ObjectiveTo investigate the efficacy and safety of the phase Ⅰ corpus callosotomy in the treatment of adult refractory epilepsy. MethodsWe conducted a retrospective analysis of 56 adults with intractable epilepsy in Tangdu Hospital from January 2011 to July 2016.All patients were treated for the phase Ⅰ total corpus callosotomy, followed up 1~5 years after surgery. Results14 cases (25.0%) patients achieved complete seizure free after surgery, 19 cases (33.9%) whose seizures reduced more than 90%, 10 cases (17.9%) reduced between 50%~90%, 7 cases (12.5%) between 30%~50%, 6 cases (10.7%) decreased below 30%; Drop attacks of 47 cases (83.9%) patients disappeared. Postoperative complications occurred in 13 cases(23.2%), and most of them recovered well. 5 cases(8.9%) had long-term sensory disassociation, no serious complications and death. The percentage of patients reporting improvement in quality of life was 67.9%. ConclusionsFor patients with intractable epilepsy who can not undergo focal resection, Ⅰ phase total corpus callosotomy has a certain effect on reducing seizure frequency, eliminating drop attacks, and improving the quality of life.
In recent years, epileptic seizure detection based on electroencephalogram (EEG) has attracted the widespread attention of the academic. However, it is difficult to collect data from epileptic seizure, and it is easy to cause over fitting phenomenon under the condition of few training data. In order to solve this problem, this paper took the CHB-MIT epilepsy EEG dataset from Boston Children's Hospital as the research object, and applied wavelet transform for data augmentation by setting different wavelet transform scale factors. In addition, by combining deep learning, ensemble learning, transfer learning and other methods, an epilepsy detection method with high accuracy for specific epilepsy patients was proposed under the condition of insufficient learning samples. In test, the wavelet transform scale factors 2, 4 and 8 were set for experimental comparison and verification. When the wavelet scale factor was 8, the average accuracy, average sensitivity and average specificity was 95.47%, 93.89% and 96.48%, respectively. Through comparative experiments with recent relevant literatures, the advantages of the proposed method were verified. Our results might provide reference for the clinical application of epilepsy detection.
ObjectiveConstructing a prediction model for seizures after stroke, and exploring the risk factors that lead to seizures after stroke. MethodsA retrospective analysis was conducted on 1 741 patients with stroke admitted to People's Hospital of Zhongjiang from July 2020 to September 2022 who met the inclusion and exclusion criteria. These patients were followed up for one year after the occurrence of stroke to observe whether they experienced seizures. Patient data such as gender, age, diagnosis, National Institute of Health Stroke Scale (NIHSS) score, Activity of daily living (ADL) score, laboratory tests, and imaging examination data were recorded. Taking the occurrence of seizures as the outcome, an analysis was conducted on the above data. The Least absolute shrinkage and selection operator (LASSO) regression analysis was used to screen predictive variables, and multivariate Logistic regression analysis was performed. Subsequently, the data were randomly divided into a training set and a validation set in a 7:3 ratio. Construct prediction model, calculate the C-index, draw nomogram, calibration plot, receiver operating characteristic (ROC) curve, and decision curve analysis (DCA) to evaluate the model's performance and clinical application value. ResultsThrough LASSO regression, nine non-zero coefficient predictive variables were identified: NIHSS score, homocysteine (Hcy), aspartate aminotransferase (AST), platelet count, hyperuricemia, hyponatremia, frontal lobe lesions, temporal lobe lesions, and pons lesions. Multivariate logistic regression analysis revealed that NIHSS score, Hcy, hyperuricemia, hyponatremia, and pons lesions were positively correlated with seizures after stroke, while AST and platelet count were negatively correlated with seizures after stroke. A nomogram for predicting seizures after stroke was established. The C-index of the training set and validation set were 0.854 [95%CI (0.841, 0.947)] and 0.838 [95%CI (0.800, 0.988)], respectively. The areas under the ROC curves were 0.842 [95%CI (0.777, 0.899)] and 0.829 [95%CI (0.694, 0.936)] respectively. Conclusion These nine variables can be used to predict seizures after stroke, and they provide new insights into its risk factors.
ObjectiveTo analyze the risk factors for seizures in patients with autoimmune encephalitis (AE) and to assess their predictive value for seizures. MethodsSeventy-four patients with AE from the First Affiliated Hospital of Xinjiang Medical University from January 2016 to March 2023 were collected and divided into seizure group (56 cases) and non-seizure group (18 cases), comparing the general clinical information, laboratory tests and imaging examinations and other related data of the two groups. The risk factors for seizures in AE patients were analyzed by multifactorial logistic regression, and their predictive value was assessed by receiver operating characteristic (ROC) curves. ResultsThe seizure group had a higher proportion of acute onset conditions in the underlying demographics compared with the non-seizure group (P<0.05). Laboratory data showed statistically significant differences in neutrophil count, calcitoninogen, lactate dehydrogenase, C-reactive protein, homocysteine, and interleukin-6 compared between the two groups (all P<0.05). Multi-factor logistic regression analysis of the above differential indicators showed that increased C-reactive protein [Odds ratio (OR)=4.621, 95% CI (1.123, 19.011), P=0.034], high homocysteine [OR=12.309, 95CI (2.217, 68.340), P=0.004] and onset of disease [OR=4.918, 95% CI (1.254, 19.228), P=0.022] were risk factors for seizures in AE patients, and the area under the ROC curve for the combination of the three indicators to predict seizures in AE patients was 0.856 [95% CI (0.746, 0.966)], with a sensitivity of 73.2% and a specificity of 83.3%. ConclusionHigh C-reactive protein, high homocysteine and acute onset are independent risk factors for seizures in patients with AE, and the combination of the three indices can better predict seizure status in patients.
Lennox-Gastaut syndrome (LGS) is a refractory epileptic encephalopathy that mainly affects children, but can also involve adults, and is characterized by multiple seizure types, electroencephalographic (EEG) abnormalities, and mental retardation. This review focuses on the etiology, pathogenesis, diagnostic criteria, and treatment of LGS. In terms of etiology, LGS may be caused by a variety of factors such as abnormal brain development, perinatal brain injury, inherited metabolic diseases, and gene mutations. The pathogenesis involves multiple gene mutations that affect the balance of neuronal excitability and inhibition.LGS is diagnosed on the basis of multiple seizure types with an age of onset of less than 18 years, an EEG that shows widespread slow (1.5~2.5 Hz) spiking slow complex waves, and a triad of intellectual and psychosocial dysfunction. Therapeutically, LGS is treated with antiepileptic seizure medications (ASMs) , including valproate, lamotrigine, and rufinamide, but patients often develop resistance to ASMs. Non-pharmacological treatments include ketogenic diet, vagus nerve stimulation (VNS) , and corpus callosotomy (CC) , which provide palliative treatment options for patients who have difficulty controlling seizures. Despite the variety of therapeutic options, the prognosis for LGS is usually poor, with patients often experiencing intellectual disability and seizures persisting into adulthood. This review emphasizes the importance of further research into the etiology and pathogenesis of LGS and the need to develop new therapeutic approaches to improve patients' quality of life and reduce the burden of disease.