ObjectiveTo explore the effect of family-school-hospital application in continuous nursing care for children with epilepsy. Methods120 children with epilepsy admitted to Children's Hospital Affiliated to Jiangnan University from January 2021 to October 2022 were randomly divided into two groups, each with 60 cases. The control group received routine care, while the experimental group received family-school-hospital continuous care. Compare the awareness of epilepsy knowledge, disease control effectiveness, medication compliance, negative emotions, physical and mental status, and quality of life before and after nursing between the families of two groups of children with epilepsy. ResultsAfter 2 months of nursing care, the scores of family members' knowledge of epilepsy in the experimental group were higher than the control group (P<0.05). The effect of disease control in the experimental group was better the control group (P<0.05). The drug compliance of the experimental group was higher than the control group (P<0.05). The quality of life score in the intervention group was higher than the control group (P<0.05). ConclusionThe application of family-school-hospital in the continuous care of children with epilepsy can improve their family members' awareness of epilepsy knowledge, effectively control the disease, improve medication compliance, improve negative emotions and physical and mental conditions, and thus improve the quality of life of children.
Objective To investigate the status quo and influencing factors of depression and anxiety in postoperative patients with thoracic neoplasms. Methods The general information questionnaire and Huaxi emotional-distress index scale (HEI) were adopted to survey 70 patients after surgery of thoracic neoplasms at the thoracic nursing outpatients from September to November 2016. There were 43 males and 27 females with age of 18-78 (56.20±11.34) years. Results The prevalence rate of depression and anxiety among postoperative patients with thoracic neoplasms was 50.0%, and moderate to severe negative emotions predominated. There was significant difference in educational levels, postoperative hospitalization and postoperative complications (P<0.05), while no significant difference in age, gender, disease types, complicated diseases, surgical procedures, pathological stages and hospitalization expenditures between patients with unhealthy emotions and normal emotions (P>0.05). Conclusion There is a high prevalence rate of negative emotion among postoperative patients with thoracic neoplasms. Educational levels, postoperative hospitalization and postoperative complications are important factors for negative emotion.
The result of the emotional state induced by music may provide theoretical support and help for assisted music therapy. The key to assessing the state of emotion is feature extraction of the emotional electroencephalogram (EEG). In this paper, we study the performance optimization of the feature extraction algorithm. A public multimodal database for emotion analysis using physiological signals (DEAP) proposed by Koelstra et al. was applied. Eight kinds of positive and negative emotions were extracted from the dataset, representing the data of fourteen channels from the different regions of brain. Based on wavelet transform, δ, θ, α and β rhythms were extracted. This paper analyzed and compared the performances of three kinds of EEG features for emotion classification, namely wavelet features (wavelet coefficients energy and wavelet entropy), approximate entropy and Hurst exponent. On this basis, an EEG feature fusion algorithm based on principal component analysis (PCA) was proposed. The principal component with a cumulative contribution rate more than 85% was retained, and the parameters which greatly varied in characteristic root were selected. The support vector machine was used to assess the state of emotion. The results showed that the average accuracy rates of emotional classification with wavelet features, approximate entropy and Hurst exponent were respectively 73.15%, 50.00% and 45.54%. By combining these three methods, the features fused with PCA possessed an accuracy of about 85%. The obtained classification accuracy by using the proposed fusion algorithm based on PCA was improved at least 12% than that by using single feature, providing assistance for emotional EEG feature extraction and music therapy.
Emotion recognition will be prosperious in multifarious applications, like distance education, healthcare, and human-computer interactions, etc. Emotions can be recognized from the behavior signals such as speech, facial expressions, gestures or the physiological signals such as electroencephalogram and electrocardiogram. Contrast to other methods, the physiological signals based emotion recognition can achieve more objective and effective results because it is almost impossible to be disguised. This paper introduces recent advancements in emotion research using physiological signals, specified to its emotion model, elicitation stimuli, feature extraction and classification methods. Finally the paper also discusses some research challenges and future developments.
