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Objective To analyze the risk factors of traumatic subdural effusion (TSE) transforming into chronic subdural hematoma (CSDH). Methods A totalof75 patients with TSE after traumatic brain injury (TBI) were included from December 2017 to July 2019. CT or MRI reexamination during treatment and follow⁃up were performed to observe whether CSDH was transformed and relevant influencing factors were analyzed. Univariate and multivariate Logistic regression analyses were performed to screen the risk factors for the transformation of TSE into CSDH. Results Logistic regression analysis showed that the higher effusion density (OR =6.021, 95%CI: 1.047-34.631; P =0.044), subdural effusion increases for a long time (OR = 1.253, 95%CI: 1.002-1.568; P = 0.048), the thicker effusion (OR = 2.080, 95%CI: 1.267-3.414; P =0.004) and brain atrophy (OR =35.392, 95%CI: 1.397-896.474; P =0.031) were risk factors for the transformation of TSE into CSDH. Conclusions The subdural effusion increases for a long time, higher effusion density, thicker effusion and brain atrophy are the risk factors associated with the transformation of TSE into CSDH. DOI：10.3969/j.issn.1672⁃6731.2020.07.008

The existing emotion recognition researches generally use neural network and attention mechanism to learn emotional features,which have relatively single feature representation.Moreover,neuroscience studies have shown that EEG signals of different frequencies and channels have different responses to emotion.Therefore,this paper proposes a method of fusing frequency and electrode channel convolutional attention for EEG emotion recognition.Specifically,EEG signals are firstly decomposed into different frequency bands and the corresponding frame-level features are extracted.Then the pre-activated residual network is employed to learn deep emotion-relevant features.At the same time,the frequency and electrode channel convolutional attention module is integrated into each pre-activated residual unit of residual network to model the frequency and channel information of EEG signals,thus generating final representation of EEG features.Experiments on DEAP and DREAMER datasets show that the proposed method helps to enhance the importing of emotion-salient information in EEG signals when compared with single-layer attention mechanism,and generates better recognition performance.