Systems and methods for replacing signal artifacts in a glucose sensor data stream
Systems and methods for minimizing or eliminating transient non-glucose related signal noise due to non-glucose rate limiting phenomenon such as ischemia, pH changes, temperatures changes, and the like. The system monitors a data stream from a glucose sensor and detects signal artifacts that have higher amplitude than electronic or diffusion-related system noise. The system replaces some or the entire data stream continually or intermittently including signal estimation methods that particularly address transient signal artifacts. The system is also capable of detecting the severity of the signal artifacts and selectively applying one or more signal estimation algorithm factors responsive to the severity of the signal artifacts, which includes selectively applying distinct sets of parameters to a signal estimation algorithm or selectively applying distinct signal estimation algorithms.
1. A system for processing data signals obtained from a glucose sensor, the system comprising:
a sensor data receiving module that receives a signal from a continuous glucose sensor over a period of time, the signal comprising sensor data indicative of measured glucose concentrations; and
a processor configured to form two different conversion functions, to select one of the conversion functions depending on an estimated glucose value based on a first portion of the sensor data, and to convert a second, subsequent portion of the sensor data from the continuous glucose sensor into estimated glucose values using the selected conversion function.
2. The system of claim 1 , wherein processor module is further configured to cause display on a display device of at least one of a numeric representation of the glucose values, an indication of directional trend of the glucose values, and a graphical representation of the glucose values.
3. The system of claim 1 , wherein forming the two conversion functions comprises applying a linear regression or a non-linear regression algorithm to at least some of the sensor data.
4. The system of claim 3 , further comprising a reference data receiving module configured to receive reference data from a reference glucose monitor, wherein the applying comprises applying the linear regression or the non-linear regression algorithm to the reference data and the at least some sensor data.
5. The system of claim 4 , wherein the processor module is further configured to match the reference data with time-corresponding sensor data to form one or more matched data pairs, wherein the applying comprises applying the linear regression or the non-linear regression algorithm to the matched data pairs.
6. The system of claim 1 , wherein forming the two conversion functions comprises applying an algorithm to at least some of the sensor data, wherein the algorithm is selected from the group consisting of a fuzzy logic algorithm, a neural network algorithm, a piece-wise linear regression algorithm, a polynomial fit algorithm, a genetic algorithm, and a pattern recognition algorithm.
7. The system of claim 6 , further comprising a reference data receiving module configured to receive reference data from a reference glucose monitor, wherein the applying comprises applying the algorithm to the reference data and the at least some sensor data.
8. The system of claim 7 , wherein the processor module is further configured to match the reference data with time-corresponding sensor data to form one or more matched data pairs, wherein the applying comprises applying algorithm to the matched data pairs.
9. The system of claim 1 , wherein each of the two different conversion functions is a linear function.