RETROSPECTIVE SENSOR CALIBRATION METHODS

• Main Authors: LI XIAOLONG, LIU MIKE C, LIANG BRADLEY C, TSAI ANDY Y, ENGEL TALY G, SHAH RAJIV, VARSAVSKY ANDREA, NOGUEIRA KEITH, GROSMAN BENYAMIN, NISHIDA JEFFREY
• Format: Patent
• Language: Chinese; English
• Published: 23.10.2018
• Subjects: DIAGNOSIS; HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATIONTECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING ORPROCESSING OF MEDICAL OR HEALTHCARE DATA; HUMAN NECESSITIES; HYGIENE; IDENTIFICATION; INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTEDFOR SPECIFIC APPLICATION FIELDS; MEDICAL OR VETERINARY SCIENCE; PHYSICS; SURGERY

A method for retrospective calibration of a glucose sensor uses stored values of measured working electrode current (Isig) to calculate a final sensor glucose (SG) value retrospectively. The Isig values may be preprocessed, discrete wavelet decomposition applied. At least one machine learning model, such as, e.g., Genetic Programing (GP) and Regression Decision Tree (DT), may be used to calculateSG values based on the Isig values and the discrete wavelet decomposition. Other inputs may include, e.g., counter electrode voltage (Vcntr) and Electrochemical Impedance Spectroscopy (EIS) data. A plurality of machine learning models may be used to generate respective SG values, which are then fused to generate a fused SG. Fused SG values may be filtered to smooth the data, and blanked if necessary. 用于葡萄糖传感器的回顾性校准的方法，使用所测量的工作电极电流(Isig)的存储值来回顾性地计算最终的传感器葡萄糖(SG)值。可以对Isig值进行预处理，并应用离散小波分解。可以使用至少个机器学习模型，例如遗传编程(GP)和回归决策树(DT)来基于Isig值和离散小波分解计算SG值。其他输入可以包括例如对电极电压(Vcntr)和电化学阻抗谱(EIS)数据。可以使用多个机器学习模型来生成相应的SG值，然后将SG值融合以产生融合的SG。可以过滤