RESUMO
The effects of monotherapy with lamotrigine on health-related quality of life were compared with those of valproate monotherapy in a randomized, double-blind trial designed to evaluate treatment-emergent weight changes in patients with epilepsy. At the end of 8 months of treatment, significantly more patients using lamotrigine compared with valproate experienced quality-of-life improvements on the Health Perceptions (42% vs 15%), Energy/Fatigue (47% vs 28%), and Social Isolation (35% vs 16%) subscales of the Quality of Life in Epilepsy-89 (QOLIE-89) questionnaire (P<0.05). Compared with valproate-treated patients, lamotrigine-treated patients were four times more likely to experience improvement in Health Perceptions, 2.3 times more likely to experience improvement in Energy/Fatigue, and 2.8 times more likely to experience improvement in Social Isolation (P<0.05). These quality-of-life improvements are consistent with the improvements in mood measured with the Beck Depression Inventory, the Cornell Dysthymia Rating Scale, and the Profile of Mood States among patients receiving lamotrigine. These data show that lamotrigine monotherapy provides benefits over valproate monotherapy in improving several aspects of health-related quality of life in patients with epilepsy. The observation that quality-of-life improvements during lamotrigine monotherapy occurred concurrently with improvements in mood suggests that the quality-of-life and mood changes may be causally related.
RESUMO
A recurrence time statistics T1 is defined and used as a feature extraction method for seizure detection. The preliminary data shows that during seizure T1 generates a peak and this peak clearly distinguishes the seizure state from background activity. When applied to multi-channel ECoG recordings, the spatial-temporal signature of T1 can be clearly observed to discriminate seizures. The T1 feature was used for automated seizure detection on two sets of long term monitoring ECoG data. The detection probability reached 97% with a 0.29 per hour average false alarm rate.
RESUMO
A data efficient blind sources separation (BSS) algorithm has been applied to preprocess intracranial EEG (ECoG) for artifact rejection. After artifacts correction a recurrence time statistics T1 feature was evaluated from the 'cleaned' data. Seizure detection performance was compared between BSS preprocessing and without preprocessing. Test results show that in a data set, for a detection rate of 96%, the false alarm rate dropped from 0.13 per hour without BSS preprocessing to 0.08 with preprocessing. For the other set of data, the false alarm rate dropped from 0.34 to 0.21 at a detection rate of 100%.