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.
ObjectiveTo investigate the application of stereoelectroencephalography (SEEG) in the refractory epilepsy related to periventricular nodular heterotopia (PNH). MethodsTen patients with drug-resistant epilepsy related to PNHs from Guangdong Sanjiu Brain Hospital and the First Affiliated Hospital of Jinan University from April 2017 to February 2021 were studied. Electrodes were implanted based on non-invasive preoperative evaluation. Then long-term monitoring of SEEG was carried out. The patterns of epileptogenic zone (EZ) were divided into four categories based on the ictal SEEG: A. only the nodules started; B. nodules and cortex synchronous initiation; C. the cortex initiation with early spreading to nodules; D. only cortex initiation. All patients underwent SEEG-guided radiofrequency thermocoagulation (RFTC), with a follow-up of at least 12 months. ResultsAll cases were multiple nodules. Four cases were unilateral and six bilateral. Eight cases were distributed in posterior pattern, and one in anterior pattern and one in diffused pattern, respectively. Seven patients had only PNH (pure PNH) and three patients were associated with other overlying cortex malformations (PNH plus). The EZ patterns of all cases were confirmed by the ictal SEEG: six patients were in pure type A, two patients were in pure type B, one patient in type A+B and one in type A+B+C, respectively. In eight patients SEEG-guided RF-TC was targeted only to PNHs; and in two patients RFTC was directed to both heterotopias and related cortical regions. The mean follow up was (33.4±14.0) months (12 ~ 58 months). Eight patients (in pure type A or type A included) were seizure free. Two patients were effective. None of the patients had significant postoperative complications or sequelae. ConclusionThe epileptic network of Epilepsy associated with nodular heterotopia may be individualized. Not all nodules are always epileptogenic, the role of each nodule in the epileptic network may be different. And multiple epileptic patterns may occur simultaneously in the same patient. SEEG can provide individualized diagnosis and treatment, be helpful to prognosis.
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.
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.
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.
The aim of this study is to evaluate the effect of laparoscopic simulation training with different attention. Attention was appraised using the sample entropy and θ/β value, which were calculated according to electroencephalograph (EEG) signal collected with BrainLink. The effect of laparoscopic simulation training was evaluated using the completion time, error number and fixation number, which were calculated according to eye movement signal collected with Tobii eye tracker. Twenty volunteers were recruited in this study. Those with the sample entropy lower than 0.77 were classified into group A and those higher than 0.77 into group B. The results showed that the sample entropy of group A was lower than that of group B, and fluctuations of A were more steady. However, the sample entropy of group B showed steady fluctuations in the first five trainings, and then demonstrated relatively dramatic fluctuates in the later five trainings. Compared with that of group B, the θ/β value of group A was smaller and shows steady fluctuations. Group A has a shorter completion time, less errors and faster decrease of fixation number. Therefore, this study reached the following conclusion that the attention of the trainees would affect the training effect. Members in group A, who had a higher attention were more efficient and faster training. For those in group B, although their training skills have been improved, they needed a longer time to reach a plateau.
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.
Objective To research clinical manifestations, electrophysiological characteristics of epileptic seizures arising from diagonal sulci (DS), to improve the level of the diagnosis and treatment of frontal epilepsy. MethodsWe reviewed all the patients underwent a detailed presurgical evaluation, including 5 patients with seizures to be proved originating from diagonal sulci by Stereo-electroencephalography (SEEG). All the 5 patients with detailed medical history, head Magnetic resonance (MRI), the Positron emission computered tomography (PET-CT) and psychological evaluation, habitual seizures were recorded by Video-electroencephalography (VEEG) and SEEG, we review the intermittent VEEG and ictal VEEG, analyzing the symptoms of seizures. Results 5 patients were divided into 2 groups by SEEG, group 1 including 3 patients with seizures arising from the bottom of DS, group 2 including 2 patients with seizures arising from the surface of DS, all the tow groups with seizures characterized by both having tonic and complex motors, tonic seizures were prominent in seizures from left DS, and tonic seizures may absent in seizures from right DS. Intermittent discharges with group1 were diffused, and intermittent discharges with group 2 were focal, but both brain areas of frontal and temporal were infected. Ictal EEG findings were consistent with the characteristics of neocortical seizures, the onset EEG shows voltage attenuation, seizures from bottom of DS with diffused EEG onset, and seizures from surface of DS with more focal EEG onset, but both frontal and anterior temporal regions were involved. Conclusionthe symptom of seizures arising from DS characterized by tonic and complex motor, can be divided into seizures arising from the bottom of DS and seizures from the surface of DS, with different electrophysiological characters.
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.
For refractory epilepsy requiring surgical treatment in clinic, precise preoperative positioning of the epileptogenic zone is the key to improving the success rate of clinical surgical treatment. Although the use of electrical stimulation to locate epileptogenic zone has been widely carried out in many medical centers, the preoperative implantation evaluation of stereoelectroencephalography (SEEG) and the interpretation of electrical stimulation induced EEG activity are still not perfect and rigorous. Especially, there are still technological limitations and unknown areas regarding electrode implantation mode, stimulation parameters design, and surgical prognosis correlation. In this paper, the clinical background, application status, technical progress and development trend of SEEG-based stereo-electric stimulation-induced cerebral electrical activity in the evaluation of refractory epilepsy are reviewed, and applications of this technology in clinical epileptogenic zone localization and cerebral cortical function evaluation are emphatically discussed. Additionally, the safety during both of high-frequency and low-frequency electrical stimulations which are commonly used in clinical evaluation of refractory epilepsy are also discussed.