Stereo-electroencephalography (SEEG) is widely used to record the electrical activity of patients' brain in clinical. The SEEG-based epileptogenic network can better describe the origin and the spreading of seizures, which makes it an important measure to localize epileptogenic zone (EZ). SEEG data from six patients with refractory epilepsy are used in this study. Five of them are with temporal lobe epilepsy, and the other is with extratemporal lobe epilepsy. The node outflow (out-degree) and inflow (in-degree) of information are calculated in each node of epileptic network, and the overlay between selected nodes and resected nodes is analyzed. In this study, SEEG data is transformed to bipolar montage, and then the epileptic network is established by using independent effective coherence (iCoh) method. The SEEG segments at onset, middle and termination of seizures in Delta, Theta, Alpha, Beta, and Gamma rhythms are used respectively. Finally, the K-means clustering algorithm is applied on the node values of out-degree and in-degree respectively. The nodes in the cluster with high value are compared with the resected regions. The final results show that the accuracy of selected nodes in resected region in the Delta, Alpha and Beta rhythm are 0.90, 0.88 and 0.89 based on out-degree values in temporal lobe epilepsy patients respectively, while the in-degree values cannot differentiate them. In contrast, the out-degree values are higher outside the temporal lobe in the patient with extratemporal lobe epilepsy. Based on the out-degree feature in low-frequency epileptic network, this study provides a potential quantitative measure for identifying patients with temporal lobe epilepsy in clinical.
The brain-computer interface (BCI) based on motor imagery electroencephalography (EEG) shows great potential in neurorehabilitation due to its non-invasive nature and ease of use. However, motor imagery EEG signals have low signal-to-noise ratios and spatiotemporal resolutions, leading to low decoding recognition rates with traditional neural networks. To address this, this paper proposed a three-dimensional (3D) convolutional neural network (CNN) method that learns spatial-frequency feature maps, using Welch method to calculate the power spectrum of EEG frequency bands, converted time-series EEG into a brain topographical map with spatial-frequency information. A 3D network with one-dimensional and two-dimensional convolutional layers was designed to effectively learn these features. Comparative experiments demonstrated that the average decoding recognition rate reached 86.89%, outperforming traditional methods and validating the effectiveness of this approach in motor imagery EEG decoding.
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.
ObjectiveThe aim was to summarize the seizure and video electroencephalogram (VEEG) characteristics of Dyke-Davidoff-Masson syndrome (DDMS). Methods The case data of four patients with Dyke-Davidoff-Masson syndrome (DDMS) who attended the Epilepsy Center of Hunan Provincial Brain Hospital from March 2022 to March 2023 were retrospectively analyzed to summarize the clinical manifestations of their seizures and the characteristics of their video electroencephalogram (VEEG). Results One case of symptomatic epilepsy with focal seizures; VEEG showed poor background activity alpha rhythmic modulation, amplitude modulation, and increased distribution of slow wave activity in the left frontal and temporal regions; bilateral frontal-central and anterior-temporal regions (more so on the left side), with sharp and slow composite wave issuance.Two cases of symptomatic epilepsy with focal seizures progressing to generalized seizures; in one case, the VEEG showed: background activity α-rhythmic modulation, amplitude modulation is possible, the left frontal, central, anterior temporal region slow wave increase; the left frontal central, parietal anterior temporal region spike-like slow wave activity mixed with spike wave, spike-slow complex wave short-medium-range issuance; the other VEEG showed: background activity α-rhythmic modulation, amplitude modulation is possible, the right frontal central, anterior temporal region slow wave increase; right frontal, central, and anterior temporal region for the famous medium-extremely high-high-amplitude slow wave activity mixed with spike wave, spike-slow complex wave short-medium-range issuance. One case of symptomatic epilepsy with generalized seizures; VEEG showed bilateral occipital alpha rhythm asymmetry, right occipital region <50% of the left side, poor regulation and amplitude modulation; bilateral frontal pole, frontal region, anterior temporal region spike and spiking slow complex wave discharges (right side was prominent), and right pterionic electrodes, anterior temporal and mesial temporal spike and spiking slow wave discharges. Conclusions Epileptic seizures are one of the main clinical manifestations of DDMS and most of them are consulted after a seizure, and their seizure types tend to be focal seizures or progress to generalized seizures, and most of them are drug-refractory epilepsies. The results of VEEG monitoring tend to be characterized by abnormal background activity, increased slow-wave activity, and the site of epileptogenic wave-like discharges tends to be in line with the site of cerebral softening foci or the site of the atrophic side of the brain as shown by cranial MRI.
