1. Brain tissue classification from stereoelectroencephalographic recordings
- Author
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Mariana Mulinari Pinheiro Machado, Philippe Kahane, Olivier David, Guillaume Becq, Alina Voda, Gildas Besancon, GIPSA - Modelling and Optimal Decision for Uncertain Systems (GIPSA-MODUS), GIPSA Pôle Automatique et Diagnostic (GIPSA-PAD), Grenoble Images Parole Signal Automatique (GIPSA-lab), Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université Grenoble Alpes (UGA)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université Grenoble Alpes (UGA)-Grenoble Images Parole Signal Automatique (GIPSA-lab), Université Grenoble Alpes (UGA), GIPSA Pôle Géométrie, Apprentissage, Information et Algorithmes (GIPSA-GAIA), GIPSA-Services (GIPSA-Services), Centre Hospitalier Universitaire [Grenoble] (CHU), Groupe d'imagerie neurofonctionnelle (GIN), Institut des Maladies Neurodégénératives [Bordeaux] (IMN), Université de Bordeaux (UB)-Centre National de la Recherche Scientifique (CNRS)-Université de Bordeaux (UB)-Centre National de la Recherche Scientifique (CNRS)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA), Institut de Neurosciences des Systèmes (INS), Aix Marseille Université (AMU)-Institut National de la Santé et de la Recherche Médicale (INSERM), Mulinari Pinheiro Machado, Mariana, [GIN] Grenoble Institut des Neurosciences (GIN), Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019]), and Institut National de la Santé et de la Recherche Médicale (INSERM)-Aix Marseille Université (AMU)
- Subjects
Computer science ,[SDV]Life Sciences [q-bio] ,Brain tissue ,050105 experimental psychology ,Stereoelectroencephalography ,Stereotaxic Techniques ,03 medical and health sciences ,Naive Bayes classifier ,0302 clinical medicine ,Classifier (linguistics) ,Humans ,0501 psychology and cognitive sciences ,Focal Epilepsies ,business.industry ,General Neuroscience ,05 social sciences ,Brain ,Pattern recognition ,Bayes Theorem ,Electroencephalography ,Linear discriminant analysis ,Electrodes, Implanted ,[SDV] Life Sciences [q-bio] ,Homogeneous ,Frequency domain ,Artificial intelligence ,Epilepsies, Partial ,business ,030217 neurology & neurosurgery - Abstract
Background Stereoelectroencephalographic (SEEG) recordings can be performed before final resective surgery in some drug-resistant patients with focal epilepsies. For good SEEG signal interpretation, it is important to correctly identify the brain tissue in which each contact is inserted. Tissue classification is usually done with the coregistration of CT scan (with implanted SEEG electrodes) with preoperative MRI. New method Brain tissue classification is done here directly from SEEG signals obtained at rest by a linear discriminant analysis (LDA) classifier using measured SEEG signals. The classification operates on features extracted from Bode plots obtained via non-parametric frequency domain transfer functions of adjacent contacts pairs. Classification results have been compared with classification from T1 MRI following the labelling procedure described in Deman et al. (2018), together with minor corrections by visual inspection by specialists. Results With the data processed from 19 epileptic patients representing 1284 contact pairs, an accuracy of 72 ± 3% was obtained for homogeneous tissue separation. To our knowledge only one previous study conducted brain tissue classification using the power spectra of SEEG signals, and the distance between contacts on a shaft. The features proposed in our article performed better with the LDA classifier. However, the Bayesian classifier proposed in Greene et al. (2020) is more robust and could be used in a future study to enhance the classification performance. Conclusions and significance Our findings suggest that careful analysis of the transfer function between adjacent contacts measuring resting activity via frequency domain identification, could allow improved interpretation of SEEG data and or their co-registration with subject’s anatomy.
- Published
- 2021
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