Combined use of multiple computational intracranial EEG analysis techniques for the localization of epileptogenic zones in Lennox-Gastaut syndrome
Traditionally, identification of epileptogenic zones primarily relied on visual inspection of intracranial electroencephalography (iEEG) recordings by experienced epileptologists; however, removal of epileptogenic zones identified by iEEG does not always guarantee favorable surgical outcomes. To confirm visual inspection results, and assist in making decisions about surgical resection areas, computational iEEG analysis methods have recently been used for the localization of epileptogenic zones. In this study, we have proposed a new approach for the localization of epileptogenic zones in Lennox-Gastaut syndrome (LGS), and have investigated whether the proposed approach could confirm surgical resection areas and predict seizure outcome before surgery. The proposed approach simultaneously used results of 2 iEEG analysis methods, directed transfer function (DTF) and time delay estimation, to enhance localization accuracy. This new combined method was applied to patients who became seizure-free after resective epilepsy surgery, as well as those who had unsuccessful surgery. A quantitative metric was also introduced that can measure how well the localized epileptogenic zones coincided with the surgical resection areas, with the aim of verifying whether the approach could confirm surgical resection areas determined by epileptologists. The estimated epileptogenic zones more strongly coincided with surgical resection areas in patients with successful, compared to those with unsuccessful surgical outcomes. Both qualitative and quantitative analyses showed that the combined use of 2 iEEG analyses resulted in a more accurate estimate of epileptogenic zones in LGS than the use of a single method. A combination of multiple iEEG analyses could not only enhance overall accuracy of localizing epileptogenic zones in LGS, but also has the potential to predict outcomes before resective surgery.