ObjectiveTo understand the relationship between the anatomy and the function of the insula lobe cortex based on the stereo-electro encephalography (SEEG) by direct electric stimulation of the insula cortex performed in the patients who suffered from the refractory epilepsy. MethodsRetrospective review was performed on 12 individuals with refractory epilepsy who were diagnosed in the Department of Functional neurosurgery of RenJi Hospital from December 2013 to September 2015. We studied all the SEEG electrodes implanted in the brain with contacts in the insula cortex. Direct electric stimulation was given to gain the brain mapping of the insula. Results12 consecutive patients with refractory epilepsy were implanted SEEG electrodes into the insula cortex. In all, 176 contacts were in the insula cortex, and 154 were included. The main clinical manifestations obtained by the stimulation were somatosensory abnormalities, laryngeal constriction, dyspnea, nausea, flustered. While somatosensory symptoms were located in the posterior insula, visceral sensory symptoms distribute relatively in the anterior insula, and other symptoms were mainly in the central and anterior part. ConclusionsThe symptoms of the insula present mainly according to the anatomy, but some of them are mixed. In addition, the manifestations of the insula are usually complex and individually.
ObjectiveThe aim of this study is to identify clinical and electroencephalographic features associated with refractoriness to the initial antiepileptic drug in typical benign childhood epilepsy with centrotemporal spikes (BECTS). MethodsA total of 87 children with typical BECTS were retrospectively reviewed in the analyses.The patients were subdivided into two groups:patients whose seizures were controlled with monotherapy, and those requiring two medications. 63 childrenachieved seizure-freedom with monotherapy, while 24 received two medications for seizure control. ResultsDiffusing foci at the follow-up EEG and delayed treatment (duration > 1 year) are two main risk factors associated with more refractory cases (P < 0.001). Delayed diagnosis (37.1%) and non-adherence to treatment (57.2%) contributed to delayed treatment. ConclusionsOur findings suggested that diffusing foci on EEG and delayed treatment are associated with more frequent seizures and refractoriness in BECTS. Diagnostic delays and non-adherence hindered timely care, which may represent opportunities for improved intervention.
ObjectiveTo explore the prognostic value of normal 24 hour video electroencephalography (VEEG) with different frequency on antiepileptic drugs (AEDs) withdrawal in cryptogenic epilepsy patients with three years seizure-free. MethodsA retrospective study was conducted in the Neurology outpatient and the Epilepsy Center of Xi Jing Hospital. The subject who had been seizure free more than 3 years were divided into continual normal twice group and once group according to the nomal frequence of 24 hour VEEG before discontinuation from January 2013 to December 2014, and then followed up to replase or to December 2015. The recurrence and cumulative recurrence rate of the two group after withdrawal AEDs were compared with chi-square or Fisher's exact test and Kaplan-Meier survival curve. A Cox proportional hazard model was used for multivariate analysis to identify the risk factors for seizure recurrence after univariate analysis. P value < 0.05 was considered significant, and all P values were two-tailed. Results95 epilepsy patients with cause unknown between 9 to 45 years old were recruited (63 in normal twice group and 32 in normal once group). The cumulated recurrence rates in continual two normal VEEG group vs one normal VEEG group were 4.8% vs 21.9% (P=0.028), 4.8% vs 25% (P=0.006) and 7.9% vs 25%(P=0.03) at 18 months, 24 months and endpoint following AEDs withdrawal and there was statistically difference between the two groups. Factors associated with increased risk were adolescent onset epilepsy (HR=2.404), history of withdrawal recurrence (HR=7.186) and abnormal VEEG (epileptic-form discharge) (HR=8.222) during or after withdrawal AEDs. The recurrence rate of each group in which abnormal VEEG vs unchanged VEEG during or after withdrawal AEDs was respectively 100% vs 4.92% (P=0.005), 80% vs 19.23%(P=0.009). ConclusionsContinual normal 24h VEEG twice before withdrawal AEDs had higher predicting value of seizure recurrence and it could guide physicians to make the withdrawal decision. Epileptic patients with adolescent onset epilepsy, history of seizure recurrence and abnormal VEEG (epileptic-form discharge) during or after withdrawal AEDs had high risk of replase, especially patients with the presence of VEEG abnormalities is associated with a high probability of seizures occurring. Discontinuate AEDs should be cautious.
