Research On Glucose Prediction Based On Chaotic Time Series Analyze And Echo State Networks

With the rapid improvement of living quality,the morbidity of diabetes trends to increasing.It is major global health problem that how to cure the diabetes.Nowadays,continuous insulin injection through the artificial pancreas(AP)is considered as one of the most effective means to treat diabetes.Accurate glucose prediction not only can provide indispensable information for designing AP,but also can warn the upcoming the hyper/hypoglycemia events for patients or doctors.Frist,the chaos characteristic in glucose time series has been identified using the chaos theory,then the chaos characteristic is proved by using the Wolf algorithm and small-data method respectively.The obtained results of Lyapunov demonstrate that the glucose time series have chaotic.Next,based on the Takens theorem,the two parameters,named optimal embedded demission and delay time,can be obtained with the Autocorrelation Function and False Nearest Neighbors for reconstructing the phase spaceWhen the reconstruction completed,a fast-nonlinear dynamic network is conducted with intention of modeling the more precise prediction function.Considering the crucial relationship between the prediction performance and the lay of middle,the paper presents a novel strategy combing the increasing learning with ESN to optimize the optimal middle nodes online.Besides,in order to stress the connection data and improve the model predictability,this article designs output feedback structure,refer to F-ESN,linking the prediction output and input.Ultimately,the experiments have been enforced with the real patients' data,which indicates the proposed method(F-I-ESN)is superior than others algorithms.A novel method combined the F-ESN with(Dynamic Risk,DR),named as DR-FESN,is proposed for predicting the hypoglycemia occurrence.DR is capability of evaluating the hypoglycemia risk by the fluctuation information of glucose and its variation tendency.The simulations are conducted with 12 virtual patients who have significant hypoglycemia symptom,which show that DR-FESN can greatly advance the early warning time without losing the sensitivity and specificity of hypoglycemia events.That will gain valuable cure time for patients and doctors.