Simulation-based medical education is becoming increasingly common. In this paper, the status and goal of SBME development is analyzed after a brief introduction of SBME. Secondly, the essentiality and possibility of bringing SBME to a situated paradigm are clarified, because there are rich implications for situated cognition as the theory foundation of SBME. As a main discussion point, eight practical situated designing principles for SBME in theoretical and practical contexts are then expounded. Finally, a specific attitude toward the relationship between theory and practice for the SBME teachers is also elucidated.
Current studies on electroencephalogram (EEG) emotion recognition primarily concentrate on discrete stimulus paradigms under controlled laboratory settings, which cannot adequately represent the dynamic transition characteristics of emotional states during multi-context interactions. To address this issue, this paper proposes a novel method for emotion transition recognition that leverages a cross-modal feature fusion and global perception network (CFGPN). Firstly, an experimental paradigm encompassing six types of emotion transition scenarios was designed, and EEG and eye movement data were simultaneously collected from 20 participants, each annotated with dynamic continuous emotion labels. Subsequently, deep canonical correlation analysis integrated with a cross-modal attention mechanism was employed to fuse features from EEG and eye movement signals, resulting in multimodal feature vectors enriched with highly discriminative emotional information. These vectors are then input into a parallel hybrid architecture that combines convolutional neural networks (CNNs) and Transformers. The CNN is employed to capture local time-series features, whereas the Transformer leverages its robust global perception capabilities to effectively model long-range temporal dependencies, enabling accurate dynamic emotion transition recognition. The results demonstrate that the proposed method achieves the lowest mean square error in both valence and arousal recognition tasks on the dynamic emotion transition dataset and a classic multimodal emotion dataset. It exhibits superior recognition accuracy and stability when compared with five existing unimodal and six multimodal deep learning models. The approach enhances both adaptability and robustness in recognizing emotional state transitions in real-world scenarios, showing promising potential for applications in the field of biomedical engineering.
Objective To explore the effect of preoperative hypothyroidism on postoperative cognition dysfunction (POCD) in elderly patients after on-pump cardiac surgery. Methods Patients who were no younger than 50 years and scheduled to have on-pump cardiac surgeries were selected in West China Hospital from March 2016 to December 2017. Based on hormone levels, patients were divided into two groups: a hypo group (hypothyroidism group, thyroid stimulating hormone (TSH) >4.2 mU/L or free triiodothyronine 3 (FT3) <3.60 pmol/L or FT4 <12.0 pmol/L) and an eu group (euthyroidism group, normal TSH, FT3 and FT4). The mini-mental state examination (MMSE) test and a battery of neuropsychological tests were used by a fixed researcher to assess cognitive function on 1 day before operation and 7 days after operation. Primer outcome was the incidence of POCD. Secondary outcomes were the incidence of cognitive degradation, scores or time cost in every aspect of cognitive function. Results No matter cognitive function was assessed by MMSE or a battery of neuropsychological tests, the incidence of POCD in the hypo group was higher than that of the eu group. The statistical significance existed when using MMSE (55.56% vs. 26.67%, P=0.014) but was absent when using a battery of neuropsychological tests (55.56% vs. 44.44%, P=0.361). The incidence of cognitive deterioration in the hypo group was higher than that in the eu group in verbal fluency test (48.15% vs. 20.00%, P=0.012). The cognitive deterioration incidence between the hypo group and the eu group was not statistically different in the other aspects of cognitive function. There was no statistical difference about scores or time cost between the hypo group and the eu group in all the aspects of cognitive function before surgery. After surgery, the scores between the hypo group and the eu group was statistically different in verbal fluency test (26.26±6.55 vs. 30.23±8.00, P=0.023) while was not statistically significant in other aspects of cognitive function. Conclusion The incidence of POCD is high in the elderly patients complicated with hypothyroidism after on-pump cardiac surgery and words reserve, fluency, and classification of cognitive function are significantly impacted by hypothyroidism over than other domains, which indicates hypothyroidism may have close relationship with POCD in this kind of patients.
