west china medical publishers
Keyword
  • Title
  • Author
  • Keyword
  • Abstract
Advance search
Advance search

Search

find Keyword "脑电图" 169 results
  • An electroencephalogram-based study of resting-state spectrogram and attention in tinnitus patients

    The incidence of tinnitus is very high, which can affect the patient’s attention, emotion and sleep, and even cause serious psychological distress and suicidal tendency. Currently, there is no uniform and objective method for tinnitus detection and therapy, and the mechanism of tinnitus is still unclear. In this study, we first collected the resting state electroencephalogram (EEG) data of tinnitus patients and healthy subjects. Then the power spectrum topology diagrams were compared of in the band of δ (0.5–3 Hz), θ (4–7 Hz), α (8–13 Hz), β (14–30 Hz) and γ (31–50 Hz) to explore the central mechanism of tinnitus. A total of 16 tinnitus patients and 16 healthy subjects were recruited to participate in the experiment. The results of resting state EEG experiments found that the spectrum power value of tinnitus patients was higher than that of healthy subjects in all concerned frequency bands. The t-test results showed that the significant difference areas were mainly concentrated in the right temporal lobe of the θ and α band, and the temporal lobe, parietal lobe and forehead area of the β and γ band. In addition, we designed an attention-related task experiment to further study the relationship between tinnitus and attention. The results showed that the classification accuracy of tinnitus patients was significantly lower than that of healthy subjects, and the highest classification accuracies were 80.21% and 88.75%, respectively. The experimental results indicate that tinnitus may cause the decrease of patients’ attention.

    Release date:2021-08-16 04:59 Export PDF Favorites Scan
  • Effects of health education based on process communication mode on the success rate and active cooperation rate of video EEG monitoring in elderly patients

    ObjectiveVideo electroencephalography (VEEG) monitoring for health education of elderly patients based on a process-based communication model, and explore the impact of this model on the success rate, negative emotions, nursing satisfaction, and active cooperation rate of such patients.MethodsFrom September 2017 to September 2019, 118 patients with suspected epilepsy, encephalitis and other diseases who required VEEG monitoring in Suining Central Hospital were selected for this study (patients aged 61 to 73 years; 54 males and 64 females). Patients were divided into 2 groups using a random number table method, 59 patients in each group.A group received routine nursing, and B group received health education based on the process communication model. The monitoring success rate, negative emotion, active cooperation rate, and nursing satisfaction were compared between the two groups.ResultsThe total effective rate in the B group was 86.44%, which was significantly higher than 76.27% in the A group (P<0.05). After nursing intervention, the scores of anxiety and depression in the two groups were significantly decreased, but the decline was greater in the B group (P<0.05). The active cooperation rate and nursing satisfaction of the B group were significantly higher than those of the A group (P<0.05).ConclusionCompared with conventional nursing, health education based on process communication mode can significantly improve the success rate of VEEG monitoring in elderly patients, alleviate the negative emotions of patients, improve the active cooperation rate and nursing satisfaction.

    Release date:2020-05-19 01:07 Export PDF Favorites Scan
  • 新型冠状病毒感染伴发癫痫及其发病机制与脑电图改变

    新型冠状病毒感染(Corona virus disease 2019,COVID-19)是一种由冠状病毒(SARS-CoV-2)导致的新型传染性疾病。关于COVID-19与癫痫之间的关系,有研究认为癫痫发作和COVID-19无明显关系;但也有不少学者认为,癫痫发作是COVID-19的继发症状,甚至是早期症状。COVID-19患者中癫痫发作发生率为0.08%~1.9%。COVID-19出现癫痫发作的直接发病机制是,SARS-COV-2能够直接进入并感染中枢神经系统,引起脑膜炎和脑炎,从而引起癫痫发作。间接发病机制包括:中枢神经系统炎症(细胞因子风暴)、血-脑屏障的破坏、凝血异常、脑卒中、线粒体功能异常、电解质紊乱。新发作和频发癫痫发作的患者可能导致预后更差,死亡率更高。COVID-19伴发癫痫患者中脑电图(Electroencephalogram,EEG)改变的主要表现为:基本节律不同程度的慢化、节律性慢活动、癫痫样放电(包括周期性放电和散在性棘波、尖波等)。癫痫患者EEG的异常部位主要分布在额叶,然而,异常EEG表现并无特异性。

    Release date: Export PDF Favorites Scan
  • Study on classification and identification of depressed patients and healthy people among adolescents based on optimization of brain characteristics of network

