ObjectiveTo investigate the clinical symptom and risk factors of diabetic seizures. MethodsThe clinical data of 44 patients with diabetes related seizures were analyzed with the clinical classification, blood glucose, Na+, Plasma Osmotic Pressure, HbA1c, EEG, brain MR, and the antiepileptic drugs. Results① Diabetic hyperglycemia (DH) related seizures: among the 28 patients, 17 cases were male patients, 11 cases were female patients. The mean age was 51.3 years old. Simple partial seizure without secondary generalized seizures (12/28, 42.8%) was the most common, 8 patients (8/28, 28.6%) showed complex partial seizure, 8 patients (8/28, 28.6%) showed no obvious focal origin generalized tonic-closure seizures. Patients with poor glycemic control (HbA1c > 9%) had significantly higher risk of generalized seizures (46.7% vs. 7.7 %, P < 0.05) (P < 0.05). ② Diabetic ketoa-cidosis or hypertonic state associated seizures: among the 7 patients, 6 cases were male patients, 1case was female patients. The mean age was 45.7 years old, 2 patients (2/7, 28.6%) had generalized tonic-clonic seizure, 2 patients (2/7, 28.6%) showed status epilepticus, 2 patients (2/7, 28.6%) showed local motor seizure, 1 patient (1/7, 14.2%) showed Jackson seizure. ③ Diabetic hypoglycemia related seizures: among the 9 patients, 7 cases were male patients, 2 cases were female patients. The mean age was 45.3 years old.5 patients showed generalized tonic-clonic seizure (5/9, 55.6%), 3 patients had complex partial seizure (3/9, 33.3%), 1 patients had generalized tonic-closure seizures (1/9, 11.1%). ConclusionSimple partial seizure is the most common in patients with diabetic hyperglycemia related seizures; so as to diabetic hypoglycemia and keto-acidosis, generalized seizures are relatively common. HbA1c can be an important risk factor of seizures for patients with hyperglycemia.
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.
ObjectiveTo explore and clarify the relationship between epileptic seizure and inducing factors. Avoid inducing factors and reduce epileptic seizure, so as to improve the quality of life in patients with epilepsy.MethodsClinical data of 604 patients diagnosed with epilepsy in Xijing Hospital of Air Force Military Medical University from January 2018 to January 2019 were collected. The clinical data of patients with epilepsy were followed up 6 months.ResultsAmong the 604 patients, 318 (52.6%) were seizure-free in the last 6 months, 286 (47.4%) had seizures. 169 (59.1%) had seizures with at least one inducing factor. Common inducing factors: 123 cases of sleep disorder (72.8%), 114 cases of emotion changes (67.5%), 87 cases of irregular medication (51.5%), 97 cases of diet related (57.4%), 33 cases of menstruation and pregnancy (19.5%), etc. Using the χ2 test, seizures with age, gender differences had no statistical significance (P > 0.05), but seizure type was statistically different between inducing factors. In generalized seizures, tonic-clonic seizures associated with sleep deprivation (χ2= 0.189), absence seizures and anger (χ2= 0.237), pressure (χ2= 0.203), irregular life (χ2= 0.214). In the focal seizures, focal motor seizures was correlated with coffee consumption (χ2=0.145), focal sensory seizures with cold (χ2=0.235), electronic equipment use (χ2 =0.153), satiety (χ2 =0.257). Complex partial seizures was correlated with anger (χ2 =0.229), stress (χ2 =0.187), and cold (χ2 =0.198). The secondarily generalized seizures was correlated with drug missing (χ2 =0.231), sleep deprivation (χ2 =0.158), stress (χ2 =0.161), cold (χ2 =0.263), satiety (χ2 =0.182). Among the inducing factors, sleep deprivation was correlated with anger (χ2 =0.167), fatigue (χ2 =0.283), and stress (χ2 =0.230).ConclusionsEpileptic seizure were usually induced by a variety of factors. Generalized seizures were associated with sleep disorders, emotional changes, stress, irregular life, etc. While focal seizures were associated with stress, emotional changes, sleep disorders, cold, satiety, etc. An analysis of the triggers found that sleep deprivation was associated with anger, fatigue, and stress. Therefore, to clarify the inducing factors of epileptic seizure, avoid the inducing factors as much as possible, reduce the harm caused by seizures, and improve the quality of life of patients.
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.