The Research Least Squares Based on Ar Model of Glucose Prediction

Abstract

Diabetes Clinical Research important task is to monitor and prevent the occurrence of high / low blood sugar events. Glucose monitoring system (Continous Glucose Monitoring System, CGMS) is used clinically in recent years gradually new blood glucose monitoring system, by measuring the concentration of glucose in interstitial fluid glucose fluctuations throughout the day to indirectly reflect the whole picture. It can be 30 minutes early to predict blood glucose levels as well as low blood sugar warning. Based on time-series modeling techniques, with blood glucose levels for non-stationary paper with improved self-regression model (AR) on blood glucose prediction. And the order of the model parameters were determined by the least squares method and adaptive AIC criteria. The results show that the algorithm can be based on time series modeling real-time and accurate display changes in blood glucose levels, while forecasting and early warning of low blood sugar glucose results showed good performance.

Research of GPCEMBP Glucose Prediction Algorithm Based on Continuous Glucose Monitoring

“…Based on AR models, Wang et al [9] adopted the least squares method and adaptive AIC criteria to optimize the AR model. The result showed that the AR prediction algorithm could effectively predict blood glucose within 30 min [10]. Subsequently, Yang et al [11] proposed a new autoregressive integrated moving average (ARIMA) model with an adaptive identification algorithm of model orders.…”

The Research of Improved Grey GM (1, 1) Model to Predict the Postprandial Glucose in Type 2 Diabetes

“…CGMS (Continuous Glucose Monitoring System) is a device that is placed on the patient and used to measure patient's blood glucose every 5 minutes. Based on the blood glucose data provided by CGMS, many kinds of prediction methods of blood glucose were proposed, such as adaptive blood glucose prediction model [ 4 ], AR (autoregressive) model [ 5 ], neural network prediction model [ 6 ], and SVM (support vector machine) model [ 7 ]. Peng et al [ 4 ] applied Kalman filter to smooth the blood glucose data from the CGMS, using AR model to build up the blood glucose prediction model, and the result showed that the blood glucose changes can be dynamically captured and the future blood glucose can be predicted.…”

“…Peng et al [ 4 ] applied Kalman filter to smooth the blood glucose data from the CGMS, using AR model to build up the blood glucose prediction model, and the result showed that the blood glucose changes can be dynamically captured and the future blood glucose can be predicted. Wang and An [ 5 ] also adopted the AR model in predicting blood glucose; the result showed that the prediction was accurate with simple calculation, but their research did not take into account the smoothness of the original data. Tresp et al [ 6 ] utilized neural network algorithm to predict blood glucose and found that their model had good tracking ability.…”