Electrode-brain interface fractional order modelling for brain tissue classification in SEEG

Epileptic patients with drug resistant epilepsy can undergo invasive stereoelectroencephalography (SEEG) examination in order to identify the epileptic zone to be removed with surgery. The correct classification of the brain tissue the electrodes are inserted in is decisive for the final clinical decision. The objective of this paper is to explore differences in brain tissue via the modelling of the brain electrode interface from SEEG recordings. The proposed model is based on the physical properties of the electrode-brain interface already presented in literature, and considers the voltage of three consecutive SEEG contacts. An identification algorithm is proposed considering constraints from the physical model and prior knowledge of the values of the components and a non-parametric study. Parameter models are identified using the data of 19 different patients, and validated by visual inspection and cross-correlation tests. Differences in the resistance of white and grey matter are observed using the coefficients of the identified models. From this coefficients, an accuracy of 73 +/- 6% is achieved for heterogeneous tissue classification of triplets of contacts. The information obtained from the study of the electrode-brain interface using typical SEEG signals proposed in this paper can be used to estimate tissue composition between triplets of consecutive SEEG contacts.

Automated summary

This paper explores differences in brain tissue through the modeling of the brain electrode interface from SEEG recordings. Using a physical model and non-parametric study, the authors successfully identify the brain tissue types and achieve a high classification accuracy.