WO2008157187A1 - Systems and methods for replacing signal artifacts in a glucose sensor data stream - Google Patents
Systems and methods for replacing signal artifacts in a glucose sensor data stream Download PDFInfo
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- WO2008157187A1 WO2008157187A1 PCT/US2008/066600 US2008066600W WO2008157187A1 WO 2008157187 A1 WO2008157187 A1 WO 2008157187A1 US 2008066600 W US2008066600 W US 2008066600W WO 2008157187 A1 WO2008157187 A1 WO 2008157187A1
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
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- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
- A61B5/7203—Signal processing specially adapted for physiological signals or for diagnostic purposes for noise prevention, reduction or removal
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- A61B5/145—Measuring characteristics of blood in vivo, e.g. gas concentration, pH value; Measuring characteristics of body fluids or tissues, e.g. interstitial fluid, cerebral tissue
- A61B5/14532—Measuring characteristics of blood in vivo, e.g. gas concentration, pH value; Measuring characteristics of body fluids or tissues, e.g. interstitial fluid, cerebral tissue for measuring glucose, e.g. by tissue impedance measurement
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- A61B5/14539—Measuring characteristics of blood in vivo, e.g. gas concentration, pH value; Measuring characteristics of body fluids or tissues, e.g. interstitial fluid, cerebral tissue for measuring pH
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- A61B5/1486—Measuring characteristics of blood in vivo, e.g. gas concentration, pH value; Measuring characteristics of body fluids or tissues, e.g. interstitial fluid, cerebral tissue using enzyme electrodes, e.g. with immobilised oxidase
- A61B5/14865—Measuring characteristics of blood in vivo, e.g. gas concentration, pH value; Measuring characteristics of body fluids or tissues, e.g. interstitial fluid, cerebral tissue using enzyme electrodes, e.g. with immobilised oxidase invasive, e.g. introduced into the body by a catheter or needle or using implanted sensors
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- A61B5/6847—Arrangements of detecting, measuring or recording means, e.g. sensors, in relation to patient specially adapted to be brought in contact with an internal body part, i.e. invasive mounted on an invasive device
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Definitions
- detecting an occurrence of a signal artifact event comprises determining a differential between a first residual at a first time point and a second residual at a second time point.
- the first predetermined condition is a residual amplitude that is at least about 5% of a sensor data amplitude
- the second predetermined condition is a residual amplitude that is no more than about 5% of a sensor data amplitude
- the received data comprises an unfiltered signal
- the instructions for processing the received data comprise instructions for displaying a graphical representation of the unfiltered data except when a signal artifact event has occurred.
- the electronics further comprise programming configured to filter the sensor data, and wherein the programming configured to process the sensor data comprises programming configured to display a graphical representation of the filtered data responsive to a determination of a start of a signal artifact event.
- the first predetermined condition is a differential amplitude that is at least about 5% of a sensor data amplitude
- the second predetermined condition is a differential amplitude that is no more than about 5% of a sensor data amplitude
- a device comprising a computer readable memory, the computer readable memory containing code for analyzing data from a continuous analyte sensor, wherein the code comprises: instructions for receiving data from an analyte sensor, the data comprising at least one sensor data point; instructions for detecting an occurrence of a signal artifact event based at least in part on an amplitude of a signal artifact; and instructions for processing the received data, wherein the processing is based at least in part upon whether a signal artifact event has occurred.
- the instructions for detecting an occurrence of a signal artifact event comprise instructions for determining an amplitude of the sensor data and instructions for determining an amplitude of a signal artifact.
- the first predetermined condition is different from the second predetermined condition.
- the instructions for detecting an occurrence of a signal artifact event comprise instructions for comparing the received data with filtered data to obtain at least one residual.
- Fig. 4C is an illustration of the receiver in another embodiment showing a representation of analyte concentration and directional trend using a gradient bar.
- Fig. 19 is a graph that illustrates dynamic and intelligent estimation algorithm selection applied to a data stream in one embodiment.
- Fig. 25 is a flow chart that illustrates the process of estimating, measuring, and comparing analyte values in one embodiment.
- Fig. 27 provides a flow chart that illustrates the evaluation of reference and/or sensor data for statistical, clinical, and/or physiological acceptability in one embodiment.
- Fig. 28 is a flow chart that illustrates the evaluation of calibrated sensor data for aberrant values in one embodiment.
- Fig. 29 provides a flow chart that illustrates a self-diagnostic of sensor data in one embodiment.
- Fig. 32 is a flowchart that illustrates a method for processing data from a glucose sensor in certain embodiments.
- the analyte can be introduced into the body, for example, a contrast agent for imaging, a radioisotope, a chemical agent, a fluorocarbon-based synthetic blood, or a drug or pharmaceutical composition, including but not limited to insulin; ethanol; cannabis (marijuana, tetrahydrocannabinol, hashish); inhalants (nitrous oxide, amyl nitrite, butyl nitrite, chlorohydrocarbons, hydrocarbons); cocaine (crack cocaine); stimulants (amphetamines, methamphetamines, Ritalin, Cylert, Preludin, Didrex, PreState, Voranil, Sandrex, Plegine); depressants (barbituates, methaqualone, tranquilizers such as Valium, Librium, Miltown, Serax, Equanil, Tranxene); hallucinogens (phencyclidine, lysergic acid, mescaline, peyote, p
- calibrated data and “calibrated data stream” as used herein are broad terms and are to be given their ordinary and customary meaning to a person of ordinary skill in the art (and are not to be limited to a special or customized meaning), and furthermore refer without limitation to data that has been transformed from its raw state to another state using a function, for example a conversion function, to provide a meaningful value to a user.