    To enhance the accuracy of computer-aided diagnosis of adolescent depression based on electroencephalogram signals, this study collected signals of 32 female adolescents (16 depressed and 16 healthy, age: 16.3 ± 1.3) with eyes colsed for 4 min in a resting state. First, based on the phase synchronization between the signals, the phase-locked value (PLV) method was used to calculate brain functional connectivity in the θ and α frequency bands, respectively. Then based on the graph theory method, the network parameters, such as strength of the weighted network, average characteristic path length, and average clustering coefficient, were calculated separately (P < 0.05). Next, using the relationship between multiple thresholds and network parameters, the area under the curve (AUC) of each network parameter was extracted as new features (P < 0.05). Finally, support vector machine (SVM) was used to classify the two groups with the network parameters and their AUC as features. The study results show that with strength, average characteristic path length, and average clustering coefficient as features, the classification accuracy in the θ band is increased from 69% to 71%, 66% to 77%, and 50% to 68%, respectively. In the α band, the accuracy is increased from 72% to 79%, 69% to 82%, and 65% to 75%, respectively. And from overall view, when AUC of network parameters was used as a feature in the α band, the classification accuracy is improved compared to the network parameter feature. In the θ band, only the AUC of average clustering coefficient was applied to classification, and the accuracy is improved by 17.6%. The study proved that based on graph theory, the method of feature optimization of brain function network could provide some theoretical support for the computer-aided diagnosis of adolescent depression.

    Release date:2021-02-08 06:54 Export PDF Favorites Scan
  • 利用自动病变检测规划立体定向脑电图:可行性回顾性研究

    本回顾性横断面研究评估了将深度学习的难治性癫痫患儿的结构性磁共振成像(MRI)纳入到规划立体定向脑电图(SEEG)植入的可行性和潜在益处。本研究旨在评估自动病变检测与 SEEG 检测出癫痫发作起始区(SOZ)之间的共定位程度。将神经网络分类器应用于基于皮层 MRI 数据的三个队列:① 对 34 例局灶性皮质发育不良(FCD)患者的神经网络进行学习、训练和交叉验证;② 对 20 名健康儿童对照者进行特异性评估;③ 对 34 例患儿纳入 SEEG 植入计划的可行性进行了评价。SEEG 电极触点的坐标与分类器预测的病变进行核验。临床神经生理学家鉴定癫痫发作起源和易激惹区的 SEEG 电极触点位置。若 SOZ 坐标点和分类器预测的病变之间的距离<10 mm 则被认为是共定位的。影像学诊断病灶的分类敏感度为 74%(25/34)。对照组中未检测到异常(特异性=100%)。在 34 例 SEEG 植入患者中,21 例有局灶性皮层 SOZ,其中 8 例经病理证实为 FCD。分类器正确地检测了这 8 例 FCD 患者中的 7 例(86%)。组织病理学存在异质性的局灶性皮层病变患者中,62% 的患者分类器输出结果与 SOZ 之间存在共定位。3 例患者中,电临床提示为局灶性癫痫,SEEG 上无 SOZ 定位点,但在这些患者中,分类器识别了尚未植入的额外异常点。自动病变检测与 SEEG 之间的共定位存在高度的一致性。 我们已经建立了一个框架,将基于深度学习的 MRI 自动病变检测纳入到 SEEG 植入计划。我们的发现支持了对自动 MRI 分析的前瞻性评估,以规划最佳电极植入轨迹方案。

    Release date:2021-08-30 02:33 Export PDF Favorites Scan
  • Discussion on the distribution characteristics and preventive effects of EEG patterns in acute mountain sickness

    ObjectiveThe purpose of the research is to study the distribution and early warning of electroencephalogram (EEG) in acute mountain sickness (AMS). MethodsA total of 280 healthy young men were recruited from September 2016 to October 2016. The basic data were collected by the centralized flow method, the general situation of the division of the investigators after the training, the Lewis Lake score, the computer self-rating anxiety scale and depression scale, and the collection of EEG. Follow up in three months. Results94 of the patients with AMS, morbidity is 33%, 21 (22.34%) of the patients are moderate to severe, 73 (77.66%) are mild, morbidity is 26.67%. The abnormal detection rate of electrogram was 7.9% (22/280), which were mild EEG, normal EEG abnormal rate was 8.6% (16/186), abnormal detection rate of mild AMS was 4.1% (3/73), and the abnormal detection rate was 14.3% (3/21) in the medium / heavy AMS. The latter was significantly different from the previous (P < 0.05). Three months follow-up of this group of patients with 0 case of high altitude disease. Conclusions The EEG in AMS is mainly a rhythm irregular, unstable, poor amplitude modulation; or two hemisphere volatility difference of more than 50% or slightly increased activity. The result is statistically significant, suggesting that EEG distributions has possible early warning of AMS.

    Release date:2017-07-26 04:06 Export PDF Favorites Scan
  • EEG waveform and spectrum-power analysis under different settings of filter parameter

