana di Fisica and Springer-Verlag GmbH Germany, part of Springer Nature 2021