- a function for example a conversion function
- electrosenor circuitry is a broad term and is to be given its ordinary and customary meaning to a person of ordinary skill in the art (and is not to be limited to a special or customized meaning), and furthermore refers without limitation to the components of a device configured to process biological information obtained from a host.
- the biological information is obtained by a sensor regarding a particular glucose in a biological fluid, thereby providing data regarding the amount of that glucose in the fluid.
- wavelet transform is a broad term and is to be given its ordinary and customary meaning to a person of ordinary skill in the art (and is not to be limited to a special or customized meaning), and furthermore refers without limitation to a transform which converts a signal into a series of wavelets, which in theory allows signals processed by the wavelet transform to be stored more efficiently than ones processed by Fourier transform. Wavelets can also be constructed with rough edges, to better approximate real-world signals.
- linear regression is a broad term and is to be given its ordinary and customary meaning to a person of ordinary skill in the art (and is not to be limited to a special or customized meaning), and furthermore refers without limitation to finding a line in which a set of data has a minimal measurement from that line.
- Byproducts of this algorithm include a slope, a y-intercept, and an R-Squared value that determine how well the measurement data fits the line.
- estimated analyte values can have a variation including a range of values outside of the estimated analyte values that represent a range of possibilities based on known physiological patterns, for example.
- the analyte sensor is an implantable glucose sensor, such as described with reference to U.S. Patent 6,001,067 and U.S. Patent Publication No. US-2005-0027463-A1.
- the analyte sensor is a transcutaneous glucose sensor, such as described with reference to U.S. Patent Publication No. US-2006-0020187-A1.
- the sensor is configured to be implanted in a host vessel or extracorporeally, such as is described in U.S. Patent Publication No. US-2007-0027385-A1, co-pending U.S. Patent Application No. 11/543,396 filed October 4, 2006, co-pending U.S. Patent Application No.
- electrodes formed from bulk metal wire provide superior performance (e.g., in contrast to deposited electrodes), including increased stability of assay, simplified rnanufacturability, resistance to contamination (e.g., which can be introduced in deposition processes), and unproved surface reaction (e.g., due to purity of material) without peeling or delamination.
- sodium bicarbonate is an advantageous grit-material because it is sufficiently hard to ablate, e.g., a parylene coating without damaging, e.g., an underlying platinum conductor.
- One additional advantage of sodium bicarbonate blasting includes its polishing action on the metal as it strips the polymer layer, thereby eliminating a cleaning step that might otherwise be necessary.
- the sensing membranes and/or membrane systems include a plurality of domains or layers, for example, an interference domain, an enzyme domain, and a resistance domain, and may include additional domains, such as an electrode domain, a cell impermeable domain (also referred to as a bioprotective layer), and/or an oxygen domain, as described in more detail in co-pending U.S. Patent Application No. 11/750,907 filed on May 18, 2007 and entitled "ANALYTE SENSORS HAVING A SIGNAL-TO-NOISE RATIO SUBSTANTIALLYUNAFFECTED BY NON-CONSTANT NOISE,” which is incorporated herein by reference in its entirety.
- Fig. 3 A illustrates a first embodiment wherein the receiver 30 shows a numeric representation of the estimated glucose value on its user interface, which is described in more detail elsewhere herein.
- Output can be continuously provided, or certain output can be selectively provided based on events, analyte concentrations and the like.
- an estimated analyte path can be continuously provided to a patient on an LCD screen, while audible alerts can be provided only during a time of existing or approaching clinical risk to a patient.
- estimation can be provided based on event triggers (for example, when an analyte concentration is nearing or entering a clinically risky zone),
- analyzed deviation of estimated analyte values can be provided when a predetermined level of variation (for example, due to known error or clinical risk) is known.
- clinical risk alarms can be activated for a predetermined time period to allow for the user to attend to his/her condition. Additionally, the clinical risk alarms can be de-activated when leaving a clinical risk zone so as not to annoy the patient by repeated clinical risk alarms, when the patient's condition is improving.
- the screen shows thresholds, including a high threshold 200 and a low threshold 202, which represent boundaries between clinically safe and clinically risky conditions for the patients.
- a normal glucose threshold for a glucose sensor is set between about 100 and 160 mg/dL, and the clinical risk zones 204 are illustrated outside of these thresholds.
- the normal glucose threshold is between about 80 and about 200 mg/dL, between about 55 and about 220 rag/dL, or other threshold that can be set by the manufacturer, physician, patient, computer program, and the like.
- glucose thresholds are given for a glucose sensor, the setting of any analyte threshold is not limited by the preferred embodiments.
- a gradient bar 210 is provided that includes thresholds 212 set at high and lows such as described in more detail with reference to Fig. 4B, above. Additionally, colors, shading, or other graphical illustration can be present to represent danger zones 214 on the gradient bar 210 such as described in more detail with reference to Fig. 4B, above.
- the representative gradient bar communicates the directional trend of the analyte concentration to the user in a simple and effective manner, namely by a directional arrow 218.
