Source: http://www.google.com/patents/US20080033254?dq=5958006
Timestamp: 2015-11-30 00:28:43
Document Index: 658737688

Matched Legal Cases: ['Application No. 60', 'Application No. 60', 'Application No. 60', 'Application No. 60', 'Application No. 60', 'Application No. 60', 'Application No. 60', 'Application No. 60', 'Application No. 60', 'Application No. 60', 'Application No. 60']

Patent US20080033254 - Systems and methods for replacing signal data artifacts in a glucose sensor ... - Google PatentsSearch Images Maps Play YouTube News Gmail Drive More »Sign inAdvanced Patent SearchPatentsSystems and methods for detecting noise episodes and processing analyte sensor data responsive thereto. In some embodiments, processing analyte sensor data includes filtering the sensor data to reduce or eliminate the effects of the noise episode on the signal....http://www.google.com/patents/US20080033254?utm_source=gb-gplus-sharePatent US20080033254 - Systems and methods for replacing signal data artifacts in a glucose sensor data streamAdvanced Patent SearchPublication numberUS20080033254 A1Publication typeApplicationApplication numberUS 11/762,638Publication dateFeb 7, 2008Filing dateJun 13, 2007Priority dateJul 25, 2003Also published asCA2687980A1, EP2155045A1, EP2155045A4, US8260393, US20100179409, US20150289819, WO2008157187A1Publication number11762638, 762638, US 2008/0033254 A1, US 2008/033254 A1, US 20080033254 A1, US 20080033254A1, US 2008033254 A1, US 2008033254A1, US-A1-20080033254, US-A1-2008033254, US2008/0033254A1, US2008/033254A1, US20080033254 A1, US20080033254A1, US2008033254 A1, US2008033254A1InventorsApurv Kamath, Aarthi Mahalingam, Ying Li, Mohammad Shariati, James Brauker, Mark Brister, Robert BoockOriginal AssigneeDexcom, Inc.Export CitationBiBTeX, EndNote, RefManPatent Citations (99), Referenced by (515), Classifications (23), Legal Events (2) External Links: USPTO, USPTO Assignment, EspacenetSystems and methods for replacing signal data artifacts in a glucose sensor data stream
US 20080033254 A1Abstract
Systems and methods for detecting noise episodes and processing analyte sensor data responsive thereto. In some embodiments, processing analyte sensor data includes filtering the sensor data to reduce or eliminate the effects of the noise episode on the signal. Images(39) Claims(35)
CROSS-REFERENCE TO RELATED APPLICATIONS [0001] This application is a continuation in part of U.S. patent application Ser. No. 11/498,410, filed Aug. 2, 2006, which is a continuation-in-part of U.S. patent application Ser. No. 10/648,849, filed Aug. 22, 2003. U.S. patent application Ser. No. 11/498,410 is a continuation-in-part of U.S. patent application Ser. No. 11/007,920, filed Dec. 8, 2004, which claims the benefit of U.S. Provisional Application No. 60/528,382 filed Dec. 9, 2003. U.S. patent application Ser. No. 11/498,410 is a continuation-in-part of U.S. patent application Ser. No. 11/077,739, filed Mar. 10, 2005, which claims the benefit of U.S. Provisional Application No. 60/587,787 filed Jul. 13, 2004; U.S. Provisional Application No. 60/587,800 filed Jul. 13, 2004; U.S. Provisional Application No. 60/614,683 filed Sep. 30, 2004; and U.S. Provisional Application No. 60/614,764 filed Sep. 30, 2004. This application is also a continuation-in-part of U.S. patent application Ser. No. 11/750,907 filed May 18, 2007, which is a continuation-in-part of U.S. patent application Ser. No. 11/675,063, filed Feb. 14, 2007, which is a continuation-in-part of U.S. patent application Ser. No. 11/503,367, filed Aug. 10, 2006, which is a continuation-in-part of U.S. patent application Ser. No. 11/439,630, filed May 23, 2006, which is a continuation-in-part of U.S. patent application Ser. No. 11/077,715, filed Mar. 10, 2005, which claims the benefit of U.S. Provisional Application No. 60/587,787 filed Jul. 13, 2004; U.S. Provisional Application No. 60/587,800 filed Jul. 13, 2004; U.S. Provisional Application No. 60/614,683 filed Sep. 30, 2004; and U.S. Provisional Application No. 60/614,764 filed Sep. 30, 2004. U.S. patent application Ser. No. 11/439,630 claims the benefit of U.S. Provisional Application No. 60/683,923 filed May 23, 2005. U.S. patent application Ser. No. 11/675,063 is a continuation-in-part of U.S. patent application Ser. No. 11/404,417, filed Apr. 14, 2006. U.S. patent application Ser. No. 11/675,063 is a continuation-in-part of U.S. patent application Ser. No. 10/896,639, filed Jul. 21, 2004, which claims the benefit of U.S. Provisional Application No. 60/490,009, filed Jul. 25, 2003. U.S. patent application Ser. No. 11/750,907 is a continuation-in-part of U.S. patent application Ser. No. 11/404,417, filed Apr. 14, 2006. Each of the aforementioned applications is incorporated by reference herein in its entirety, and each is hereby expressly made a part of this specification.
