[Application of scalp electroencephalogram in treatment of refractory epilepsy with vagus nerve stimulation]

[Construction and analysis of brain metabolic network in temporal lobe epilepsy patients based on 18F-FDG PET]

The establishment of brain metabolic network is based on 18fluoro-deoxyglucose positron emission computed tomography ( 18F-FDG PET) analysis, which reflect the brain functional network connectivity in normal physiological state or disease state. It is now applied to basic and clinical brain functional network research. In this paper, we constructed a metabolic network for the cerebral cortex firstly according to 18F-FDG PET image data from patients with temporal lobe epilepsy (TLE).Then, a statistical analysis to the network properties of patients with left or right TLE and controls was performed. It is shown that the connectivity of the brain metabolic network is weakened in patients with TLE, the topology of the network is changed and the transmission efficiency of the network is reduced, which means the brain metabolic network connectivity is extensively impaired in patients with TLE. It is confirmed that the brain metabolic network analysis based on 18F-FDG PET can provide a new perspective for the diagnose and therapy of epilepsy by utilizing PET images.

[Research on electroencephalogram power spectral density of stroke patients under transcranial direct current stimulation]

Transcranial direct current stimulation (tDCS) has become a new method of post-stroke rehabilitation treatment and is gradually accepted by people. However, the neurophysiological mechanism of tDCS in the treatment of stroke still needs further study. In this study, we recruited 30 stroke patients with damage to the left side of the brain and randomly divided them into a real tDCS group (15 cases) and a sham tDCS group (15 cases). The resting EEG signals of the two groups of subjects before and after stimulation were collected, then the difference of power spectral density was analyzed and compared in the band of delta, theta, alpha and beta, and the delta/alpha power ratio (DAR) was calculated. The results showed that after real tDCS, delta band energy decreased significantly in the left temporal lobes, and the difference was statistically significant ( P < 0.05); alpha band energy enhanced significantly in the occipital lobes, and the difference was statistically significant ( P < 0.05); the difference of theta and beta band energy was not statistically significant in the whole brain region ( P > 0.05). Furthermore, the difference of delta, theta, alpha and beta band energy was not statistically significant after sham tDCS ( P > 0.05). On the other hand, the DAR value of stroke patients decreased significantly after real tDCS, and the difference was statistically significant ( P < 0.05), and there was no significant difference in sham tDCS ( P > 0.05). This study reveals to a certain extent the neurophysiological mechanism of tDCS in the treatment of stroke.

[Intelligence-aided diagnosis of Parkinson's disease with rapid eye movement sleep behavior disorder based on few-channel electroencephalogram and time-frequency deep network]

Aiming at the limitations of clinical diagnosis of Parkinson's disease (PD) with rapid eye movement sleep behavior disorder (RBD), in order to improve the accuracy of diagnosis, an intelligent-aided diagnosis method based on few-channel electroencephalogram (EEG) and time-frequency deep network is proposed for PD with RBD. Firstly, in order to improve the speed of the operation and robustness of the algorithm, the 6-channel scalp EEG of each subject were segmented with the same time-window. Secondly, the model of time-frequency deep network was constructed and trained with time-window EEG data to obtain the segmentation-based classification result. Finally, the output of time-frequency deep network was postprocessed to obtain the subject-based diagnosis result. Polysomnography (PSG) of 60 patients, including 30 idiopathic PD and 30 PD with RBD, were collected by Nanjing Brain Hospital Affiliated to Nanjing Medical University and the doctor's detection results of PSG were taken as the gold standard in our study. The accuracy of the segmentation-based classification was 0.902 4 in the validation set. The accuracy of the subject-based classification was 0.933 3 in the test set. Compared with the RBD screening questionnaire (RBDSQ), the novel approach has clinical application value.

Early Prediction of Refractory Epilepsy in Children Under Artificial Intelligence Neural Network

In order to realize the early prediction of refractory epilepsy in children, data preprocessing technology was used to improve the data quality, and the detection model of refractory epilepsy in children based on convolutional neural network (CNN) was established. Then, the data in the epilepsy electroencephalography (EEG) signal public data set was used for model training and the diagnosis of refractory epilepsy in children. Moreover, back propagation neural network (BPNN), support vector machine (SVM), XGBoost, gradient boosting decision tree (GBDT), AdaBoost algorithm were introduced for comparison. The results showed that the early prediction accuracy of BP, SVM, XGBoost, GBDT, AdaBoost, and the algorithm in this study for refractory epilepsy in children were 0.745, 0.778, 0.885, 0.846, 0.874, and 0.941, respectively. The sensitivities were 0.81, 0.826, 0.822, 0.84, 0.859, and 0.918, respectively. The specificities were 0.683, 0.696, 0.743, 0.792, 0.84, and 0.905, respectively. The accuracy was 0.707, 0.732, 0.765, 0.802, 0.839, and 0.881, respectively. The recall rates were 0.69, 0.716, 0.753, 0.784, 0.813, and 0.877, respectively. F1 scores were 0.698, 0.724, 0.759, 0.793, 0.826, and 0.879, respectively. Through the comparisons of the above six indicators, the algorithm proposed in this study was significantly higher than other algorithms, suggesting that the proposed algorithm was more accurate in early prediction of refractory epilepsy in children. Analysis of the EEG characteristics and magnetic resonance imaging (MRI) images of refractory epilepsy in children suggested that the MRI images of patients' brains under this algorithm had obvious characteristics. The reason for the prediction error of the algorithm was that the duration of epilepsy was too short or the EEG of the patient didn't change notably during the epileptic seizure. In summary, the prediction method of refractory epilepsy in children based on CNN was accurate, which had broad adoption prospects in assisting clinicians in the examination and diagnosis of refractory epilepsy in children.