- a person with diabetes obtains a blood sample and measures the glucose concentration using a test strip, and the like.
- the gradient bar can be a vertical instead of horizontal bar.
- a gradient fill can be used to represent analyte concentration, variation, or clinical risk, for example.
- the bar graph includes color, for example the center can be green in the safe zone that graduates to red in the danger zones; this can be in addition to or in place of the divided segments.
- the segments of the bar graph are clearly divided by lines; however color, gradation, and the like can be used to represent areas of the bar graph.
- the directional arrow can be represented by a cascading level of arrows to a represent slow or rapid rate of change. In some embodiments, the directional arrow can be flashing to represent movement or pending danger.
- a sensor data receiving module also referred to as the sensor data module, or processor module, receives sensor data (e.g., a data stream), including one or more time-spaced sensor data points hereinafter referred to as "data stream,” “sensor data,” “sensor analyte data”, “glucose signal,” from a sensor via the receiver, which can be in wired or wireless communication with the sensor.
- the sensor data can be raw or smoothed (filtered), or include both raw and smoothed data.
- raw sensor data may include an integrated digital data value, e.g., a value averaged over a time period such as by a charge capacitor.
- a reference data receiving module receives reference data from a reference glucose monitor, including one or more reference data points.
- the reference glucose points can comprise results from a self-monitored blood glucose test (e.g., from a finger stick test).
- the user can administer a self-monitored blood glucose test to obtain a glucose value (e.g., point) using any known glucose sensor, and enter the numeric glucose value into the computer system,
- a self-monitored blood glucose test comprises a wired or wireless connection to the receiver 30 (e.g.
- the self-monitored glucose test is integral with the receiver 30 so that the user simply provides a blood sample to the receiver 30, and the receiver 30 runs the glucose test to determine a reference glucose value.
- the processor module is configured to form a matching data pair only if a signal artifact is not detected. In some embodiments wherein the data signal is evaluated for signal artifacts, the processor module is configured to prompt a user for a reference glucose value during a time when one or more signal artifact(s) is not detected.
- the data pairs are selected only within a certain glucose value threshold, for example wherein the reference glucose value is between about 40 and about 400 mg/dL.
- the data pairs that form the initial calibration set are selected according to their time stamp. In certain embodiments, the data pairs that form the initial calibration set are selected according to their time stamp, for example, by waiting a predetermined "break-in" time period after implantation, the stability of the sensor data can be increased. In certain embodiments, the data pairs that form the initial calibration set are spread out over a predetermined time period, for example, a period of two hours or more. In certain embodiments, the data pairs that form the initial calibration set are spread out over a predetermined glucose range, for example, spread out over a range of at least 90 mg/dL or more.
- the conversion function module also referred to as the processor module, uses the calibration set to create a conversion function.
- the conversion function substantially defines the relationship between the reference glucose data and the glucose sensor data.
- a variety of known methods can be used with the preferred embodiments to create the conversion function from the calibration set.
- a linear least squares regression is performed on the initial calibration set such as described in more detail with reference to Fig. 6.
- an output module also referred to as the processor module, provides output to the user via the user interface.
- the output is representative of the estimated glucose value, which is determined by converting the sensor data into a meaningful glucose value such as described in more detail with reference to block 56, above.
- User output can be in the form of a numeric estimated glucose value, an indication of directional trend of glucose concentration, and/or a graphical representation of the estimated glucose data over a period of time, for example.
- Other representations of the estimated glucose values are also possible, for example audio and tactile.
- the conversion function can be adapted to compensate for the sensitivity differences in blood sugar level.
- the conversion function comprises two different regression lines, wherein a first regression line is applied when the estimated blood glucose concentration is at or below a certain threshold (e.g., 150 mg/dL) and a second regression line is applied when the estimated blood glucose concentration is at or above a certain threshold (e.g., 150 mg/dL).
- a predetermined pivot of the regression line that forms the conversion function can be applied when the estimated blood is above or below a set threshold (e.g., 150 mg/dL), wherein the pivot and threshold are determined from a retrospective analysis of the performance of a conversion function and its performance at a range of glucose concentrations.
- the regression line that forms the conversion function is pivoted about a point in order to comply with clinical acceptability standards (e.g., Clarke Error Grid, Consensus Grid, mean absolute relative difference, or other clinical cost function).
- clinical acceptability standards e.g., Clarke Error Grid, Consensus Grid, mean absolute relative difference, or other clinical cost function.
- the sensor calculates a trimmed mean (e.g., removes highest and lowest measurements from a data set) and then regresses the remaining measurements to estimate the glucose value.
- the total signal can be filtered using a non-recursive filter, such as a finite impulse response (FIR) filter.
- FIR finite impulse response
- An FIR filter is a digital signal filter, in which every sample of output is the weighted sum of past and current samples of input, using only some finite number of past samples.
- the total signal can be filtered using a recursive filter, such as an infinite impulse response (IIR) filter.
- IIR infinite impulse response
- the constant noise signal component 1030 can be obtained by calibrating the sensor signal using reference data, such as one or more blood glucose values obtained from a handheld blood glucose meter, from which the baseline "b" of a regression can be obtained, representing the constant noise signal component 1030.
- the analyte signal component 1040 can be obtained by subtracting the constant noise signal component 1030 from the filtered signal 1020.
