Nonlinear Systematic Methods And Dynamic Complex Networks For Medical Data Analysis And Integration

The nonlinear and systematic methods, which describe their research target from a holistic and systematic point of view, show better adaptability when modeling the high-dimensional and complex medical data.In this paper, two types of data: EEG data of epilepsies patients and SELDI-TOF serum protein mass spectrometry data of patients with gastric cancer are processed based on the nonlinear theory and the technology of phase space reconstruction. Some valuable results were obtained from the data processing and model building procedure. The major works involved in this paper are as follows:(1) This paper presented the analysis and modeling of a nonlinear system based on the EEG data of epilepsies patients. From the engineering implementation point of view, this paper established a uniform processing channel, including data filtering, preprocessing, dimension reduction, phase space reconstruction, classifier modeling and computational optimization, which make the method complete, robust and real-time.More specifically, in this paper multi-channel data have been filtered with nonlinear characteristics by method of surrogate data. Factor analysis is used for dimension reduction, Improved multi-channel phase space reconstruction technology is used for reconstruction of the EEG strange attractor. Furthermore, the classifier model of EEG data in illness-period and inter-illness-period has been constructed by the correlation dimension of singular attractors. In the mean time a comprehensive discussion and optimization have been finished on the time-complexity of the correlation dimension computation, in order to satisfy the requirement of real time on-line computation.(2) Focusing on the insufficiency of the current spectra] modeling of protein biomarkers, this paper proposed a new method for protein mass spectrometry data modeling and discrimination based on the nonlinear theory and systematic methods. This paper carried out the modeling and classification of the SELDI-TOF serum protein mass spectrometry data of patients with gastric cancer. Using phase space reconstruction, the data from 20 normal samples and 20 cancer samples are reconstructed in 2 specific data ranges, based on which a linear classifier model is established. The result verified the superiority over the classifier model of single protein biomarkers.(3) The concept of dynamic complex network modeling has been introduced to the modeling of high-dimensional, complex medical data. A feasibility study and further work have been proposed according to the complex network modeling of high-dimensional, continuous medical data.