Objectives: Hemispherotomy (HT) is a surgical option for treatment of drug-resistant seizures due to hemispheric structural lesions. Factors affecting seizure outcome have not been fully clarified. In our study, we used a brain Machine Learning (ML) approach to evaluate the possible role of Inter-hemispheric EEG Connectivity (IC) in predicting post-surgical seizure outcome. Methods: We collected 21 pediatric patients with drug-resistant epilepsy; who underwent HT in our center from 2009 to 2020; with a follow-up of at least two years. We selected 5-s windows of wakefulness and sleep pre-surgical EEG and we trained Artificial Neuronal Network (ANN) to estimate epilepsy outcome. We extracted EEG features as input data and selected the ANN with best accuracy. Results: Among 21 patients, 15 (71%) were seizure and drug-free at last follow-up. ANN showed 73.3% of accuracy, with 85% of seizure free and 40% of non-seizure free patients appropriately classified. Conclusions: The accuracy level that we reached supports the hypothesis that pre-surgical EEG features may have the potential to predict epilepsy outcome after HT. Significance: The role of pre-surgical EEG data in influencing seizure outcome after HT is still debated. We proposed a computational predictive model, with an ML approach, with a high accuracy level.

Can Presurgical Interhemispheric EEG Connectivity Predict Outcome in Hemispheric Surgery? A Brain Machine Learning Approach / Pepi, Chiara; Mercier, Mattia; Carfì Pavia, Giusy; de Benedictis, Alessandro; Vigevano, Federico; ROSSI ESPAGNET, MARIA CAMILLA; Falcicchio, Giovanni; Efisio Marras, Carlo; Specchio, Nicola; DE PALMA, Luca. - In: BRAIN SCIENCES. - ISSN 2076-3425. - (2022). [10.3390/brainsci13010071]

Can Presurgical Interhemispheric EEG Connectivity Predict Outcome in Hemispheric Surgery? A Brain Machine Learning Approach

Mattia Mercier
Secondo
Methodology
;
Maria Camilla Rossi-Espagnet
;
Luca de Palma
2022

Abstract

Objectives: Hemispherotomy (HT) is a surgical option for treatment of drug-resistant seizures due to hemispheric structural lesions. Factors affecting seizure outcome have not been fully clarified. In our study, we used a brain Machine Learning (ML) approach to evaluate the possible role of Inter-hemispheric EEG Connectivity (IC) in predicting post-surgical seizure outcome. Methods: We collected 21 pediatric patients with drug-resistant epilepsy; who underwent HT in our center from 2009 to 2020; with a follow-up of at least two years. We selected 5-s windows of wakefulness and sleep pre-surgical EEG and we trained Artificial Neuronal Network (ANN) to estimate epilepsy outcome. We extracted EEG features as input data and selected the ANN with best accuracy. Results: Among 21 patients, 15 (71%) were seizure and drug-free at last follow-up. ANN showed 73.3% of accuracy, with 85% of seizure free and 40% of non-seizure free patients appropriately classified. Conclusions: The accuracy level that we reached supports the hypothesis that pre-surgical EEG features may have the potential to predict epilepsy outcome after HT. Significance: The role of pre-surgical EEG data in influencing seizure outcome after HT is still debated. We proposed a computational predictive model, with an ML approach, with a high accuracy level.
2022
brain machine learning; hemispherotomy; outcome; seizure prediction.
01 Pubblicazione su rivista::01a Articolo in rivista
Can Presurgical Interhemispheric EEG Connectivity Predict Outcome in Hemispheric Surgery? A Brain Machine Learning Approach / Pepi, Chiara; Mercier, Mattia; Carfì Pavia, Giusy; de Benedictis, Alessandro; Vigevano, Federico; ROSSI ESPAGNET, MARIA CAMILLA; Falcicchio, Giovanni; Efisio Marras, Carlo; Specchio, Nicola; DE PALMA, Luca. - In: BRAIN SCIENCES. - ISSN 2076-3425. - (2022). [10.3390/brainsci13010071]
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/1724670
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