- Noise is clinically important because it can induce error and can reduce sensor performance, such as by providing a signal that causes the analyte concentration to appear higher or lower than the actual analyte concentration.
- upward or high noise e.g., noise that causes the signal to increase
- downward or low noise e.g., noise that causes the signal to decrease
- Some known interfering species for a glucose sensor include but are not limited to acetaminophen, ascorbic acid, bilirubin, cholesterol, creatinine, dopamine, ephedrine, ibuprofen, L-dopa, methyldopa, salicylate, tetracycline, tolazamide, tolbutamide, triglycerides, and uric acid.
- a signal component's percentage of the total signal is determined using one or more of a variety of methods of quantifying an amplitude of signal components and total signal, from which each components percent contribution can be calculated, as is appreciated by one skilled in the art.
- the signal component(s) can be quantified by comparing the peak-to-peak amplitudes of each signal component for a time period, whereby the peak-to-peak amplitudes of each component can be compared to the peak-to-peak amplitude of the total signal to determine it's percentage of the total signal, as is appreciated by one skilled in the art.
- the signal component(s) can be quantified by determining the Root Mean Square (RMS) of the signal component for a time period.
- RMS Root Mean Square
- the signal component(s) can be quantified using the formula: wherein there are a number ( «) of data values (x) for a signal (e.g., analyte component, non- constant noise component, constant noise component, and/or total signal) during a predetermined time period (e.g., about 1 day, about 2 days, about 3 days, etc).
- a signal e.g., analyte component, non- constant noise component, constant noise component, and/or total signal
- a predetermined time period e.g., about 1 day, about 2 days, about 3 days, etc.
- the signal components can be compared to the total signal to determine a percentage of each signal component within the total signal.
- a glucose sensor produces a data stream that is indicative of the glucose concentration of a host, such as described in more detail above.
- signal artifacts also referred to as “signal artifact events” or “noise episodes” for example, such as shown in sections 74a, 74b of the data stream of Figs. 7B and 7C, respectively, illustrate time periods during which "signal artifacts” can be seen, which are significantly different from the previously described system noise (sections 72a, 72b).
- skin temperature can vary dramatically, which can result in thermally related erosion of the signal (e.g., temperature changes between 32 and 39 degrees Celsius have been measured in humans).
- increased impedance can result from the sensor resting against wall of the blood vessel, for example, producing this non-glucose reaction rate-limiting noise due to oxygen deficiency.
- a sensor data receiving module also referred to as the sensor data module 82, or processor module, receives sensor data (e.g., a data stream), including one or more time-spaced sensor data points.
- the data stream is stored in the sensor for additional processing; in some alternative embodiments, the sensor periodically transmits the data stream to the receiver 30, which can be in wired or wireless communication with the sensor.
- raw and/or filtered data is stored in the sensor and/or receiver.
- the processor module in either the sensor electronics and/or the receiver electronics can evaluate an intermittent or continuous signal-to-noise measurement to determine aberrancy of sensor data responsive to a signal-to-noise ratio above a set threshold.
- signal residuals e.g., by comparing raw and filtered data
- pattern recognition can be used to identify noise associated with physiological conditions, such as low oxygen, or other known signal aberrancies.
- the system can be configured, in response to aberrancies in the data stream, to trigger signal estimation, adaptively filter the data stream according to the aberrancy, and the like, as described in more detail elsewhere herein.
- the signal artifacts replacement module also referred to as the signal estimation module, replaces some or an entire data stream with estimated glucose signal values using signal estimation. Numerous embodiments for performing signal estimation are described in more detail in the section entitled "Signal Artifacts Replacement,” all of which are encompassed by the signal artifacts replacement module, block 86. It is noted that in some embodiments, signal estimation/replacement is initiated in response to positive detection of signal artifacts on the data stream, and subsequently stopped in response to detection of negligible signal artifacts on the data stream.
- signal estimation can additionally include discarding data that is considered sufficiently unreliable and/or erroneous such that the data should not be used in a signal estimation algorithm.
- the system can be programmed to discard outlier data points, for example data points that are so extreme that they can skew the data even with the most comprehensive filtering or signal estimation, and optionally replace those points with a projected value based on historical data or present data (e.g., linear regression, recursive filtering, and the like).
- discarding sensor data includes discarding sensor data that falls outside of a "Cone of Possibility" such as described in more detail elsewhere herein.
- Oxygen sensors are useful for a variety of purposes.
- U.S. Patent No. 6,512,939 to Colvin et at which is incorporated herein by reference, discloses an oxygen sensor that measures background oxygen levels.
- Colvin et at rely on the oxygen sensor for the data stream of glucose measurements by subtraction of oxygen remaining after exhaustion of glucose by an enzymatic reaction from total unreacted oxygen concentration.
- Fig. 9 is a graph that shows a comparison of sensor current and counter-electrode voltage in a host over time.
- the x-axis represents time in minutes.
- the first y-axis 91 represents sensor counts from the working electrode and thus plots a raw sensor data stream 92 for the glucose sensor over a period of time.
- the second y-axis 93 represents counter- electrode voltage 94 in counts.
- the graph illustrates the correlation between sensor data 92 and counter-electrode voltage 94; particularly, that erroneous counter electrode function 96 where the counter voltages drops approximately to zero substantially coincides with transient non-glucose related signal artifacts 98. In other words, when counter-electrode voltage is at or near zero, sensor data includes signal artifacts.
