Denoising of Continuous Glucose Monitoring Signal with Adaptive SG Filter

摘要: This paper aims at simulating an intelligent Savitzky–Golay (SG) filter which can automatically adjust its parameters in accordance with the changes sampling or cut-off frequency. The is implemented on signals obtained from Continuous Glucose Monitoring (CGM) device. devices indicate any life-threatening (hyperglycemic hypoglycemic) event, so that a preventive action may be taken to control blood glucose. accuracy of these generally not very good due factors like sensor electronics and movement artefacts. In present research work, data GlucoSim simulator used, educational software simulate glucose insulin levels their dynamics healthy diabetic (Type-1) individuals. Genetic Algorithm Particle Swarm Optimization techniques are used tune leading GA-SGF PSO-SGF filters. signal denoised using adaptive It observed results provides fast efficient filtering.