ABSTRACT
EEG-based emotion recognition through artificial intelligence is one of the major areas of biomedical and machine learning, which plays a key role in understanding brain activity and developing decision-making systems. However, the traditional EEG-based emotion recognition is a single feature input mode, which cannot obtain multiple feature information, and cannot meet the requirements of intelligent and high real-time brain computer interface. And because the EEG signal is nonlinear, the traditional methods of time domain or frequency domain are not suitable. In this paper, a CNN-DSC-Bi-LSTM-Attention (CDBA) model based on EEG signals for automatic emotion recognition is presented, which contains three feature-extracted channels. The normalized EEG signals are used as an input, the feature of which is extracted by multi-branching and then concatenated, and each channel feature weight is assigned through the attention mechanism layer. Finally, Softmax was used to classify EEG signals. To evaluate the performance of the proposed CDBA model, experiments were performed on SEED and DREAMER datasets, separately. The validation experimental results show that the proposed CDBA model is effective in classifying EEG emotions. For triple-category (positive, neutral and negative) and four-category (happiness, sadness, fear and neutrality), the classification accuracies were respectively 99.44% and 99.99% on SEED datasets. For five classification (Valence 1-Valence 5) on DREAMER datasets, the accuracy is 84.49%. To further verify and evaluate the model accuracy and credibility, the multi-classification experiments based on ten-fold cross-validation were conducted, the elevation indexes of which are all higher than other models. The results show that the multi-branch feature fusion deep learning model based on attention mechanism has strong fitting and generalization ability and can solve nonlinear modeling problems, so it is an effective emotion recognition method. Therefore, it is helpful to the diagnosis and treatment of nervous system diseases, and it is expected to be applied to emotion-based brain computer interface systems.
ABSTRACT
PURPOSE: Discuss the application effect of the six-step standard communication process in the communication ability training of newly recruited nurses. METHODS: This is a before and after control study. The control group included 45 newly recruited nurses in our hospital in 2019, and the observation group included 40 newly recruited nurses in our hospital in 2020. The control group completed the training according to the existing communication training program, and the observation group implemented a training program based on the "six-step standard communication process" on the basis of the existing communication training. The training period was 12 months. The training effect of the two groups of new nurses was compared. RESULTS: After training, the total scores of clinical communication skills of the new nurses in the control group and observation group were 252.56 ± 24.950 and 268.05 ± 19.335 points, respectively; the total scores of communication behavior were 39.00 ± 4.676 and 48.08 ± 2.515 points, respectively; the total scores of general self-efficacy were 26.89 ± 3.017 and 31.25 ± 5.027 points, respectively; the satisfaction scores of communication training were 17.56 ± 2.018 and 19.45 ± 0.986 points, respectively, and the differences were statistically significant (P < 0.05). CONCLUSION: The implementation of a training program based on the "six-step standard communication process" can effectively improve the clinical communication skills and self-efficacy of newly recruited nurses, and can be promoted and applied to the communication training of newly recruited nurses.
ABSTRACT
Objective: To construct a training content system for new nurses in cancer hospitals based on postcompetency and to provide guidance for clinical new nurse training. Methods: Based on literature review, semistructured interviews, and questionnaire surveys, a new draft of the nurse training content system was initially established, and 17 experts were selected to make two rounds of inquiry on the system by the Delphi method, so as to construct a new nurse training content system. Results: The effective rate of recovery of the two rounds of expert correspondence was 100%, the cooperation among experts was high, and the authoritative coefficient of experts was 0.89. The content system of new nurse training constructed included 2 first-class indexes, 5 second-class indexes, and 45 third-class indexes. Conclusion: The new nurse training content system is closely combined with clinical work, pays attention to improving nurses' competence, reflects the characteristics of nursing work in cancer hospitals, has a certain scientific and practical significance, and can provide guidance for the training of new nurses in cancer hospitals.