- more frequently sampled data e.g., 30-second
- less frequent data e.g., 5-minute
- the processor performs a linear regression.
- the processor performs a linear regression analysis of the n (e.g., 10) most recent sampled sensor values to smooth out the noise.
- a linear regression averages over a number of points in the time course and thus reduces the influence of wide excursions of any point from the regression line.
- Linear regression defines a slope and intercept, which is used to generate a "Projected Glucose Value," which can be used to replace sensor data. This regression can be continually performed on the data stream or continually performed only during the transient signal artifacts.
- signal estimation can include non-linear regression.
- the processor performs a trimmed regression, which is a linear regression of a trimmed mean (e.g., after rejecting wide excursions of any point from the regression line).
- a trimmed mean e.g., removes highest and lowest measurements from a data set and then regresses the remaining measurements to estimate the glucose value.
- the processor runs a non- recursive filter, such as a finite impulse response (FIR) filter.
- FIR filter is a digital signal filter, in which every sample of output is the weighted sum of past and current samples of input, using only some finite number of past samples.
- Fig. 14 is a graph that illustrates a raw data stream from a glucose sensor and an IIR- estimated signal that can be used to replace some of or the entire data stream.
- the x-axis represents time in minutes; the y-axis represents sensor data output in counts.
- a raw data signal 140 which is illustrated as a dotted line, shows a data stream wherein some system noise can be detected, however signal artifacts 142 can be particularly seen in a portion thereof (and can be detected by methods such as described above).
- the IIR-estimated signal 144 which is illustrated as a solid line, represents the data stream after signal estimation using an HR filter, such as described above, and appears at least somewhat "smoothed" on the graph. It is noted that the IIR-estimated signal induces a time lag on the data stream; however it appears to be a particularly good estimate of glucose data values during signal artifacts 142, as compared to the FIR filter (Fig. 13), for example.
- an acceptance filter can also be applied to new sensor data to minimize effects of high non-physiological data.
- each sampled data point e.g., every 30 seconds
- Each new point is compared against the most representative estimate of the sensor curve at the previous sampling interface (e.g., 30-second time point), or at a projection to a current estimated value.
- the current data point is compared to the most recent value of the average maximum values over a time period (e.g., 5 sampled data points over a 2.5 minute period).
- the ratio of current value to the comparison value is greater than a certain threshold (e.g., about 1.02), then the current data point is replaced with a previously accepted value (e.g., 30-second value). If the ratio of current value to the comparison value is in at or within a certain range (e.g., about 1.02 to 0.90), then the current data point is accepted. If the ratio of current value to the comparison value is less than a certain threshold (e.g., about 0.90), then the current data point is replaced with a previously accepted value.
- the acceptance filter step and max-average calculation are continuously run throughout the data set (e.g., fixed 5-minute windows) on a rolling window basis (e.g., every 30 seconds).
- the processor runs a "Cone of Possibility Replacement Method.” It is noted that this method can be performed in the sensor and/or in the receiver.
- the Cone of Possibility Detection Method utilizes physiological information along with glucose signal values in order define a "cone" of physiologically feasible glucose signal values within a human. Particularly, physiological information depends upon the physiological parameters obtained from continuous studies in the literature as well as our own observations.
- a first physiological parameter uses a maximal sustained rate of change of glucose in humans (e.g., about 4 to 5 mg/dl/min) and a maximum sustained acceleration of that rate of change (e.g., about 0.1 to 0.2 mg/min/min).
- the Cone of Possibility Replacement Method utilizes physiological information along with blood glucose data in order to improve the estimation of blood glucose values within a human in an embodiment of Signal Artifacts Replacement.
- the Cone of Possibility Replacement Method can be performed on raw data in the sensor, on raw data in the receiver, or on smoothed data (e.g., data that has been replaced/estimated in the sensor or receiver by one of the methods described above) in the receiver.
- smoothed data e.g., data that has been replaced/estimated in the sensor or receiver by one of the methods described above
- a centerline of the cone can be projected from a number of previous, optionally smoothed, incremental data points (e.g., previous four, 5-minute data points).
- Each predicted cone centerline point (e.g., 5 minute point) increases by the slope (S) (e.g., for the regression in counts/minute) multiplied by the data point increment (e.g., 5 minutes).
- Counts/mg/dL is estimated from glucose and sensor range calculation over the data set
- the cone widens for each 5 -minute increment for which a sensor value fails to fall inside the cone up to 30 minutes, such as can be seen in the table above. At 30 minutes, a cone has likely widened enough to capture an observed sensor value, which is used, and the cone collapses back to a 5 -minute increment width. If no sensor values are captured within 30 minutes, the cone generation routine starts over using the next four observed points. In some implementations special rules can be applied, for example in a case where the change in counts in one 5 -minute interval exceeds an estimated 30-mg/dL amount. In this case, the next acceptable point can be more than 20 to 30 minutes later. It is noted that an implementation of this algorithm includes utilizing the cone of possibility to predict glucose levels and alert patients to present or upcoming dangerous blood glucose levels.
- the cone can widen hi set multiples (e.g., 20 mg/dL) of equivalent amounts for each additional time interval (e.g., 5 minutes), which rapidly widens the cone to accept data.
