A signal processing application for evaluating self-monitoring blood glucose strategies in a software agent model. Issue 2 (July 2015)

• Record Type: Journal Article
• Authors: Wang, Zhanle; Paranjape, Raman
• Is Part Of: Computer methods and programs in biomedicine. Volume 120:Issue 2(2015)
• Page Start: 77
• Page End: 87
• Publication Date: 2015-07
• Subjects: Self-awareness -- Software agent -- Cross-correlation -- Self-monitoring blood glucose -- Diabetes; Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2015.04.008
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: A cross-correlation function and an average deviation between the continuous blood glucose and the interpolation of limited blood glucose samples are calculated. This model evaluates blood glucose monitoring frequency in a self-aware patient software agent model. This model provides diabetes patients and physicians a good understanding of the illness from limited blood glucose samples. We propose a new way to determine an appropriate blood glucose monitoring frequency to reduce the cost and discomfort associated with blood glucose monitoring. We propose a base line to assist people to determine an individual appropriate monitoring frequency based on their situations. Abstract: We propose the signal processing technique of calculating a cross-correlation function and an average deviation between the continuous blood glucose and the interpolation of limited blood glucose samples to evaluate blood glucose monitoring frequency in a self-aware patient software agent model. The diabetic patient software agent model[1] is a 24-h circadian, self-aware, stochastic model of a diabetic patient's blood glucose levels in a software agent environment. The purpose of this work is to apply a signal processing technique to assist patients and physicians in understanding the extent of a patient's illness using a limited number of blood glucose samples. A second purpose of this work is to determine an appropriate blood glucose monitoring frequency in order to have a minimum number of samples taken that still provide a good understanding of the patient's blood glucose levels. For society in general, the monitoring cost of diabetes is an extremely important issue, and these costs can vary tremendously depending on monitoring approaches and monitoring frequencies. Due to the cost and discomfort associated with blood glucose monitoring, today, patients expect monitoring frequencies specific to their health profile. The proposed method quantitatively assesses various monitoring protocols (from 6 times per day to 1 time per week) in nine predefined categories of patient agents in terms of risk factors of health status and age. Simulation results show that sampling 6 times per day is excessive, and not necessary for understanding the dynamics of the continuous signal in the experiments. In addition, patient agents in certain conditions only need to sample their blood glucose 1 time per week to have a good understanding of the characteristics of their blood glucose. Finally, an evaluation scenario is developed to visualize this concept, in which appropriate monitoring frequencies are shown based on the particular conditions of patient agents. This base line can assist people in determining an appropriate monitoring frequency based on their personal health profile.