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.
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.
Repetitive transcranial magnetic stimulation (rTMS) can influence the stimulated brain regions and other distal brain regions connecting to them. The purpose of the study is to investigate the effects of low-frequency rTMS over primary motor cortex on brain by analyzing the brain functional connectivity and coordination between brain regions. 10 healthy subjects were recruited. 1 Hz rTMS was used to stimulate primary motor cortex for 20 min. 1 min resting state electroencephalography (EEG) was collected before and after the stimulation respectively. By performing phase synchronization analysis between the EEG electrodes, the brain functional network and its properties were calculated. Signed-rank test was used for statistical analysis. The result demonstrated that the global phase synchronization in alpha frequency band was decreased significantly after low-frequency rTMS (P<0.05). The phase synchronization was down-regulated between motor cortex and ipsilateral frontal/parietal cortex, and also between contralateral parietal cortex and bilateral frontal cortex. The mean degree and global efficiency of brain functional networks in alpha frequency band were significantly decreased (P<0.05), and the mean shortest path length were significantly increased (P<0.05), which suggested the information transmission of the brain networks and its efficiency was reduced after low-frequency rTMS. This study verified the inhibition function of the low-frequency rTMS to brain activities, and demonstrated that low-frequency rTMS stimulation could affect both stimulating brain regions and distal brain regions connected to them. The findings in this study could be of guidance to clinical application of low-frequency rTMS.
Uncovering the alterations of neural interactions within the brain during epilepsy is important for the clinical diagnosis and treatment. Previous studies have shown that the phase-amplitude coupling (PAC) can be used as a potential biomarker for locating epileptic zones and characterizing the transition of epileptic phases. However, in contrast to the θ-γ coupling widely investigated in epilepsy, few studies have paid attention to the β-γ coupling, as well as its potential applications. In the current study, we use the modulation index (MI) to calculate the scalp electroencephalography (EEG)-based β-γ coupling and investigate the corresponding changes during different epileptic phases. The results show that the β-γ coupling of each brain region changes with the evolution of epilepsy, and in several brain regions, the β-γ coupling decreases during the ictal period but increases in the post-ictal period, where the differences are statistically significant. Moreover, the alterations of β-γ coupling between different brain regions can also be observed, and the strength of β-γ coupling increases in the post-ictal period, where the differences are also significant. Taken together, these findings not only contribute to understanding neural interactions within the brain during the evolution of epilepsy, but also provide a new insight into the clinical treatment.
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.
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 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 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.
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.