- additional time interval e.g., 5 minutes
- sensor calibration data is optimized using the Clarke Error Grid, the Consensus Grid, or an alternative error assessment that assigns risk levels based on the accuracy of matched data parrs.
- the regression of counts vs. reference glucose can be adjusted such that the maximum number of points are in the A+B zones without reducing the A+B percentage, and the number of points are optimized such that the highest percentage are in the A zone and lowest percentage are in the D, E and C zones.
- three distinct lines optimized for clinical acceptability can represent the regression line.
- an additional useful criterion can be used to compute the root mean squared percentage bias for the data set. Better fits are characterized by reduction in the total root mean squared percentage bias.
- a predetermined pivot (e.g., 10 degree) of the regression line can be applied when the estimated blood is above or below a set threshold (e.g., 150 mg/dL), wherein the pivot and threshold are determined from a retrospective analysis of the performance of a conversion function and its performance at a range of glucose concentrations.
- a set threshold e.g. 150 mg/dL
- reference changes in electrode potential can be used to estimate glucose sensor data during positive detection of signal artifacts from an electrochemical glucose sensor, the method hereinafter referred to as reference drift replacement.
- the electrochemical glucose sensor comprises working, counter, and reference electrodes, such as described with reference to Figs. I 5 2 and 10 above. This method exploits the function of the reference electrode as it drifts to compensate for counter electrode limitations during oxygen deficits, pH changes, and/or temperature changes such as described in more detail above with reference to Figs. 1OA, 1OB, and 1OC.
- a potentiostat is generally designed so that a regulated potential difference between the reference electrode 102 and working electrode 100 is maintained as a constant
- the potentiostat allows the counter electrode voltage to float within a certain voltage range (e.g., from between close to the + 1.2V observed for the working electrode to as low as battery ground or 0.0 V).
- the counter electrode voltage measurement will reside within this voltage range dependent on the magnitude and sign of current being measured at the working electrode and the electroactive species type and concentration available in the electrolyte adjacent to the counter electrode 104, This species will be electrochemically recruited (e.g., reduced/accepting electrons) to equal the current of opposite sign (e.g., oxidized/donating electrons) occurring at the working electrode 100. It has been discovered that the reduction of dissolved oxygen or hydrogen peroxide from oxygen converted in the enzyme layer are the primary species reacting at the counter electrode to provide this electronic current balance in this embodiment.
- the counter electrode can be driven in its electrochemical search for electrons all the way to ground or 0.0V.
- the working and counter electrode currents must still maintain substantially equivalent currents. Therefore, the reference electrode 102 will drift upward creating new oxidizing and reducing potentials that maintain equal currents at the working and counter electrodes.
- the reference electrode 102 can be monitored to determine the severity of the signal artifacts on the data stream. Particularly, a substantially direct relationship between the reference electrode drift and signal artifacts has been discovered.
- the measured glucose signal I SE N SE
- the circuit described with reference to Fig. 1 OA can be used to determine the CV curve on a regularly scheduled basis or as needed.
- the desired reference voltage and applied potential are made variable, and the reference voltage can be changed at a defined rate while measuring the signal strength from the working electrode, which allows for generation of a CV curve while a sensor is in vivo.
- prediction algorithms can be used to replace glucose data signals for data which does not exist because 1) it has been discarded, 2) it is missing due to signal transmission errors and the like, or 3) it represents a time period (e.g., future) for which a data stream has not yet been obtained based on historic and/or present data.
- Prediction/projection algorithms include any of the above described Signal Artifacts Replacement algorithms, and differ only in the fact that they are implemented to replace time points/periods during which no data is available (e.g., for the above-described reasons), rather than including that existing data, within the algorithmic computation.
- signal replacement/estimation algorithms are used to predict where the glucose signal should be, and if the actual data stream varies beyond a certain threshold of that projected value, then signal artifacts are detected.
- other data processing can be applied alone, or in combination with the above- described methods, to replace data signals during system noise and/or signal artifacts.
- Fig. 15 is a flow chart that illustrates a process of selectively applying signal estimation in embodiments.
- a sensor data receiving module also referred to as the sensor data module, receives sensor data (e.g., a data stream), including one or more time-spaced sensor data points, such as described in more detail with reference to block 82 in Fig. 8.
- the signal artifacts detection module evaluates the amplitude and/or frequency of the transient non-glucose related signal artifacts, which amplitude and/or frequency can be used to define the severity in terms of a threshold (e.g., high or low) or a numeric representation (e.g., a value from 1 to 10).
- a threshold e.g., high or low
- a numeric representation e.g., a value from 1 to 10
- the signal artifacts detection module evaluates a duration of the transient non- glucose related signal artifacts, such that as the duration increases, a severity can be defined in terms of a threshold (e.g., short or long) or a numeric representation (e.g., 10, 20, 30, 40, 50, or 60 minutes).
- the signal artifacts detection module evaluates the frequency content from a Fourier Transform and defines severity in terms of a threshold (e.g., above or below 30 cycles per hour) or a numeric representation (e.g., 50 cycles per hour). All of the signal artifacts detection methods described herein can be implemented to include determining a severity of the signal artifacts, threshold, and/or numerical representations.
- signal artifacts replacement is normally turned off, except during detected signal artifacts.
