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.
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.
ObjectiveTo explore the application effect of standardized management on video-electroencephalogram (VEEG) monitoring.MethodsIn January 2018, a multidisciplinary standardized management team composed with doctors, technicians, and nurses was established. The standardized management plan for VEEG monitoring from outpatient, pre-hospital appointment, hospitalization and post-discharge follow-up was developed; the special quilt for epilepsy patients was designed and customized, braided for the patient instead of shaving head, standardized the work flow of the staff, standardized the health education of the patients and their families, and standardized the quality control of the implementation process. The standardized managemen effect carried out from January to December 2018 (after standardized managemen) was compared with the management effect from January to December 2017 (before standardized managemen).ResultsAfter standardized management, the average waiting time of patients decreased from (2.08±1.13) hours to (0.53±0.21) hours, and the average hospitalization days decreased from (6.63±2.54) days to (6.14±2.17) days. The pass rate of patient preparation increased from 63.14% to 90.09%. The capture rate of seizure onset increased from 73.37% to 97.08%. The accuracy of the record increased from 33.12% to 94.10%, the doctor’s satisfaction increased from 76.34±29.53 to 97.99±9.27, and the patient’s satisfaction increased from 90.04±18.97 to 99.03±6.51. The difference was statistically significant (P<0.05).ConclusionStandardization management is conducive to ensuring the homogeneity of clinical medical care, reducing the average waiting time and the average hospitalization days, improving the capture rate and accuracy of seizures, ensuring the quality of medical care and improving patient’s satisfaction.
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.
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.
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.
The causes of mental disorders are complex, and early recognition and early intervention are recognized as effective way to avoid irreversible brain damage over time. The existing computer-aided recognition methods mostly focus on multimodal data fusion, ignoring the asynchronous acquisition problem of multimodal data. For this reason, this paper proposes a framework of mental disorder recognition based on visibility graph (VG) to solve the problem of asynchronous data acquisition. First, time series electroencephalograms (EEG) data are mapped to spatial visibility graph. Then, an improved auto regressive model is used to accurately calculate the temporal EEG data features, and reasonably select the spatial metric features by analyzing the spatiotemporal mapping relationship. Finally, on the basis of spatiotemporal information complementarity, different contribution coefficients are assigned to each spatiotemporal feature and to explore the maximum potential of feature so as to make decisions. The results of controlled experiments show that the method in this paper can effectively improve the recognition accuracy of mental disorders. Taking Alzheimer's disease and depression as examples, the highest recognition rates are 93.73% and 90.35%, respectively. In summary, the results of this paper provide an effective computer-aided tool for rapid clinical diagnosis of mental disorders.
Purpose To analyze the clinical characteristicsand prognostic factors of Status epilepticus (SE) in children. Methods The clinical data of 33 children with SE treated in Jinan Central Hospital Affiliated of Shandong University from January 2014 to June 2021 were collected, and their clinical characteristics were analyzed. Then, according to Glasgow prognosis scale, the children were divided into good prognosis group (n=20) and poor prognosis group (n=13). The age of first attack, duration of attack, type of attack and SE classification, EEG, cranial imaging and etiology were used to analyze the influencing factors of SE prognosis. Results 75.7% were 0 ~ 6 years old in the age of first attack, and 29 cases of convulsive status epilepticus accounted for 87.9% in the classification of seizure types. There were significant differences in age of first attack, duration of attack, EEG, history of mental retardation and etiology between the two groups (P<0.05); Logistic regression analysis showed that the age of first attack, duration of attack, history of mental retardation and EEG were independent factors affecting the prognosis. Conclusion Low age, especially ≤ 6 years old, is the high incidence of SE in children at first attack. Most children are symptomatic and have obvious incentives. Convulsive SE is the main type of SE in children. The age of first onset, duration of epilepsy, history of mental retardation, and EEG can affect the prognosis of SE.