ObjectiveTo investigate characteristics of motor semiology of epileptic seizure originated from dorsolateral frontal lobe. MethodsRetrospectively analysis the clinical profiles of patients who were diagnosed dorsolateral frontal lobe epilepsy (FLE) based on stereoelectroencephalography (SEEG) and underwent respective surgeries subsequently. Component of motor semiology in a seizure can be divided into elementary motor (EM, include tonic, versive, clonic, and myoclonic seizures) and complex motor (CM, include automotor, hypermotor, and so on). A Talairach coordinate system was constructed in the sagittal series of MRI images in each case. From the cross point of VAC and the Sylvian Fissure, a line was drawn antero-superiorly, which made an angle of 60° with the AC-PC line, then the frontal lobe could be divided into anterior and posterior portion. The epileptogenic zone, which was defined as ictal onset and early spreading zone in SEEG, was classified into three types, according to the positional relationship of the responding electrodes contacts and the "60° line": the anterior, posterior, and intermediate FLE. The correlation of the components of motor semiology in seizures and the location of the epileptogenic zone was analyzed. ResultsFive cases (26.3%) were verified as anterior FLE, among which there were 2 of EM, one of CM, and 2 of EM+CM. In 7 cases (36.8%) of intermediate FLE, there were one of EM, none of CM, and 6 of EM+CM. In the rest 7 cases of posterior FLE, there were 6 of EM, none of CM, and one of EM+CM. Compared with the cases that the epileptogenic zone involved anterior portion, the posterior FLE is more likely to present EM seizures (85.7%), and less likely to show CM components (P < 0.05). And Compared with the anterior FLE and posterior FLE, the intermediate FLE is more likely to present EM+CM seizures (85.7%)(P < 0.05). ConclusionThe motor seizure semiology of dorsolateral FLE has significant correlation with the localization of the epileptogenic zone. Posterior FLE mainly present a pure elementary motor seizure, and once the epileptogenic zone involved anteriorly beyond the "60° line", the component of complex motor seizure would be seen. Intermediate FLE, as its specialty of transboundary, is more likely to show "comprised semiology" of EM and CM. Construction of the "60° line" with AC-PC coordinate system in the MRI images may play an useful role in semiology analysis in presurgical evaluation of FLE.
ObjectiveTo evaluate the application of stereotactic electrode implantation on precise epileptogenic zone localization. MethodRetrospectively studied 140 patients with drug-resist epilepsy from March 2012 to June 2015, who undergone a procedure of intracranial stereotactic electrode for localized epileptogenic zone. ResultsIn 140 patients who underwent the ROSA navigated implantation of intracranial electrode, 109 are unilateral implantation, 31 are bilateral; 3 patients experienced an intracranial hematoma caused by the implantation. Preserved time of electrodes, on average, 8.4days (range 2~35 days); Obseved clinical seizures, on average, 10.8 times per pt (range 0~98 times); There were no cerebrospinal fluid leak, intracranial hematoma, electrodes fracture or patient death, except 2 pt's scalp infection (1.43%, scalp infection rate); 131 pts' seizure onset area was precisely localized; 71 pts underwent SEEG-guide resections and were followed up for more than 6 months. In the group of 71 resection pts, 56 pts were reached Engel I class, 2 were Engel Ⅱ, 3 was Engel Ⅲ and 10 were Engel IV class. ConclusionTo intractable epilepsy, when non-invasive assessments can't find the epileptogenic foci, intracranial electrode implantation combined with long-term VEEG is an effective method to localize the epileptogenic foci, especially the ROSA navigated stereotactic electrode implantation, which is a micro-invasive, short-time, less-complication, safe-guaranteed, and precise technique.
The quality of sleep has a great relationship with health and working efficiency. The result of sleep stage classification is an important indicator to measure the quality of sleep, and it is also an important way to diagnose and treat sleep disorders. In this paper, the method of detrended cross-correlation analysis (DCCA) was used to analyze sleep stage classification, sleep electroencephalograph signals, which were extracted from the MIT-BIH Polysomnographic Database randomly. The results showed that the average DCCA exponent of the awake period is smaller than that of the first stage of non-rapid eye movement (NREM) sleeps. It is well concluded that the method of studying the sleep electroencephalograph with this method is of great significance to improve the quality of sleep, to diagnose and to treat sleep disorders.