- a first signal estimation algorithm e.g., linear regression, FIR filter etc.
- a second signal estimation algorithm optimized for signal artifacts e.g., IIR filter, Cone of Possibility Replacement Method, etc.
- the signal replacement module comprises programming to selectively switch on and off a plurality of distinct signal estimation algorithms based on the severity of the detected signal artifacts.
- the severity of the signal artifacts can be defined as high and low.
- a first filter e.g., trimmed regression, linear regression, FIR, Reference Electrode Method, etc.
- a second filter e.g., IIR, Cone of Possibility Method, etc.
- all of the above signal replacement algorithms can be selectively applied in this manner based on the severity of the detected signal artifacts.
- Fig. 16 is a graph that illustrates an embodiment wherein the signal replacement module comprises programming to selectively switch on and off a signal artifacts replacement algorithm responsive to detection of signal artifacts.
- the x-axis represents time in minutes; the first y-axis 160 represents sensor data output in counts.
- the second y-axis 164 represents counter-electrode voltage in counts; counter electrode voltage data 165 is illustrated as a solid line.
- the signal replacement module comprises programming to selectively apply different parameters to a single signal artifacts replacement algorithm (e.g., IIR, Cone of Possibility Replacement Method, etc.).
- a single signal artifacts replacement algorithm e.g., IIR, Cone of Possibility Replacement Method, etc.
- the parameters of an algorithm can be switched according to signal artifacts detection; in such an example, an IIR filter with a 30-minute cycle length can be used during times of no noise or system noise and a 60-minute cycle length can be used during signal artifacts.
- the severity of the signal artifacts can be defined as short and long; in such an example, an IIR filter with a 30-minute cycle length can be used during the short signal artifacts and a 60- minute cycle length can be used during long signal artifacts.
- a first order regression can be selected when the reference glucose data is within a certain threshold (for example, 100 to 200 mg/dL), indicating correlation with a straight line
- a second order regression can be selected when the reference glucose data is outside of a certain threshold (for example, 100 to 200 mg/dL), indicating correlation with a curved line because the likelihood of the glucose concentration turning around (for example, having a curvature) is greatest at high and low values.
- the algorithm(s) selected from the evaluation step is employed to estimate analyte values for a time period. Accordingly, analyte values are more dynamically and intelligently estimated to accommodate the dynamic nature of physiological data. Additional processes, for example applying physiological boundaries, evaluation of the estimation algorithms after employing the algorithms, evaluating a variation of estimated analyte values, measuring and comparing analyte values, and the like (e.g., such as described in U.S. Patent Publication No. US-2005-0203360-A1) can be applied to the dynamic and intelligent estimative algorithms described herein
- the variation of possible estimated analyte values analysis process 450 obtains sensor data, which can be raw, smoothed, calibrated and/or otherwise processed.
- Pattern recognition algorithms that can be used in this embodiment include, but are not limited to, stochastic nonlinear time-series analysis, exponential (non-linear) autoregressive model, process feedback nonlinear autoregressive (PFNAR) model, neural networks, and the like.
- statistically calculated patterns can provide information useful in analyzing a variation of estimated analyte values for a patient that includes consideration of the patient's normal physiological patterns.
- Pattern recognition enables the algorithmic analysis of analyte data to be customized to a user, which is useful when analyte information is variable with each individual user, such as has been seen in glucose in humans, for example.
- the variation of possible estimated analyte values evaluation process 150 provides output based on the variation analysis.
- the result of this variation analysis provides a "zone" of possible values, which can be displayed to the user, considered in data analysis, and/or used in evaluating of performance of the estimation, for example.
- Fig. 25 is a flow chart that illustrates the process 480 of estimating, measuring, and comparing analyte values in one embodiment.
- the estimating, measuring, and comparing analyte values process 480 estimates one or more analyte values for a time period.
- this analyte values estimation uses conventional projection using first or second order regression, for example.
- dynamically and intelligently selecting of one or more algorithms from a plurality of algorithms, dynamically and intelligently estimating analyte values within physiological boundaries), dynamic and intelligent estimation and evaluation of estimated analyte values, variation analysis (e.g., such as described in U.S. Patent Publication No. US-2005-0203360-A1), and the like can be applied to the process described herein.
- the estimating, measuring, and comparing analyte values process 480 obtains sensor data for the time period for which the estimated analyte values were calculated at block 484.
- the measured analyte data can be raw, smoothed, calibrated, and/or otherwise processed.
- a resulting deviation from this comparison between estimated and corresponding measured analyte values may or may not imply error in the estimative algorithms. While not wishing to be bound by theory, it is believed that the deviation between estimated and corresponding measured analyte values is due, at least in part, to behavioral changes by a patient, who observes estimated analyte values and determines to change the present trend of analyte values by behavioral and/or therapeutic changes (for example, medication, carbohydrate consumption, exercise, rest, and the like). Accordingly, the deviation can also be used to illustrate positive changes resulting from the educational aspect of providing estimated analyte values to the user, for example.
- Fig. 27 provides a flow chart 520 that illustrates the evaluation of reference and/or sensor data for statistical, clinical, and/or physiological acceptability in one embodiment. Although some acceptability tests are disclosed herein, any known statistical, clinical, physiological standards and methodologies can be applied to evaluate the acceptability of reference and sensor analyte data.
