Machine Learning and Stereoelectroencephalographic Feature Extraction for Brain Tissue Classification - Modelling and Optinal Decision for Uncertain Systems Accéder directement au contenu
Communication Dans Un Congrès Année : 2021

Machine Learning and Stereoelectroencephalographic Feature Extraction for Brain Tissue Classification

Résumé

Tissue classification of white or gray matter is a necessary information in the study of brain connectivity. Currently this classification is made by the coregistration of the implanted electrodes in the Magnetic Resonance Imaging (MRI) of the patient. This process is complex and therefore is not always carried out, and is limited by the image resolution and by the accuracy of the coregistration. This paper studies the performance of machine learning (ML) algorithms used with features extracted from Stereo-Electroencephalogram (SEEG) signals recorded from three epileptic patients, for electrode contact classification, to serve as a decision support for specialists and researchers. The features are based on epileptic detection, and are extracted from both time and frequency domain. Accuracy, Area Under Curve and F1-Score are evaluated for each ML algorithm, and feature importance is assessed by feature permutation. Satisfactory results were achieved, with a maximum of 79% accuracy in group separation for patient specific classification, and 74% in inter-patient classification, indicating high potential in ML techniques for brain tissue classification.

Dates et versions

hal-03483874 , version 1 (16-12-2021)

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Citer

Pedro Henrique Peres Morais Lopes, Mariana Mulinari Pinheiro Machado, Alina Voda, Gildas Besancon, Philippe Kahane, et al.. Machine Learning and Stereoelectroencephalographic Feature Extraction for Brain Tissue Classification. BMS 2021 - 11th IFAC symposium on Biological and Medical Systems (BMS 2021), Sep 2021, Ghent, Belgium. pp.340-345, ⟨10.1016/j.ifacol.2021.10.279⟩. ⟨hal-03483874⟩
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