ObjectiveTo explore the clinical features and EEG features of gelastic seizures, and analyze its value of lateral localization of epileptogenic area. MethodsAll patients with gelastic seizures admitted to the Sanbo Brain Hospital of Capital Medical University between January 2014 and December 2023 were reviewed and analyzed for history, symptomatology, imaging, electroencephalographic features and surgical protocols in patients who met the inclusion criteria and were followed up for at least 1 year, and surgical efficacy was assessed by using the Engel grading. ResultsA total of 51 patients with gelastic seizures were included, there were 32 (62.75%) males and 19 (37.25%) females, 21 (41.18%) with hypothalamic hamartomas (HH) and 30 (58.82%) with non-hypothalamic hamartomas. The age of onset was earlier in the HH group than in the non-HH group, with a median age of onset of 24.00 (0.00 ~ 96.00) and 78.00 (1.00 ~ 396.00) months (P<0.001). There are three types of laughter according to their characteristics: smiling or pleasant expressions, laughing out loud, crying or bitter laughter, with smiling or pleasant expressions being the most common (49.02%). Simple laughter is rare in all patients and is often accompanied by other manifestations such as autonomic symptoms, automatic movements, complex movements, and tonic seizures. Most of the HH group started with laughter whereas in the non-HH group laughter appeared mostly in the mid to late stages (P=0.007). Most of the HH group (57.14%) had preserved consciousness whereas most of the non-HH group (83.33%) had loss of consciousness (P=0.003). The interictal discharges in the HH group were mostly diffuse or multiregional, whereas those in the non-HH group were mostly regional (P=0.035). The onset of EEG during the seizure period in the HH group was mostly diffuse, whereas those in the non-HH group were mostly regional, mainly in the frontal and temporal regions, but there was no significant difference between the two groups (P=0.148). The non-HH group was mostly seen in those with definite lesions, and the most common type of lesion was FCD (focal cortical dysplasia, FCD). All patients enrolled in the group underwent surgical treatment, and stereoelectroencephalogram (SEEG) electrode implantation was performed in 13 cases in the HH group and in 17 cases in the non-HH group. 61.90% of the patients in the HH group had an Engel grade I, and 73.33% of the patients in the non-HH group had an Engel grade I. ConclusionsGelastic seizures has a complex neural network, with common causes other than hypothalamic hamartomas, and is most commonly seen in frontal or temporal lobe epilepsy, as well as in the insula or parietal lobe, with the most common type of lesion being FCD. The symptomatology, stage of onset, and electroencephalographic features of gelastic seizures can help in the differential diagnosis, and SEEG can help define the origin of the seizure and its diffusion pathway. The overall prognosis of surgical treatment was better in both the hypothalamic hamartomas and non-hypothalamic hamartomas groups.
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.
In recent years, it has become a new direction in the field of neuroscience to explore the mode characteristics, functional significance and interaction mechanism of resting spontaneous electroencephalography (EEG) and task-evoked EEG. This paper introduced the basic characteristics of spontaneous EEG and task-evoked EEG, and summarized the core role of spontaneous EEG in shaping the adaptability of the nervous system. It focused on how the spontaneous EEG interacted with the task-evoked EEG in the process of task processing, and emphasized that the spontaneous EEG could significantly affect the performance of tasks such as perception, cognition and movement by regulating neural activities and predicting external stimuli. These studies provide an important theoretical basis for in-depth understanding of the principle and mechanism of brain information processing in resting and task states, and point out the direction for further exploring the complex relationship between them in the future.
Aiming at the problem that the feature extraction ability of forehead single-channel electroencephalography (EEG) signals is insufficient, which leads to decreased fatigue detection accuracy, a fatigue feature extraction and classification algorithm based on supervised contrastive learning is proposed. Firstly, the raw signals are filtered by empirical modal decomposition to improve the signal-to-noise ratio. Secondly, considering the limitation of the one-dimensional signal in information expression, overlapping sampling is used to transform the signal into a two-dimensional structure, and simultaneously express the short-term and long-term changes of the signal. The feature extraction network is constructed by depthwise separable convolution to accelerate model operation. Finally, the model is globally optimized by combining the supervised contrastive loss and the mean square error loss. Experiments show that the average accuracy of the algorithm for classifying three fatigue states can reach 75.80%, which is greatly improved compared with other advanced algorithms, and the accuracy and feasibility of fatigue detection by single-channel EEG signals are significantly improved. The results provide strong support for the application of single-channel EEG signals, and also provide a new idea for fatigue detection research.
