The Research On Data Analysis And Prediction Of Physical Parameters In Intelligent Monitoring System Of Health

With the improvement of the people's lives, the expectation of life is longing, and the old people is becoming more and more. To meet with the health of old people and improve the living quality, Community medical service should be strengthened aiming at the focus on sanitation of old people's healthy life. In China, tele-monitoring of health has been developed in recent decade, it is the inevitable result with the development of society, and it is closely related with medicine, information science, computer, network and communication. Many methods of monitoring information process have been put forward as a lot of work for monitoring system has been done by scholars both at home and abroad. However, the traditional processing methods have its defects or suitable conditions in the actual application. The hope of monitoring system is more accurate and more comprehensive with the progress of the society. Therefore, the study on the tele-monitoring system and the processing methods of health information has important realistic meaning and extension value.The analyzing methods of monitoring information have been put forward on the basis of analysis of the characteristics of monitoring information and its related technologies, and the recent studies. According to the mass monitoring data, the technologies of data mining for physical parameters, such as wavelet analysis, support vector machines and neural networks, have been used to eliminate redundant data, identify abnormal data in physical parameters which will influence diagnosis, and forecast techniques have been used to realize forecast of monitoring information.The main work and contributions of the paper are as follows.1. The signal processing methods for monitoring information have been brought forward. As the monitoring signals are disturbed by noise, artifact and data loss, the error valuations of signal parameter, and it leads to the error alarm of guardianship. Because of wavelet transform can restrain jamming signal and noise contained in monitoring information effectively, the method of wavelet transform based on improved threshold is put forward to handle faint noise interference. While the combined method of wavelet transform and Hampel is adopted to strong noise signal. The experiment results show that the proposed denoising method can achieve good results compared with traditional wavelet threshold, and it can improve the accuracy and validity of alarm.2. The methods of identifying abnormal values of monitoring information have been raised. According to the problem of identifying abnormal monitoring information, the abnormal recognition method of monitoring information is put forward, which is combined hypothesis based on improved statistics with AR(p) based on Kalman filter, after studying on many other deposing method of abnormal. First, the abnormal monitoring data has been identified by means of improving hypothesis, and it will be delete directly. Then the deleted points are took as missing value, and the abnormal values are be predicted with AR(p) model based on Kalman filtering algorithm, and we fill the abnormal values with the predicted values, which is the preparation for next prediction.3. To realize the alarm of monitoring system, the integrated forecast model of monitoring information based on wavelet decomposition and least squares supports vector regression are established. Firstly, obtaining each decomposed layers sequence of monitoring information with wavelet decomposition. Secondly, the each decomposed layers sequence are predicted by using the least square support vector regression machine. Finally, realizing the prediction of monitoring information through composing the forecast results of each sequence. Meanwhile, the LS-SVMs are optimized by using PSO, and the related experiments are conducted and the results show the integrated method can obtain better forecasting results than other forecast methods.4. The article develops the experiment platform of intelligent health monitoring system. System structure, hardware and software architecture are designed. Healthy monitoring system is programmed on the operation system Windows NT. SQL Server 2000 is used as database, and Windows visual studio.net 2005 is adopted as development tool. Experiment results show that health monitoring system has the function of reducing noise effectively, identification of unusual values and prediction. The efficiency of proposed methods are intensified by experiment results.