The purpose of using brain-computer interface (BCI) is to build a bridge between brain and computer for the disable persons, in order to help them to communicate with the outside world. Electroencephalography (EEG) has low signal to noise ratio (SNR), and there exist some problems in the traditional methods for the feature extraction of EEG, such as low classification accuracy, lack of spatial information and huge amounts of features. To solve these problems, we proposed a new method based on time domain, frequency domain and space domain. In this study, independent component analysis (ICA) and wavelet transform were used to extract the temporal, spectral and spatial features from the original EEG signals, and then the extracted features were classified with the method combined support vector machine (SVM) with genetic algorithm (GA). The proposed method displayed a better classification performance, and made the mean accuracy of the Graz datasets in the BCI Competitions of 2003 reach 96%. The classification results showed that the proposed method with the three domains could effectively overcome the drawbacks of the traditional methods based solely on time-frequency domain when the EEG signals were used to describe the characteristics of the brain electrical signals.
Currently, monitoring system of awareness of the depth of anesthesia has been more and more widely used in clinical practices. The intelligent evaluation algorithm is the key technology of this type of equipment. On the basis of studies about changes of electroencephalography (EEG) features during anesthesia, a discussion about how to select reasonable EEG parameters and classification algorithm to monitor the depth of anesthesia has taken place. A scheme which combines time domain analysis, frequency domain analysis and the variability of EEG and decision tree as classifier and least squares to compute Depth of anesthesia Index (DOAI) is proposed in this paper. Using the EEG of 40 patients who underwent general anesthesia with propofol, and the classification and the score of the EEG annotated by anesthesiologist, we verified this scheme with experiments. Classification and scoring was based on a combination of modified observer assessment of alertness/sedation (MOAA/S), and the changes of EEG parameters of patients during anesthesia. Then we used the BIS index to testify the validation of the DOAI. Results showed that Pearson's correlation coefficient between the DOAI and the BIS over the test set was 0.89. It is demonstrated that the method is feasible and has good accuracy.
Brain-computer interface (BCI) systems identify brain signals through extracting features from them. In view of the limitations of the autoregressive model feature extraction method and the traditional principal component analysis to deal with the multichannel signals, this paper presents a multichannel feature extraction method that multivariate autoregressive (MVAR) model combined with the multiple-linear principal component analysis (MPCA), and used for magnetoencephalography (MEG) signals and electroencephalograph (EEG) signals recognition. Firstly, we calculated the MVAR model coefficient matrix of the MEG/EEG signals using this method, and then reduced the dimensions to a lower one, using MPCA. Finally, we recognized brain signals by Bayes Classifier. The key innovation we introduced in our investigation showed that we extended the traditional single-channel feature extraction method to the case of multi-channel one. We then carried out the experiments using the data groups ofⅣ_ⅢandⅣ_Ⅰ. The experimental results proved that the method proposed in this paper was feasible.
Sleep stage scoring is a hotspot in the field of medicine and neuroscience. Visual inspection of sleep is laborious and the results may be subjective to different clinicians. Automatic sleep stage classification algorithm can be used to reduce the manual workload. However, there are still limitations when it encounters complicated and changeable clinical cases. The purpose of this paper is to develop an automatic sleep staging algorithm based on the characteristics of actual sleep data. In the proposed improved K-means clustering algorithm, points were selected as the initial centers by using a concept of density to avoid the randomness of the original K-means algorithm. Meanwhile, the cluster centers were updated according to the 'Three-Sigma Rule' during the iteration to abate the influence of the outliers. The proposed method was tested and analyzed on the overnight sleep data of the healthy persons and patients with sleep disorders after continuous positive airway pressure (CPAP) treatment. The automatic sleep stage classification results were compared with the visual inspection by qualified clinicians and the averaged accuracy reached 76%. With the analysis of morphological diversity of sleep data, it was proved that the proposed improved K-means algorithm was feasible and valid for clinical practice.