Speech feature learning is the core and key of speech recognition method for mental illness. Deep feature learning can automatically extract speech features, but it is limited by the problem of small samples. Traditional feature extraction (original features) can avoid the impact of small samples, but it relies heavily on experience and is poorly adaptive. To solve this problem, this paper proposes a deep embedded hybrid feature sparse stack autoencoder manifold ensemble algorithm. Firstly, based on the prior knowledge, the psychotic speech features are extracted, and the original features are constructed. Secondly, the original features are embedded in the sparse stack autoencoder (deep network), and the output of the hidden layer is filtered to enhance the complementarity between the deep features and the original features. Third, the L1 regularization feature selection mechanism is designed to compress the dimensions of the mixed feature set composed of deep features and original features. Finally, a weighted local preserving projection algorithm and an ensemble learning mechanism are designed, and a manifold projection classifier ensemble model is constructed, which further improves the classification stability of feature fusion under small samples. In addition, this paper designs a medium-to-large-scale psychotic speech collection program for the first time, collects and constructs a large-scale Chinese psychotic speech database for the verification of psychotic speech recognition algorithms. The experimental results show that the main innovation of the algorithm is effective, and the classification accuracy is better than other representative algorithms, and the maximum improvement is 3.3%. In conclusion, this paper proposes a new method of psychotic speech recognition based on embedded mixed sparse stack autoencoder and manifold ensemble, which effectively improves the recognition rate of psychotic speech.
OBJECTIVE: To investigate the role of direct and indirect recognition in pig-to-man xenotransplantation. METHODS: Taken the peripheral blood lymphocytes (PBLC) from three Neijiang pigs and two humans as stimulators and respondors, the one-way mixed lymphatic reactions (MLR) of xenograft were carried out, and allo- and self-PBLC as control. RESULTS: Among the three patterns of MLR, syngeneic was MLR the lowest in proliferation, the allogenic MLR was the highest, and the xenogenic MLR was medium. The PBLCs from humans and pigs were matched on HLA-A, B, DR and DQ by means of modified Terasaki assay. The match on pigs was failure because of the pre-existing natural xenogenic antibody in the testing serum. CONCLUSION: The results suggest that the degree of MHC matching still affect the rejection in xenotransplantation, but the present serum assay of MHC matching is not fit for pig.
Gait recognition is a new technology in biometric recognition and medical treatment which has advantages such as long-distance and non-invasiveness. Depending on the differences between different people's walking postures, we can recognize individuals by characteristics extracted from the images of walking movement. A complete gait recognition process usually includes gait sequence acquisition, gait detection, feature extracting and recognition. In this paper, the commonly used methods of these four processes are introduced, and feature extraction methods are classified from different multi-angle views. And then the new algorithm of multi-view emerged in recent years is highlighted. In addition, this paper summarizes the existing difficulties of gait recognition, and looks into the future development trends of it.
This study investigates a brain-computer interface (BCI) system based on an augmented reality (AR) environment and steady-state visual evoked potentials (SSVEP). The system is designed to facilitate the selection of real-world objects through visual gaze in real-life scenarios. By integrating object detection technology and AR technology, the system augmented real objects with visual enhancements, providing users with visual stimuli that induced corresponding brain signals. SSVEP technology was then utilized to interpret these brain signals and identify the objects that users focused on. Additionally, an adaptive dynamic time-window-based filter bank canonical correlation analysis was employed to rapidly parse the subjects’ brain signals. Experimental results indicated that the system could effectively recognize SSVEP signals, achieving an average accuracy rate of 90.6% in visual target identification. This system extends the application of SSVEP signals to real-life scenarios, demonstrating feasibility and efficacy in assisting individuals with mobility impairments and physical disabilities in object selection tasks.
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.
The research of sleep staging is not only a basis of diagnosing sleep related diseases but also the precondition of evaluating sleep quality, and has important clinical significance. In recent years, the research of automatic sleep staging based on computer has become a hot spot and got some achievements. The basic knowledge of sleep staging and electroencephalogram (EEG) is introduced in this paper. Then, feature extraction and pattern recognition, two key technologies for automatic sleep staging, are discussed in detail. Wavelet transform and Hilbert-Huang transform, two methods for feature extraction, are compared. Artificial neural network and support vector machine (SVM), two methods for pattern recognition are discussed. In the end, the research status of this field is summarized, and development trends of next phase are pointed out.
Steady-state visual evoked potential (SSVEP) is one of the commonly used control signals in brain-computer interface (BCI) systems. The SSVEP-based BCI has the advantages of high information transmission rate and short training time, which has become an important branch of BCI research field. In this review paper, the main progress on frequency recognition algorithm for SSVEP in past five years are summarized from three aspects, i.e., unsupervised learning algorithms, supervised learning algorithms and deep learning algorithms. Finally, some frontier topics and potential directions are explored.