Non-invasive analysis of blood-brain barrier permeability based on wavelet and machine learning approaches

TL;DR

This study presents non-invasive methods using wavelet and machine learning techniques to analyze blood-brain barrier permeability through EEG data, aiding in brain disease treatment.

Contribution

It introduces two novel approaches for non-invasive detection of blood-brain barrier opening using EEG analysis, validated by comparative metrics.

Findings

Both methods successfully recognize blood-brain barrier opening.

Wavelet and machine learning approaches show good agreement.

Methods are validated with F-measures and ROC-curves.

Abstract

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 analyse 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. 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.