Cognitive reappraisal is an important strategy for emotion regulation. Studies show that even healthy people may not be able to implement this strategy successfully, but the underlying neural mechanism behind the behavioral observation of success or failure of reappraisal is unclear. In this paper, 28 healthy college students participated in an experiment of emotional regulation with the cognitive reappraisal strategy. They were asked to complete the cognitive psychological questionnaires before the experiment. Their behavioral scores and scalp electroencephalogram (EEG) signals were collected simultaneously during the experiment. We divided all the subjects into two groups, according to the statistical test of valence scores. Then we analyzed their questionnaires, early event-related potential (ERP) components N200, P200, and late positive potential (LPP), and calculated the correlation between the valence score and the amplitude of LPP. The results showed that, in both groups, compared with negative-watching, the reappraisal induced larger N200 and P200 components and there were two modulation patterns (“increase” and “decrease”) of the reappraisal effect on the amplitude of early LPP (300?1 000 ms after stimulus onset). Moreover, correlation analysis showed that significant positive correlation between two differences in the successful group, i.e., the greater difference in the valence scoresin between reappraisal and negative-watching, the greater difference in the amplitude of early LPP between reappraisal and negative-watching; but no such effect was found in the failure group. These results indicated that, whether reappraisal was successful or not, no significant effect on early ERP components was found; and there were different patterns of the reappraisal effect on early LPP. The difference between successful and failure groups was mainly reflected in early LPP, that is, the EEG characteristics and behavioral scores of successful group were significantly positively correlated. Furthermore, the small sample analysis showed that this correlation only existed in the pattern of "increase". In the future, more research of this modulation mode is necessary in order to find more stable EEG characteristics under successful cognitive reappraisal in emotion regulation.
Objective To evaluate the changes in depressive symptoms and emotional responses in obstructive sleep apnea (OSA) patients after six months of continuous positive airway pressure (CPAP) therapy. Methods From June 2021 to December 2023, adult patients diagnosed with OSA at our hospital who were recommended for CPAP therapy as a first-line treatment were recruited. Demographic data (age, body mass index, gender), oxygen desaturation index, maximum duration of apnea and maximum duration of apnea were recorded. The patients were divided into a CPAP group and a non-CPAP group according to whether they were compliant to CPAP treatment. All patients completed questionnaires (including CES-D, DERS, ERS, and ESS) at 0, 1, 2, 4, and 6 months. Differences in general data and questionnaire results were compared between the two groups. Results The patients in the CPAP group showed significantly lower levels of depression and daytime sleepiness at 1, 2, 4, and 6 months compared with the non-CPAP group, with statistically significant differences (all P<0.05). Additionally, the CPAP group exhibited significantly lower scores in emotional responses and difficulties in emotion regulation across the same time points, with statistically significant differences (all P<0.05). In the non-CPAP group, increases in the apnea hypopnea index (AHI) and worsening emotional responses were key factors contributing to the exacerbation of depressive symptoms in OSA patients, with statistically significant differences (P<0.05). Conclusions CPAP therapy significantly improves depressive symptoms, emotional responses, and emotional regulation in OSA patients. Increases in the AHI and worsening emotional responses are primary factors leading to the worsening of depressive symptoms in OSA patients.
Objective To analyze and explore positive emotional experiences of patients, in order to provide reference for improving the medical services. Methods Using NVivo software, praise letters from a tertiary hospital in Guangdong in 2020 and 2021 were used as the research object for three-level coding. The positive emotional experiences of patients were explored through tools such as analytic hierarchy process and node item map. Results A total of 8601 patient praise letters were received, and after screening, a total of 8128 valid texts were obtained. In 2020, there were 2570 patient praise letters, including 69 from the emergency department, 638 from the outpatient department, and 1863 from the inpatient department. In 2021, there were 5558 patient praise letters, including 203 from the emergency department, 2071 from the outpatient department, and 3284 from the inpatient department. The most praise letters were from the inpatient department, with a total of 5147 letters (63.3%). There were 2709 letters (33.3%) from outpatient department, and 272 letters (3.3%) from emergency department. The classification of patient praise letters showed that patient praise for the process and individuals were most common (77.4%). After step-by-step encoding, the valid text formed 36 third level nodes, 8 second level nodes, and 3 first level nodes. Patient praise letters mainly focused on emotional evaluation at the first level node, followed by emotional expression and emotional response. Word frequency analysis showed that in terms of positive emotional experiences, the word “thank you” had the highest frequency among patients. In terms of patient perception of service, the term “patience” had the highest frequency. Conclusions When patients express praise for medical services, they pay more attention to the personal performance of medical staff and the experience of the service process compared to the final result. In the process of hospital management, the emotions of patients should be fully considered.