    Objective To explore the change of EEG waveform recorded by clinical EEG under different filtering parameters. Methods22 abnormal EEG samples of epilepsy patients with abundant abnormal waveforms recorded in Peking University first hospital were selected as the case group (abnormal group), and 30 normal EEG samples of healthy people with matched sex and age were selected as the control group (normal group). Visual examination and power spectrum analysis were then performed to compare the difference of wave forms and spectrum power under different settings of filter parameter between the two groups. ResultsThe results of visual examination show that, lower high-frequency filtering has an effect on the fast wave composition of EEG and may distort and reduce the spike wave. Higher low-frequency filtering has an effect on the overall background and slow wave activity of EEG and may change the amplitude morphology of some slow waves. The results of power spectrum analysis show that, Compare the difference between the EEG normal group and the abnormal group, the main difference under the settings of 0.5~70Hz was on the θ and α3 frequency band, different brain regions were slightly different. In the central region, the difference in the high frequency band (α3, γ1, γ2) decreases or disappears with the decrease of the high frequency filtering. In the rest of the brain, the difference in the δ band appears gradually with the increase of the low frequency filtering. Compare the difference between frontal area and occipital area under different filter set, for the normal group, under the settings of 0.5 ~ 70 Hz, the difference between two regions is mainly on the θ, γ1 and γ2 band. When high frequency filter reduces, the difference between two regions on high frequency band (γ1, γ2) are gradually reduced or disappeared. And when low frequency filter increases, the difference on δ band appears. For the abnormal group, the difference between frontal and occipital region under the settings of 0.5 ~ 70 Hz is mainly on γ1 and γ2 bands. When the high-frequency filter decreases, the difference between two regions on high-frequency bands are gradually decreased or disappeared. All the results can be corrected by FDR. ConclusionThe results show that the filter setting has a significant influence on EEG results. In clinical application, we should strictly set 0.5 ~ 70 Hz bandpass filtering as the standard.

    Release date:2022-04-28 09:14 Export PDF Favorites Scan
  • Study on the property of correlation dimension of sleep apnea syndrome electroencephalogram

    Sleep apnea syndrome (SAS) is a kind of common and harmful systemic sleep disorder. SAS patients have significant iconography changes in brain structure and function, and electroencephalogram (EEG) is the most intuitive parameter to describe the sleep process which can reflect the electrical activity and function of brain tissues. Based on the non-stationary and nonlinear characteristics of EEG, this paper analyzes the correlation dimension of sleep EEG in patients with SAS. Six SAS patients were classed as SAS group and six healthy persons were classified into a control group. The results showed that the correlation dimension of sleep EEG in the SAS group and the control group decreased gradually with the deepening of sleep, and then increased to the level of awake and light sleep stage with rapid eye movement (REM). The correlation dimension of SAS group was significantly lower than that of control group (P<0.01) throughout all the stages. The results suggested that there were significant nonlinear dynamic differences between the EEG signals of SAS patients and of healthy people, which provided a new direction for the study of the physiological mechanism and automatic detection of SAS.

    Release date:2017-04-13 10:03 Export PDF Favorites Scan
  • Using electroencephalogram for emotion recognition based on filter-bank long short-term memory networks

    Emotion plays an important role in people's cognition and communication. By analyzing electroencephalogram (EEG) signals to identify internal emotions and feedback emotional information in an active or passive way, affective brain-computer interactions can effectively promote human-computer interaction. This paper focuses on emotion recognition using EEG. We systematically evaluate the performance of state-of-the-art feature extraction and classification methods with a public-available dataset for emotion analysis using physiological signals (DEAP). The common random split method will lead to high correlation between training and testing samples. Thus, we use block-wise K fold cross validation. Moreover, we compare the accuracy of emotion recognition with different time window length. The experimental results indicate that 4 s time window is appropriate for sampling. Filter-bank long short-term memory networks (FBLSTM) using differential entropy features as input was proposed. The average accuracy of low and high in valance dimension, arousal dimension and combination of the four in valance-arousal plane is 78.8%, 78.4% and 70.3%, respectively. These results demonstrate the advantage of our emotion recognition model over the current studies in terms of classification accuracy. Our model might provide a novel method for emotion recognition in affective brain-computer interactions.

    Release date:2021-08-16 04:59 Export PDF Favorites Scan
  • Resting-state electroencephalogram classification of patients with schizophrenia or depression

    The clinical manifestations of patients with schizophrenia and patients with depression not only have a certain similarity, but also change with the patient's mood, and thus lead to misdiagnosis in clinical diagnosis. Electroencephalogram (EEG) analysis provides an important reference and objective basis for accurate differentiation and diagnosis between patients with schizophrenia and patients with depression. In order to solve the problem of misdiagnosis between patients with schizophrenia and patients with depression, and to improve the accuracy of the classification and diagnosis of these two diseases, in this study we extracted the resting-state EEG features from 100 patients with depression and 100 patients with schizophrenia, including information entropy, sample entropy and approximate entropy, statistical properties feature and relative power spectral density (rPSD) of each EEG rhythm (δ, θ, α, β). Then feature vectors were formed to classify these two types of patients using the support vector machine (SVM) and the naive Bayes (NB) classifier. Experimental results indicate that: ① The rPSD feature vector P performs the best in classification, achieving an average accuracy of 84.2% and a highest accuracy of 86.3%; ② The accuracy of SVM is obviously better than that of NB; ③ For the rPSD of each rhythm, the β rhythm performs the best with the highest accuracy of 76%; ④ Electrodes with large feature weight are mainly concentrated in the frontal lobe and parietal lobe. The results of this study indicate that the rPSD feature vector P in conjunction with SVM can effectively distinguish depression and schizophrenia, and can also play an auxiliary role in the relevant clinical diagnosis.

    Release date:2020-02-18 09:21 Export PDF Favorites Scan
17 pages Previous 1 2 3 ... 17 Next

Format

Content