Sleep stage classification is essential for clinical disease diagnosis and sleep quality assessment. Most of the existing methods for sleep stage classification are based on single-channel or single-modal signal, and extract features using a single-branch, deep convolutional network, which not only hinders the capture of the diversity features related to sleep and increase the computational cost, but also has a certain impact on the accuracy of sleep stage classification. To solve this problem, this paper proposes an end-to-end multi-modal physiological time-frequency feature extraction network (MTFF-Net) for accurate sleep stage classification. First, multi-modal physiological signal containing electroencephalogram (EEG), electrocardiogram (ECG), electrooculogram (EOG) and electromyogram (EMG) are converted into two-dimensional time-frequency images containing time-frequency features by using short time Fourier transform (STFT). Then, the time-frequency feature extraction network combining multi-scale EEG compact convolution network (Ms-EEGNet) and bidirectional gated recurrent units (Bi-GRU) network is used to obtain multi-scale spectral features related to sleep feature waveforms and time series features related to sleep stage transition. According to the American Academy of Sleep Medicine (AASM) EEG sleep stage classification criterion, the model achieved 84.3% accuracy in the five-classification task on the third subgroup of the Institute of Systems and Robotics of the University of Coimbra Sleep Dataset (ISRUC-S3), with 83.1% macro F1 score value and 79.8% Cohen’s Kappa coefficient. The experimental results show that the proposed model achieves higher classification accuracy and promotes the application of deep learning algorithms in assisting clinical decision-making.
Objective To investigate the expression of S100A12 protein in peripheral blood of patients with acute respiratory distress syndrome (ARDS) and its clinical significance. Methods Sixty ARDS patients admitted between October 2015 and December 2016 were included in the study. They were divided into a mild group (n=20), a moderate group (n=20) and a severe group (n=20) according to the oxygenation state. Meanwhile they were divided into a survival group (n=37) and a death group (n=23) according to the 30-day prognosis. Sixty simultaneous healthy subjects were selected as a control group. The clinical data of all subjects were collected. Fasting venous blood was collected in the morning for measurement of S100A12 expression in peripheral blood by ELISA method. Results Compared with the control group, the levels of S100A12, interleukin-1 (IL-1), tumor necrosis factor alpha (TNF-α) in peripheral blood and APACHEⅡ score were significantly increased in the ARDS patients, and PaO 2/FiO 2 was significantly decreased (all P<0.05). The level of S100A12 in peripheral blood was positively correlated with the severity of ARDS. The level of S100A12 was much higher in the severe group than that in the moderate group and the mild group, and higher in the moderate group than that in the mild group (allP<0.05). Correlation analysis showed that S100A12 level in peripheral blood of the ARDS patients was positively correlated with IL-1, TNF-α and APACHEⅡ score (P<0.05), but negatively correlated with PaO 2/FiO 2 (P<0.05). Logistic regression analysis showed that IL-1, TNF-α, APACHEⅡ score and S100A12 were independent risk factors for prognosis of ARDS. PaO 2/FiO 2 was an independent protective factor for prognosis of ARDS. ROC curve analysis showed that the S100A12 level had a certain predictive value for ARDS and could be used as a prognostic indicator. Conclusions The level of S100A12 in peripheral blood of ARDS patients is significantly increased and is closely related to the severity of ARDS. It has a potential clinical value for early diagnosis, treatment and prognosis of ARDS.