Objective To investigate the pleural effusion lymphocyte subsets in patients with pneumonia complicated with pleural effusion and its relationship with the occurrence of critical illness. MethodsPatients with pneumonia complicated with pleural effusion (246 cases) admitted to our hospital from January 2020 to June 2022 were selected as the research subjects. According to the severity of pneumonia, they were divided into a critical group (n=150) and a non-critical group (n=96). After 1:1 matching by propensity score matching method, there were 60 cases in each group. The general data of the two groups were compared. CD3+, CD4+, CD8+, CD4+/CD8+ ratio were detected by flow cytometry. Multivariate logistic regression was used to analyze the risk factors of critical pneumonia, and a nomogram prediction model was constructed and evaluated. The relationship between PSI score and lymphocyte subsets in pleural effusion was analyzed by local weighted regression scatter smoothing (LOWESS). Results After matching, the differences between the two groups of patients in the course of disease, heat peak, heat course, atelectasis, peripheral white blood cell count (WBC), C-reactive protein (CRP), D-dimer (D-D), procalcitonin (PCT) and hemoglobin were statistically significant (P<0.05). Compared with the non-critical group, the proportion of CD3+, CD4+, CD4+/CD8+ cells in critical group was lower (P<0.05), and the proportion of CD8+ cells was higher (P<0.05). Combined atelectasis, increased course of disease, fever peak and fever course, increased WBC, CRP, D-D, CD8+ and PCT levels, and decreased CD3+, CD4+, CD4+/CD8+ and Hb levels were independent risk factors for the occurrence of critical pneumonia (P<0.05). The nomogram prediction model based on independent influencing factors had high discrimination, accuracy and clinical applicability. There was a certain nonlinear relationship between pneomonia severity index and CD3+, CD4+, CD8+ and CD4+/CD8+. Conclusions Lymphocyte subsets in pleural effusion are closely related to the severity of pneumonia complicated with pleural effusion. If CD3+, CD4+, CD8+ and CD4+/CD8+ are abnormal, attention should be paid to the occurrence of severe pneumonia.
ObjectiveTo investigate the correlation between graft maturity and knee function after anterior cruciate ligament (ACL) reconstruction.MethodsA total of 50 patients who underwent ACL reconstruction with autologous tendons between August 2016 and August 2018 were included in the study. There were 28 males and 22 females, with an average age of 31.0 years (range, 18-50 years). At 6 months and 2 years after operation, the signal to noise quotient (SNQ) values of tibial and femoral ends of graft were measured by MRI, and the mean value was taken as the SNQ value of graft. The function of knee joint was evaluated by Tegner, Lysholm, and International Knee Documentation Committee (IKDC) scores. The differences in SNQ values between tibial and femoral ends were analyzed at 6 months and 2 years after operation. The correlation between SNQ value at 6 months after operation and knee function score at 2 years after operation was analyzed. According to SNQ value at 6 months after operation, the patients were divided into group A (SNQ value≥12) and group B (SNQ value<12) and the correlation between SNQ value and knee function score was further analyzed.ResultsAll incisions healed primarily without infection or injury of blood vessels and nerves. All patients were followed up 24-28 months (mean, 26.6 months). The IKDC, Lysholm, and Tegner scores at 6 months and 2 years after operation were significantly higher than those before operation (P<0.05), and all scores at 2 years after operation were also significantly higher than those at 6 months (P<0.05). The SNQ values at 6 months and 2 years after operation were 12.517±6.272 and 10.900±6.012, respectively, and the difference was significant (t=1.838, P=0.007). The SNQ values of graft at 6 months after operation were significantly different from those at 2 years after operation (P<0.05), and the SNQ values of tibial and femoral ends of graft at the same time point were significantly different (P<0.05). The SNQ value of 50 patients at 6 months after operation was negatively correlated with Lysholm, IKDC, and Tegner scores at 2 years after operation (r=–0.965, P=0.000; r=–0.896, P=0.000; r=–0.475, P=0.003). The patients were divided into groups A and B according to the SNQ value, each with 25 cases; the SNQ values of the two groups at 6 months after operation were negatively correlated with Lysholm, IKDC, and Tegner scores at 2 years after operation (P<0.05).ConclusionAfter ACL reconstruction, the knee function scores and graft maturity of patients gradually improved. The lower the SNQ value in the early stage, the higher the knee function score in the later stage. The SNQ value of MRI in the early stage after ACL reconstruction can predict the knee function in the later stage.
