One of the most important environmental cleanliness indicators is airborne microbe. However, the particularity of clean operating environment and controlled experimental environment often leads to the limitation of the airborne microbe research. This paper designed and implemented a microenvironment test chamber for airborne microbe research in normal test conditions. Numerical simulation by Fluent showed that airborne microbes were evenly dispersed in the upper part of test chamber, and had a bottom-up concentration growth distribution. According to the simulation results, the verification experiment was carried out by selecting 5 sampling points in different space positions in the test chamber. Experimental results showed that average particle concentrations of all sampling points reached 107 counts/m3 after 5 minutes’ distributing of Staphylococcus aureus, and all sampling points showed the accordant mapping of concentration distribution. The concentration of airborne microbe in the upper chamber was slightly higher than that in the middle chamber, and that was also slightly higher than that in the bottom chamber. It is consistent with the results of numerical simulation, and it proves that the system can be well used for airborne microbe research.
Detection and classification of malignant arrhythmia are key tasks of automated external defibrillators. In this paper, 21 metrics extracted from existing algorithms were studied by retrospective analysis. Based on these metrics, a back propagation neural network optimized by genetic algorithm was constructed. A total of 1,343 electrocardiogram samples were included in the analysis. The results of the experiments indicated that this network had a good performance in classification of sinus rhythm, ventricular fibrillation, ventricular tachycardia and asystole. The balanced accuracy on test dataset reached up to 99.06%. It illustrates that our proposed detection algorithm is obviously superior to existing algorithms. The application of the algorithm in the automated external defibrillators will further improve the reliability of rhythm analysis before defibrillation and ultimately improve the survival rate of cardiac arrest.
Acute respiratory distress syndrome (ARDS) is a serious threat to human life and health disease, with acute onset and high mortality. The current diagnosis of the disease depends on blood gas analysis results, while calculating the oxygenation index. However, blood gas analysis is an invasive operation, and can’t continuously monitor the development of the disease. In response to the above problems, in this study, we proposed a new algorithm for identifying the severity of ARDS disease. Based on a variety of non-invasive physiological parameters of patients, combined with feature selection techniques, this paper sorts the importance of various physiological parameters. The cross-validation technique was used to evaluate the identification performance. The classification results of four supervised learning algorithms using neural network, logistic regression, AdaBoost and Bagging were compared under different feature subsets. The optimal feature subset and classification algorithm are comprehensively selected by the sensitivity, specificity, accuracy and area under curve (AUC) of different algorithms under different feature subsets. We use four supervised learning algorithms to distinguish the severity of ARDS (P/F ≤ 300). The performance of the algorithm is evaluated according to AUC. When AdaBoost uses 20 features, AUC = 0.832 1, the accuracy is 74.82%, and the optimal AUC is obtained. The performance of the algorithm is evaluated according to the number of features. When using 2 features, Bagging has AUC = 0.819 4 and the accuracy is 73.01%. Compared with traditional methods, this method has the advantage of continuously monitoring the development of patients with ARDS and providing medical staff with auxiliary diagnosis suggestions.