- the reference analyte data is evaluated with respect to substantially time corresponding converted sensor data to determine the acceptability of the matched pair.
- clinical acceptability considers a deviation between time corresponding analyte measurements (for example, data from a glucose sensor and data from a reference glucose monitor) and the risk (for example, to the decision making of a person with diabetes) associated with that deviation based on the glucose value indicated by the sensor and/or reference data. Evaluating the clinical acceptability of reference and sensor analyte data, and controlling the user interface dependent thereon, can minimize clinical risk.
- the receiver evaluates clinical acceptability each time reference data is obtained.
- the sensor data receiving module receives substantially continuous sensor data (e.g., a data stream) via a receiver and converts that data into estimated analyte values.
- substantially continuous is a broad term and is used in its ordinary sense, without limitation, to refer to a data stream of individual measurements taken at time intervals (e.g., time-spaced) ranging from fractions of a second up to, e.g., 1, 2, or 5 minutes or more.
- time intervals e.g., time-spaced
- sensor data can be occasionally recalibrated in response to changes in sensor sensitivity (drift), for example.
- Initial calibration and re-calibration of the sensor require a reference analyte value. Accordingly, the receiver can receive reference analyte data at any time for appropriate processing.
- the reference data receiving module receives reference analyte data from a reference analyte monitor.
- the reference data comprises one analyte value obtained from a reference monitor.
- the reference data includes a set of analyte values entered by a user into the interface and averaged by known methods, such as are described elsewhere herein.
- the reference data comprises a plurality of analyte values obtained from another continuous analyte sensor.
- the reference data can be pre-screened according to environmental and physiological issues, such as time of day, oxygen concentration, postural effects, and patient-entered environmental data.
- the sensor comprises an implantable glucose sensor
- an oxygen sensor within the glucose sensor is used to determine if sufficient oxygen is being provided to successfully complete the necessary enzyme and electrochemical reactions for accurate glucose sensing.
- the patient is prompted to enter data into the user interface, such as meal times and/or amount of exercise, which can be used to determine likelihood of acceptable reference data.
- the reference data is matched with time-corresponding sensor data, which is then evaluated on a modified clinical error grid to determine its clinical acceptability.
- Some evaluation data can be used to evaluate an optimum time for reference analyte measurement.
- the user interface can then prompt the user to provide a reference data point for calibration within a given time period. Consequently, because the receiver proactively prompts the user during optimum calibration times, the likelihood of error due to environmental and physiological limitations can decrease and consistency and acceptability of the calibration can increase.
- results of the evaluation are assessed. If acceptability is determined, then processing continues to block 528 to re-calculate the conversion function using the new matched data pair in the calibration set.
- the sensor data transformation module uses the new conversion function (from block 528) to continually (or intermittently) convert sensor data into estimated analyte values, also referred to as calibrated data, or converted sensor data, such as is described in more detail above.
- the processing progresses to block 534 to adjust the calibration set.
- the conversion function module removes one or more oldest matched data pair(s) and recalculates the conversion function accordingly.
- the conversion function module removes the least concordant matched data pair from the calibration set, and recalculates the conversion function accordingly.
- the conversion function module re-creates the conversion function using the adjusted calibration set.
- the processing loops back to block 534 to adjust the calibration set once again.
- This process can continue until the calibration set is no longer sufficient for calibration, for example, when the calibration set includes only one or no matched data pairs with which to create a conversion function. In this situation, the system can return to the initial calibration or start-up mode, which is described in more detail with reference to Figs. 16 and 19, for example. Alternatively, the process can continue until inappropriate matched data pairs have been sufficiently purged and acceptability is positively determined.
- Another criterion comprises ensuring all raw sensor data, estimated sensor analyte values, and/or reference analyte values are within a predetermined range (e.g., 40 mg/dL to 400 mg/dL for glucose values).
- Another criterion comprises evaluating the rate of change of the analyte concentration (e.g., from sensor data) during the time stamp of the matched pair(s). For example, sensor and reference data obtained during the time when the analyte concentration is undergoing a slow rate of change can be less susceptible to inaccuracies caused by time lag and other physiological and non-physiological effects.
- the estimated glucose value is represented by a numeric value.
- the user interface graphically represents the estimated glucose data trend over predetermined a time period (e.g., one, three, and nine hours, respectively).
- a time period e.g., one, three, and nine hours, respectively.
- other time periods can be represented.
- the measured analyte value is represented by a numeric value.
- other time periods can be represented.
- pictures, animation, charts, graphs, ranges of values, and numeric data can be selectively displayed.
- the processor module is configured to filter the sensor data and to display a graphical representation of the filtered sensor data responsive to a determination of a start of a signal artifact event. In some embodiments, the processor module is configured to display a graphical representation of unfiltered data (e.g., raw data) responsive to a determination of an end of a signal artifact event. In some embodiments, the processor module is configured to display a graphical representation of unfiltered data except when a signal artifact event is determined. It has been found that adaptive filtering as described herein, including selective filtering during signal artifact events, increases accuracy of displayed data, decreases display of noisy data, and/or reduces data gaps and/or early shut off as compared to conventional sensors.
Abstract
Description
Claims
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AU2008266162A AU2008266162A1 (en) | 2007-06-13 | 2008-06-11 | Systems and methods for replacing signal artifacts in a glucose sensor data stream |
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