ObjectiveTo summarize clinical electrophysiological features and efficacy of some of Anti-epileptic drugs(AEDs) of Juvenile myoclonic epilepsy (JME). MethodsClinical electrophysiological information of 101 outpatients with JME observed at Xuanwu Hospital from Jul. 2001 to Sep. 2014 was retrospectively analyzed, including the seizure types, trigger factors, electroencephalogram. We followed some of these patients and compared the efficacy between different AEDs. Result According to different seizure types, there are four subtypes: Myoclonus (MJ) only 11.88%, MJ+generalized tonic-clonic seizure(GTCS) 75.24%, MJ+GTCS+Absence(Abs) 11.88%, MJ+Abs 1.00%. Patients with typical ictal generalized poly-spike and waves (PSW) or spike and waves (SW) or spikes account for 96.80%. And 75.00% of patients have no MJ and 91.80% have no GTCS with valproic acid monotherapy. 65.00% and 88.24% of patients were seizure free of MJ and GTCS recpectively. But the difference of efficacy between these two drugs have no statistically significance. Sleep deprivation was the primary trigger factors, accounting for 16.83%. ConclusionJME has clinical heterogeinety, clinicians should fully understand the whole condition of JME individual, including their clinical manifestation, EEG features, reaction to AEDs, trigger factors, habitual patterns and so on, in order to help making individualized therapy.
Seizures during sleep increase the probability of complication and sudden death. Effective prediction of seizures in sleep allows doctors and patients to take timely treatments to reduce the aforementioned probability. Most of the existing methods make use of electroencephalogram (EEG) to predict seizures, which are not specific developed for the sleep. However, EEG during sleep has its characteristics compared with EEG during other states. Therefore, in order to improve the sensitivity and reduce the false alarm rate, this paper utilized the characteristics of EEG to predict seizures during sleep. We firstly constructed the feature vector including the absolute power spectrum, the relative power spectrum and the power spectrum ratio in different frequencies. Secondly, the separation criterion and branch-and-bound method were applied to select features. Finally, support vector machine classifier were trained, which is then employed for online prediction. Compared with the existing method that do not consider the characteristics of sleeping EEG (sensitivity 91.67%, false alarm rate 9.19%), the proposed method was superior in terms of sensitivity (100%) and false alarm rate (2.11%). This method can improve the existing epilepsy prediction methods and has important clinical value.
ObjectiveTo evaluate the role of several examinations in the presurgical localization of insular/peri-insular cortex epilepsy (IPICE). MethodsThe data of patients with IPICE who were identified by resective surgery from 2011.1 to 2015.4 were retrospectively analyzed. The role of semiology, scalp EEG, MRI and magnetoencephalography (MEG)in the localization of epileptogenic zones for patients with IPICE were evaluated. Results18 patients were selected according to the criteria. The localization of IPICE was supported by semiology in 16 patients, supported by MRI in 6 patients, supported by MEG in 17 patients. In 12 patients with negative MRI, the dipoles were showed in insular/peri-insular area in 11 patients. The localization role of MEG for patients with IPICE is more obvious than that of MRI (P < 0.05). The MEG result played conclusive role in 9 patients. According to result of MEG, the plans of intracranial recording were canceled in 3 patients, and the plans of intracranial electrodes implanting were modified in 5 patients. The resective surgery involving the insular/peri-insular cortex was performed in all the 18 patients. During the follow-up of 12~48 months, seizure-free was reported in 11 patients, although 2 patients were missed. ConclusionThe combination of the results of semiology, scalp EEG, MRI and MEG was helpful in the localization of epileptogenic zones for patients with IPICE, and MEG played a valuable role in this localization.
Studies have shown that the clinical manifestation of patients with neuropsychiatric disorders might be related to the abnormal connectivity of brain functions. Psychogenic non-epileptic seizures (PNES) are different from the conventional epileptic seizures due to the lack of the expected electroencephalographically epileptic changes in central nervous system, but are related to the presence of significant psychological factors. Diagnosis of PNES remains challenging. We found in the present work that the connectivity between the frontal and parieto-occipital in PNES was weaker than that of the controls by using network analysis based on electroencephalogram (EEG) signals. In addition, PNES were recognized by using the network properties as linear discriminant nalysis (LDA) input and classification accuracy was 85%. This study may provide a feasible tool for clinical diagnosis of PNES.