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At present many hospitals have to deal with the patient’s care and nursing for Acute Hypotensive Episodes (AHE) occurring in Intensive Care Units. AHE can cause fainting or shock suddenly, leading to irreversibility organ damage, and even death. Therefore, forecasting of occurrence of AHE is of practical value. However, the prediction of clinical AHE largely depends on the doctors’ experience, which cannot guarantee the high rate of success. It is thus very meaningful for the clinical care to use appropriate methods to predict the AHE with an automatic and reliable method. In this study, a Probability Distribution Patterns Analysis (PDPA) method is presented to solve the time series prediction problem of AHE. In the first phase, the features are extracted from the PDPA in the global and integral time series, and the partial local time series in the fixed time window. In the second phase, the proposed algorithm combining Genetic Algorithm (GA) and Support Vector Machine (SVM), namely GA-SVM is adopted to select the vital features for the effective classification. In order to demonstrate the generality of our method, we also conduct experiments on a classical time series problem-Control Chart Patterns (CCPs) multi-class time series, which is a benchmark problem in the process control. For CCPs problem, the experimental results demonstrate that the proposed method outperforms several traditional methods. The obtained accuracy is 98.65%, which is superior to listed previous works using the same CCPs model. For AHE classification and forecasting, the methodology is applied in two data sets, a small data set (37 records) and a big one (2892 records). The test accuracy of 89.19%, sensitivity of 91.67%, specificity of 88% in the small data set, and a test accuracy of 80.76%, sensitivity of 78.19%, specificity of 81.51% in the big data set are achieved with the classification model. [ABSTRACT FROM AUTHOR]

Phase-sensitive optical time domain reflectometer (Φ-OTDR) can detect the occurrence of external vibration, but lacks of ability to classify multiple similar events. With the help of machine learning method, the events can be basically classified. An event recognition method based on deep learning and traditional classifier is proposed in this paper to further improve the classification accuracy. Firstly, the temporal-spatial data matrix from Φ-OTDR is directly sent to convolutional neural network (CNN) and the data features can be obtained automatically. Then these data features are transmitted to a traditional classifier for further classification. The traditional classifier can find a better classification hyperplane to improve the classification accuracy. In this paper, the most suitable classifier is support vector machine (SVM). As CNN works as a black box, T-distributed stochastic neighbor embedding (T-SNE) method and gradient-weighted class activation mapping (Grad-CAM) method are applied to visualize CNN's working process, which illustrates the correctness of feature extraction. Experimental results based on 11,997 images under eight event categories show that the classification accuracy can be improved by the traditional classifier. The CNN+SVM strategy performs the best and reaches 94.17 % accuracy, which offers 2.04 % improvement compared with using CNN alone. [ABSTRACT FROM AUTHOR]