ObjectiveTo explore the advantages and disadvantages of using two intracranial EEG (iEEG) monitoring methods—Subdural ectrodes electroencephalography (SDEG)and Stereoelectroencephalography (SEEG), in patients with “difficult to locate” Intractable Epilepsy. MethodsRetrospectively analyzed the data of 60 patients with SDEG monitoring (49 cases) and SEEG monitoring (11 cases) from January 2010 to December 2018 in the Department of Neurosurgery of the First Affiliated Hospital of Fujian Medical. Observe and statistically compare the differences in the evaluation results of epileptic zones, surgical efficacy and related complications of the two groups of patients, and review the relevant literature. ResultsThe results showed that the two groups of SDEG and SEEG had no significant difference in the positive rate and surgical resection rate of epileptogenic zones, but the bilateral implantation rate of SEEG (5/11, 45.5%) was higher than that of SDEG (18/49, 36.7%). At present, there was no significant difference in the postoperative outcome among patients with epileptic zones resected after SDEG and SEEG monitoring (P>0.05). However, due to the limitation of the number of SEEG cases, it is not yet possible to conclude that the two effects were the same. There was a statistically significant difference in the total incidence of serious complications of bleeding or infection between the two groups (SDEG 20 cases vs. SEEG 1 case, P<0.05). There was a statistically significant difference in the total incidence of significant headache or cerebral edema between the two groups (SDEG 26 cases vs. SEEG 2 cases, P<0.05). There was a statistically significant difference in the incidence of cerebrospinal fluid leakage, subcutaneous fluid incision, and poor healing of incision after epileptic resection (SDEG 14 cases vs. SEEG 0 case, P<0.05); there were no significant differences in dysfunction of speech, muscle strength between the two groups (P>0.05). ConclusionSEEG has fewer complications than SDEG, SEEG is safer than SDEG. The two kinds of iEEG monitoring methods have advantages in the localization of epileptogenic zones and the differentiation of functional areas. The effective combination of the two methods in the future may be more conducive to the location of epileptic zones and functional areas.
PurposeTo analyze the effect of medication withdraw (MW) on long-term electroencephalogram (EEG) monitoring in children who need preoperative assessment for refractory epilepsy.MethodsRetrospective analysis was performed on the data of preoperative long-term EEG monitoring of children with refractory epilepsy who needed preoperative evaluation in the Pediatric Epilepsy Center of Peking University First Hospital from August 2018 to December 2019. Monitoring duration: at least three habitual seizures were detected, or the monitoring duration were as long as 10 days. MW protocol was according to the established plan.ResultsA total of 576 children (median age 4.4 years) required presurgical ictal EEGs, and 75 (75/576, 13.0%) needed MW for ictal EEGs. Among the 75 cases, 38 were male and 37 were female. The age range was from 15 months to 17 years (median age: 7.0 years). EEG and clinical data of with 65 children who strictly obey the MW protocol were analyzed. The total monitoring duration range was from 44.1 h (about 2 days) to 241.8 h (about 10 days)(median: 118.9 h (about 5 days)). Interictal EEG features before MW were including focal interictal epileptiform discharge (IED) in 39 cases (39/65, 60%), focal and generalized IED in 2 cases (2/65, 3.1%), multifocal IED in 20 cases (20/65, 30.7%), multifocal and generalized IED in 2 cases (2/65, 3.1%), and no IED in 2 cases (2/65, 3.1%). After MW, 18 cases (18/65, 27.7%) had no change in IED and the other 47 cases had changes of IED after MW. And IEDs in 46 cases (46/65, 70.8%) were aggravated, and IED was decreased in 1 case. The pattern of aggravated IED was original IED increasement, in 41 cases (41/46, 89.1%), and 5 cases (5 /46, 10.9%) had generalized IED which was not detected before MW. Of the 46 patients with IED exacerbations, 87.3% appeared within 3 days after MW. Habitual seizures were detected in 56 cases (86.2%, 56/65) after MW, and within 3 days of MW in 80.4% cases. Eight patients (14.3%) had secondary bilateral-tonic seizure (BTCS), of which only 1 patient had no BTCS in his habitual seizures. In 56 cases, 94.6% (53/56) had seizures after MW of two kinds of AEDs.Conclusions① In this group, thirteen percent children with intractable epilepsy needed MW to obtain ictal EEG; ② Most of them (86.2%) could obtain ictal EEG by MW. The IED and ictal EEG after MW were still helpful for localization of epileptogenic zone; ③ Most of the patients can obtain ictal EEG within 3 days after MW or after MW of two kinds of AEDs;4. The new secondary generalization was extremely rare.