Objective: The feasibility of peri-orbital electrodes, which are not invasive and do not induce pain, as a supplemental electrode for detection of ictal discharges in medial temporal lobe epilepsy (MTLE) was examined. Methods: Patients with MTLE, who underwent video-EEG monitoring with simultaneous peri-orbital and sphenoidal electrodes and obtained good outcome following standard anterior temporal lobectomy, were subjects in this study. Initial ictal discharge amplitudes were compared between sphenoidal (Sp1/ 2), standard anterior temporal in 10-20 system (F7/ 8), peri-orbital (superior orbital lateral: SOL, inferior orbital medial: IOM), frontopolar (Fp1/ 2), frontal (F3/4) and ear (A1/ 2) electrodes. Results: A total of 34 consecutive seizures from 20 patients were analyzed, with a maximum amplitude observed at Sp1/2 (57.57±5.59), followed by F7/8 (54.89±5.59), SOL (50.97±5.59), IOM (46.95±5.59), A1/2 (45.07±5.69), Fp1/2 (44.78±5.62), and F3/4 (37.75±5.66) (mean±standard error, μV). There was no statistical difference between Sp1/2, F7/8, SOL, and IOM values. When the sphenoidal electrode was omitted, 13 seizures (13/34, 38.2%) resulted in the highest amplitude at peri-orbital electrodes and 10 seizures (10/ 34, 29.4%) at F7/8. Conclusions: Peri-orbital electrodes could detect ictal discharges in MTLE as well as sphenoidal and standard anterior temporal electrodes in 10-20 system and are useful for supplemental recording for detecting ictal epileptiform discharges in MTLE.

Purpose：We aimed to build a predictive model for ADL, applicable to all patients admitted to a convalescent rehabilitation ward, that uses a simple index as explanatory variables, which is accessible early during hospitalization.Methods：We included 1153 patients admitted to our convalescent rehabilitation ward. Stepwise multiple regression analysis was conducted using 18 Functional Independence Measure (FIM) sub-items at admission, with sex, age, and time since onset as explanatory variables. The total FIM motor score was calculated by summing 13 motor sub-items at discharge. Cross-validation was performed on 85 participants, independent of the analysis group, to confirm the model's clinical applicability.Results：The multiple regression analysis results for the analysis group showed a significant equation with an R2 of 0.712. In the validation group with 85 patients, difference between the predicted and actual FIM scores at discharge was compared between the two groups, and a strong correlation of r=0.888 (p＜0.019 was observed, without any significant difference between the groups.Conclusion：This model is less labor intensive when constructing a prediction equation because the objective variable is easily obtained. Our findings indicate that other convalescent rehabilitation wards can develop prognostic models using their data and implement them clinically.

Purpose：We aimed to build a predictive model for ADL, applicable to all patients admitted to a convalescent rehabilitation ward, that uses a simple index as explanatory variables, which is accessible early during hospitalization.Methods：We included 1153 patients admitted to our convalescent rehabilitation ward. Stepwise multiple regression analysis was conducted using 18 Functional Independence Measure (FIM) sub-items at admission, with sex, age, and time since onset as explanatory variables. The total FIM motor score was calculated by summing 13 motor sub-items at discharge. Cross-validation was performed on 85 participants, independent of the analysis group, to confirm the model's clinical applicability.Results：The multiple regression analysis results for the analysis group showed a significant equation with an R2 of 0.712. In the validation group with 85 patients, difference between the predicted and actual FIM scores at discharge was compared between the two groups, and a strong correlation of r=0.888 (p＜0.01) was observed, without any significant difference between the groups.Conclusion：This model is less labor intensive when constructing a prediction equation because the objective variable is easily obtained. Our findings indicate that other convalescent rehabilitation wards can develop prognostic models using their data and implement them clinically.