Epileptic seizures and the interictal epileptiform discharges both have similar waveforms. And a method to effectively extract features that can be used to distinguish seizures is of crucial importance both in theory and clinical practice. We constructed state transfer networks by using visibility graphlet at multiple sampling intervals and analyzed network features. We found that the characteristics waveforms in ictal periods were more robust with various sampling intervals, and those feature network structures did not change easily in the range of the smaller sampling intervals. Inversely, the feature network structures of interictal epileptiform discharges were stable in range of relatively larger sampling intervals. Furthermore, the feature nodes in networks during ictal periods showed long-term correlation along the process, and played an important role in regulating system behavior. For stereo-electroencephalography at around 500 Hz, the greatest difference between ictal and the interictal epileptiform occurred at the sampling interval around 0.032 s. In conclusion, this study effectively reveals the correlation between the features of pathological changes in brain system and the multiple sampling intervals, which holds potential application value in clinical diagnosis for identifying, classifying, and predicting epilepsy.
Background music has been increasingly affecting people’s lives. The research on the influence of background music on working memory has become a hot topic in brain science. In this paper, an improved electroencephalography (EEG) experiment based on n-back paradigm was designed. Fifteen university students without musical training were randomly selected to participate in the experiment, and their behavioral data and the EEG data were collected synchronously in order to explore the influence of different types of background music on spatial positioning cognition working memory. The exact low-resolution brain tomography algorithm (eLORETA) was applied to localize the EEG sources and the cross-correlation method was used to construct the cortical brain function networks based on the EEG source signals. Then the characteristics of the networks under different conditions were analyzed and compared to study the effects of background music on people’s working memory. The results showed that the difference of peak periods after stimulated by different types of background music were mainly distributed in the signals of occipital lobe and temporal lobe (P < 0.05). The analysis results showed that the brain connectivity under the condition with background music were stronger than those under the condition without music. The connectivities in the right occipital and temporal lobes under the condition of rock music were significantly higher than those under the condition of classical music. The node degrees, the betweenness centrality and the clustering coefficients under the condition without music were lower than those under the condition with background music. The node degrees and clustering coefficients under the condition of classical music were lower than those under the condition of rock music. It indicates that music stimulation increases the brain activity and has an impact on the working memory, and the effect of rock music is more remarkable than that of classical music. The behavioral data showed that the response accuracy in the state of no music, classical music and rock music were 86.09% ± 0.090%, 80.96% ± 0.960% and 79.36% ± 0.360%, respectively. We conclude that background music has a negative impact on the working memory, for it takes up the cognitive resources and reduces the cognitive ability of spatial location.
Epilepsy is one of the most common neurological disorders, and surgical intervention is usually used for drug-resistant focal epilepsy. Cortical electrical stimulation is widely used in preoperative evaluation of epilepsy to explore the anatomical-clinical electrical correlations between epileptogenic and functional networks through electrical stimulation, and the functional brain maps produced by cortical electrical stimulation depict areas of the functional cortex at an individual level, identifying the functional cortex with greater precision, as well as helping to establish epilepsy network, enabling more precise localization of seizure zones and providing a more accurate localization for surgical resection. Electrical cortical stimulation has become a standard technique for the preoperative assessment of brain region function in brain surgery. It is an indispensable part of preoperative evaluation.The main types of functional mapping by electrical stimulation include stereoelectroencephalography (SEEG) and subdural electrode (SDE), SEEG-guided cortical electrical stimulation is gradually becoming more mainstream compared to subdural electrodes, and is increasingly valuable and important as a preoperative evaluation of epilepsy. It is increasingly demonstrating its value and importance because it avoids craniotomy, takes less time for surgery, has fewer associated complications and infections, and can explore deep lesions, increasing the understanding of human functional neuroanatomy and enabling more precise localization of seizure zones.This article reviews the history of the development of cortical electrical stimulation technology, the intrinsic mechanisms, the value of the application of SEEG, and also provides a comprehensive comparison between SEEG and SDE, despite the irreplaceable advantages of SEEG, attention should be paid to the unresolved clinical and scientific issues of SEEG, and the establishment of a consensus-based clinical guideline, as the application of this technology will be more widely used in both clinical and scientific work.
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.
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.
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.
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.
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.
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.
In this paper, a feature extraction algorithm of weighted multiple multiscale entropy is proposed to solve the problem of information loss which is caused in the multiscale process of traditional multiscale entropy. Algorithm constructs the multiple data sequences from large to small on each scale. Then, considering the different contribution degrees of multiple data sequences to the entropy of the scale, the proportion of each sequence in the scale sequence is calculated by combining the correlation between the data sequences, so as to reconstruct the sample entropy of each scale. Compared with the traditional multiscale entropy the feature extraction algorithm based on weighted multiple multiscale entropy not only overcomes the problem of information loss, but also fully considers the correlation of sequences and the contribution to total entropy. It reduces the fluctuation between scales, and digs out the details of electroencephalography (EEG). Based on this algorithm, the EEG characteristics of autism spectrum disorder (ASD) children are analyzed, and the classification accuracy of the algorithm is increased by 23.0%, 10.4% and 6.4% as compared with the EEG extraction algorithm of sample entropy, traditional multiscale entropy and multiple multiscale entropy based on the delay value method, respectively. Based on this algorithm, the 19 channel EEG signals of ASD children and healthy children were analyzed. The results showed that the entropy of healthy children was slightly higher than that of the ASD children except the FP2 channel, and the numerical differences of F3, F7, F8, C3 and P3 channels were statistically significant (P<0.05). By classifying the weighted multiple multiscale entropy of each brain region, we found that the accuracy of the anterior temporal lobe (F7, F8) was the highest. It indicated that the anterior temporal lobe can be used as a sensitive brain area for assessing the brain function of ASD children.