Fear emotion is a typical negative emotion that is commonly present in daily life and significantly influences human behavior. A deeper understanding of the mechanisms underlying negative emotions contributes to the improvement of diagnosing and treating disorders related to negative emotions. However, the neural mechanisms of the brain when faced with fearful emotional stimuli remain unclear. To this end, this study further combined electroencephalogram (EEG) source analysis and cortical brain network construction based on early posterior negativity (EPN) analysis to explore the differences in brain information processing mechanisms under fearful and neutral emotional picture stimuli from a spatiotemporal perspective. The results revealed that neutral emotional stimuli could elicit higher EPN amplitudes compared to fearful stimuli. Further source analysis of EEG data containing EPN components revealed significant differences in brain cortical activation areas between fearful and neutral emotional stimuli. Subsequently, more functional connections were observed in the brain network in the alpha frequency band for fearful emotions compared to neutral emotions. By quantifying brain network properties, we found that the average node degree and average clustering coefficient under fearful emotional stimuli were significantly larger compared to neutral emotions. These results indicate that combining EPN analysis with EEG source component and brain network analysis helps to explore brain functional modulation in the processing of fearful emotions with higher spatiotemporal resolution, providing a new perspective on the neural mechanisms of negative emotions.
Analyzing the influence of mixed emotional factors on false memory through brain function network is helpful to further explore the nature of brain memory. In this study, Deese-Roediger-Mc-Dermott (DRM) paradigm electroencephalogram (EEG) experiment was designed with mixed emotional memory materials, and different kinds of music were used to induce positive, calm and negative emotions of three groups of subjects. For the obtained false memory EEG signals, standardized low resolution brain electromagnetic tomography algorithm (sLORETA) was applied in the source localization, and then the functional network of cerebral cortex was built and analyzed. The results show that the positive group has the most false memories [(83.3 ± 6.8)%], the prefrontal lobe and left temporal lobe are activated, and the degree of activation and the density of brain network are significantly larger than those of the calm group and the negative group. In the calm group, the posterior prefrontal lobe and temporal lobe are activated, and the collectivization degree and the information transmission rate of brain network are larger than those of the positive and negative groups. The negative group has the least false memories [(73.3 ± 2.2)%], and the prefrontal lobe and right temporal lobe are activated. The brain network is the sparsest in the negative group, the degree of centralization is significantly larger than that of the calm group, but the collectivization degree and the information transmission rate of brain network are smaller than the positive group. The results show that the brain is stimulated by positive emotions, so more brain resources are used to memorize and associate words, which increases false memory. The activity of the brain is inhibited by negative emotions, which hinders the brain’s memory and association of words and reduces false memory.
In order to monitor the emotional state changes of audience on real-time and to adjust the music playlist, we proposed an algorithm framework of an electroencephalogram (EEG) driven personalized affective music recommendation system based on the portable dry electrode shown in this paper. We also further finished a preliminary implementation on the Android platform. We used a two-dimensional emotional model of arousal and valence as the reference, and mapped the EEG data and the corresponding seed songs to the emotional coordinate quadrant in order to establish the matching relationship. Then, Mel frequency cepstrum coefficients were applied to evaluate the similarity between the seed songs and the songs in music library. In the end, during the music playing state, we used the EEG data to identify the audience’s emotional state, and played and adjusted the corresponding song playlist based on the established matching relationship.