双谱
脑电图
立体脑电图
癫痫
联轴节(管道)
神经科学
模式识别(心理学)
计算机科学
心理学
人工智能
癫痫外科
光谱密度
材料科学
电信
冶金
作者
Laura Gagliano,Alex Chang,Leila Abrishami Shokooh,Dènahin Hinnoutondji Toffa,Frédéric Lesage,Mohamad Sawan,Dang Khoa Nguyen,Elie Bou Assi
标识
DOI:10.1109/embc40787.2023.10340885
摘要
Connectivity analyses of intracranial electroencephalography (iEEG) could guide surgical planning for epilepsy surgery by improving the delineation of the seizure onset zone. Traditional approaches fail to quantify important interactions between frequency components. To assess if effective connectivity based on cross-bispectrum -a measure of nonlinear multivariate cross-frequency coupling- can quantitatively identify generators of seizure activity, cross-bispectrum connectivity between channels was computed from iEEG recordings of 5 patients (34 seizures) with good postsurgical outcome. Personalized thresholds of 50% and 80% of the maximum coupling values were used to identify generating electrode channels. In all patients, outflow coupling between α (8-15 Hz) and β (16-31 Hz) frequencies identified at least one electrode inside the resected seizure onset zone. With the 50% and 80% thresholds respectively, an average of 5 (44.7%; specificity = 82.6%) and 2 (22.5%; specificity = 99.0%) resected electrodes were correctly identified. Results show promise for the automatic identification of the seizure onset zone based on cross-bispectrum connectivity analysis.
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