System-Identification-Based Automatic Brain Tissue Classification for Stereoelectroencephalography

In the cases of drug-resistant epilepsy, patients might undergo resective surgery of the epileptic zone (EZ). The success of the surgery depends on the correct identification of the EZ and the eloquent cortex to be avoided. In both cases, the correct classification of the tissue where the measuring...

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Veröffentlicht in:	2022 26th International Conference on System Theory, Control and Computing (ICSTCC) S. 440 - 445
Hauptverfasser:	Pinheiro Machado, Mariana Mulinari, Voda, Alina, Besaneon, Gildas, Becq, Guillaume, David, Olivier, Kahane, Philippe
Format:	Tagungsbericht
Sprache:	Englisch
Veröffentlicht:	IEEE 19.10.2022
Schlagworte:	brain tissue classification Control systems Epilepsy Feature extraction Frequency-domain analysis Magnetic resonance imaging Medical services non-integer order model non-parametric frequency domain identifi-cation SEEG Surgery system identification based classification
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Beschreibung
Zusammenfassung:	In the cases of drug-resistant epilepsy, patients might undergo resective surgery of the epileptic zone (EZ). The success of the surgery depends on the correct identification of the EZ and the eloquent cortex to be avoided. In both cases, the correct classification of the tissue where the measuring contacts are inserted is needed during the stereoelectroencephalography (SEEG). Most of the tissue classification procedures rely on imaging. In this paper a system identification based automatic classifier is proposed using previously proposed non-parametric and parametric methods for single contact tissue classification. By combining both identification methods, poorly classified contacts can be eliminated, and overall contact classification can be improved, especially for the parametric classifier. The proposed method can be either used in combination with imaging methods, or it could be used to help select contacts to be recorded during SEEG Investigation.
DOI:	10.1109/ICSTCC55426.2022.9931852