Heart valve disease (HVD) is one of the common cardiovascular diseases. Heart sound is an important physiological signal for diagnosing HVDs. This paper proposed a model based on combination of basic component features and envelope autocorrelation features to detect early HVDs. Initially, heart sound signals lasting 5 minutes were denoised by empirical mode decomposition (EMD) algorithm and segmented. Then the basic component features and envelope autocorrelation features of heart sound segments were extracted to construct heart sound feature set. Then the max-relevance and min-redundancy (MRMR) algorithm was utilized to select the optimal mixed feature subset. Finally, decision tree, support vector machine (SVM) and k-nearest neighbor (KNN) classifiers were trained to detect the early HVDs from the normal heart sounds and obtained the best accuracy of 99.9% in clinical database. Normal valve, abnormal semilunar valve and abnormal atrioventricular valve heart sounds were classified and the best accuracy was 99.8%. Moreover, normal valve, single-valve abnormal and multi-valve abnormal heart sounds were classified and the best accuracy was 98.2%. In public database, this method also obtained the good overall accuracy. The result demonstrated this proposed method had important value for the clinical diagnosis of early HVDs.
Sleep apnea syndrome (SAS) is a kind of common and harmful systemic sleep disorder. SAS patients have significant iconography changes in brain structure and function, and electroencephalogram (EEG) is the most intuitive parameter to describe the sleep process which can reflect the electrical activity and function of brain tissues. Based on the non-stationary and nonlinear characteristics of EEG, this paper analyzes the correlation dimension of sleep EEG in patients with SAS. Six SAS patients were classed as SAS group and six healthy persons were classified into a control group. The results showed that the correlation dimension of sleep EEG in the SAS group and the control group decreased gradually with the deepening of sleep, and then increased to the level of awake and light sleep stage with rapid eye movement (REM). The correlation dimension of SAS group was significantly lower than that of control group (P<0.01) throughout all the stages. The results suggested that there were significant nonlinear dynamic differences between the EEG signals of SAS patients and of healthy people, which provided a new direction for the study of the physiological mechanism and automatic detection of SAS.
Electroencephalogram (EEG) serves as an effective indicator of detecting fatigue driving. Utilizing the open accessible Shanghai Jiao Tong University Emotion Electroencephalography Dataset (SEED-VIG), driving states are divided into three categories including awake, tired and drowsy for investigation. Given the characteristics of mutual influence and interdependence among EEG channels, as well as the consistency of the graph convolutional neural network (GCNN) structure, we designed an adjacency matrix based on the Pearson correlation coefficients of EEG signals among channels and their positional relationships. Subsequently, we developed a GCNN for recognition. The experimental results show that the average classification accuracy of driving state categories for 20 subjects, from the SEED-VIG dataset under the smooth feature of differential entropy (DE) linear dynamic system is 91.66%. Moreover, the highest classification accuracy can reach 98.87%, and the average Kappa coefficient is 0.83. This work demonstrates the reliability of this method and provides a guideline for the research field of safe driving brain computer interface.
We proposed a multi-resolution-wavelet-transform based method to extract brainstem auditory evoked potential (BAEP) from the background noise and then to identify its characteristics correctly. Firstly we discussed the mother wavelet and wavelet transform algorithm and proved that bi-orthogonal wavelet bior5.5 and stationary discrete wavelet transform (SWT) were more suitable for BAEP signals. The correlation analysis of D6 scale wavelet coefficients between single trails and the ensemble average of all trails showed that the trails with good correlation (> 0.4) had higher signal-to-noise ratio, so that we could get a clear BAEP from a few trails by an average and wavelet filter method. Finally, we used this method to select desirable trails, extracted BAEP from every 10 trails and calculated theⅠ-Ⅴinter-waves' latency. The results showed that this strategy of trail selection was efficient. This method can not only achieve better de-noising effect, but also greatly reduce the stimulation time needed as well.
