Abstract

Objective To investigate the application value of stereoelectroencephalography (SEEG) in the preoperative assessment of epilepsy surgery. Methods The clinical data of 78 patients with drug-refractory epilepsy assessed with SEEG before procedure at the Epilepsy Center, Guangdong 999 Brain Hospital from April 2014 to April 2015 were analyzed retrospectively. The noninvasive assessment was conducted first, and the SEEG electrode placement scheme was designed according to the clinical and imaging data, and then under the guidance of robotized stereotactic assistant (ROSA), the electrode implantation was completed. The SEEG monitoring was conducted in order to complete the resection of epileptogenic zone finally under the guidance of neuronavigation. Results Seventy-eight patients completed 81 SEEG electrode implantations (3 patients had additional electrode implantations), including 16 (20.5%) bilateral implantations. A total of 940 SEEG electrodes were implanted and the average number of implanted electrodes was 12.3 per case. Only 1 patient had an epidural hematoma in the electrode region, and other patients did not have the complications, such as serious hemorrhage, infection, cerebrospinal fluid leakage, electrode fraction or displacement. Seventy-six patients successfully completed the resection of epileptogenic zone, and 2 patients failed to undergo surgery. Fifty-nine patients were followed up for 6-18 months. The seizure control for Engel rating grades were grade Ⅰ in 47 cases (79.7%), grade Ⅱ in 2 cases (3.4%), grade Ⅲ in 6 cases (10.2%), and grade Ⅳ in 4 cases (6.8%). In 51 patients with MRI positive, the Engel grade Ⅰ was in 42 cases (82.4%); in 8 patients with MRI negative, the Engel grade Ⅰ was in 5 cases. There was no significant difference in the seizure control rate between the 2 groups (P>0.05). Conclusion The surgical treatment of epilepsy under the guidance of SEEG is both safe and effective. Key words: Epilepsy; Electroencephalography; Stereoelectroencephalography; Robotized stereotactic assistant; Multimodel images

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