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.
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.
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.
ObjectiveTo preliminarily explore the damage effect of stereo electroencephalogram-guided radiofrequency thermocoagulation after increasing the number of electrodes in the epileptic foci.MethodsEight cases were included from 42 patients requiring SEEG from the Department of Neurosurgery of the Second Hospital of Lanzhou University during June 2017 to Jan. 2019, of which 6 cases were hypothetical epileptogenic foci located in the functional area or deep in the epileptogenic foci that could not be surgically removed, 2 patients who were unwilling to undergo craniotomy; added hypothetical epileptic foci Electrodes, the number of implanted electrodes exceeds the number of electrodes needed to locate the epileptic foci. After radiofrequency thermocoagulation damages the epileptogenic foci, the therapeutic effect is analyzed.ResultsIn 8 patients, the number of implanted electrodes increased from 1 ~ 6, with an average of (4±2.2), and the number of thermosetting points increased by 2 ~ 10, with an average of (7±3.1); follow-up (9±3.2) months, Epilepsy control status: 3 cases of Engel Ⅰ, 3 cases of Engel Ⅱ, 2 cases of Engel Ⅲ; 8 cases of epileptic seizure frequency decreased≥50%. There was a statistically significant difference in the frequency of attacks before and after thermocoagulation (P<0.05).ConclusionsIncreasing the lesion volume of the epileptic foci can obviously improve the efficacy of epilepsy. SEEG-guided radiofrequency thermocoagulation is an effective supplementary method for classical resection.