The brain-computer interface (BCI) systems used in practical applications require as few electroencephalogram (EEG) acquisition channels as possible. However, when it is reduced to one channel, it is difficult to remove the electrooculogram (EOG) artifacts. Therefore, this paper proposed an EOG artifact removal algorithm based on wavelet transform and ensemble empirical mode decomposition. Firstly, the single channel EEG signal is subjected to wavelet transform, and the wavelet components which involve EOG artifact are decomposed by ensemble empirical mode decomposition. Then the predefined autocorrelation coefficient threshold is used to automatically select and remove the intrinsic modal functions which mainly composed of EOG components. And finally the ‘clean’ EEG signal is reconstructed. The comparative experiments on the simulation data and the real data show that the algorithm proposed in this paper solves the problem of automatic removal of EOG artifacts in single-channel EEG signals. It can effectively remove the EOG artifacts when causes less EEG distortion and has less algorithm complexity at the same time. It helps to promote the BCI technology out of the laboratory and toward commercial application.
Objective To reflect the correlation between social support and mental health of the aged through the Pearson correlation coefficient. Methods Databases including PubMed, SpringerLink, EMbase, The Cochrane Library, VIP, WanFang Data and CNKI were searched from inception to October, 2011 to collect literature on the correlation between social support and mental health of the aged. The studies were screened according to the inclusion and exclusion criteria. After extracting data and assessing the quality of the included studies, meta-analysis was conducted using RevMan 5.0 software. Results Of the 2 396 identified studies, 4 studies were included. The results showed that 4 studies were not high in the overall quality. The total score of social support of the elderly and its three dimensions were related to mental health. Among 9 factors associated with mental health, somatization, depression and anxiety were weakly correlated to the objective support while the others were extremely weakly correlated. Anxiety and phobic anxiety were weakly correlated to the subjective support while the others were extremely weakly correlated. Phobic anxiety was weakly correlated to the utilizing degree while the others were extremely weakly correlated. Somatization, anxiety and phobic anxiety were weakly correlated to the total score of social support while the others were extremely weakly correlated. Conclusion Social support probably improves mental health of the aged to some extent.
Impedance cardiography (ICG) is essential in evaluating cardiac function in patients with cardiovascular diseases. Aiming at the problem that the measurement of ICG signal is easily disturbed by motion artifacts, this paper introduces a de-noising method based on two-step spectral ensemble empirical mode decomposition (EEMD) and canonical correlation analysis (CCA). Firstly, the first spectral EEMD-CCA was performed between ICG and motion signals, and electrocardiogram (ECG) and motion signals, respectively. The component with the strongest correlation coefficient was set to zero to suppress the main motion artifacts. Secondly, the obtained ECG and ICG signals were subjected to a second spectral EEMD-CCA for further denoising. Lastly, the ICG signal is reconstructed using these share components. The experiment was tested on 30 subjects, and the results showed that the quality of the ICG signal is greatly improved after using the proposed denoising method, which could support the subsequent diagnosis and analysis of cardiovascular diseases.
Heart sound segmentation is a key step before heart sound classification. It refers to the processing of the acquired heart sound signal that separates the cardiac cycle into systolic and diastolic, etc. To solve the accuracy limitation of heart sound segmentation without relying on electrocardiogram, an algorithm based on the duration hidden Markov model (DHMM) was proposed. Firstly, the heart sound samples were positionally labeled. Then autocorrelation estimation method was used to estimate cardiac cycle duration, and Gaussian mixture distribution was used to model the duration of sample-state. Next, the hidden Markov model (HMM) was optimized in the training set and the DHMM was established. Finally, the Viterbi algorithm was used to track back the state of heart sounds to obtain S1, systole, S2 and diastole. 500 heart sound samples were used to test the performance of our algorithm. The average evaluation accuracy score (F1) was 0.933, the average sensitivity was 0.930, and the average accuracy rate was 0.936. Compared with other algorithms, the performance of our algorithm was more superior. It is proved that the algorithm has high robustness and anti-noise performance, which might provide a novel method for the feature extraction and analysis of heart sound signals collected in clinical environments.