FIELD OF THE INVENTION [0002] The present invention relates generally to systems and methods for processing data received from a glucose sensor. Particularly, the present invention relates to systems and methods for detecting and processing signal artifacts, including detecting, estimating, predicting, filtering, displaying, and otherwise minimizing the effects of signal artifacts in a glucose sensor data stream. BACKGROUND OF THE INVENTION [0003] Diabetes mellitus is a disorder in which the pancreas cannot create sufficient insulin (Type I or insulin dependent) and/or in which insulin is not effective (Type 2 or non-insulin dependent). In the diabetic state, the victim suffers from high blood sugar, which causes an array of physiological derangements (kidney failure, skin ulcers, or bleeding into the vitreous of the eye) associated with the deterioration of small blood vessels. A hypoglycemic reaction (low blood sugar) is induced by an inadvertent overdose of insulin, or after a normal dose of insulin or glucose-lowering agent accompanied by extraordinary exercise or insufficient food intake. [0004] Conventionally, a diabetic person carries a self-monitoring blood glucose (SMBG) monitor, which typically comprises uncomfortable finger pricking methods. Due to the lack of comfort and convenience, a diabetic will normally only measure his or her glucose level two to four times per day. Unfortunately, these time intervals are so far spread apart that the diabetic will likely find out too late, sometimes incurring dangerous side effects, of a hyperglycemic or hypoglycemic condition. In fact, it is not only unlikely that a diabetic will take a timely SMBG value, but additionally the diabetic will not know if their blood glucose value is going up (higher) or down (lower) based on conventional methods. [0005] Consequently, a variety of transdermal and implantable electrochemical sensors are being developed for continuous detecting and/or quantifying blood glucose values. Many implantable glucose sensors suffer from complications within the body and provide only short-term and less-than-accurate sensing of blood glucose. Similarly, transdermal sensors have run into problems in accurately sensing and reporting back glucose values continuously over extended periods of time. Some efforts have been made to obtain blood glucose data from implantable devices and retrospectively determine blood glucose trends for analysis; however these efforts do not aid the diabetic in determining real-time blood glucose information. Some efforts have also been made to obtain blood glucose data from transdermal devices for prospective data analysis, however similar problems have occurred. [0006] Data streams from glucose sensors are known to have some amount of noise, caused by unwanted electronic and/or diffusion-related system noise that degrades the quality of the data stream. Some attempts have been made in conventional glucose sensors to smooth the raw output data stream representative of the concentration of blood glucose in the sample, for example by smoothing or filtering of Gaussian, white, random, and/or other relatively low amplitude noise in order to improve the signal to noise ratio, and thus data output. SUMMARY OF THE INVENTION [0007] Systems and methods are provided that accurately detect signal noise that is caused by substantially non-glucose reaction rate-limiting phenomena, such as interfering species, ischemia, pH changes, temperature changes, pressure, and stress, for example, which are referred to herein as signal artifacts or “noise episodes”. Detecting signal artifacts and processing the sensor data based on detection of signal artifacts provides accurate estimated glucose measurements to a diabetic patient so that they can proactively care for their condition to safely avoid hyperglycemic and hypoglycemic conditions. [0008] Accordingly, in a first aspect, a method for analyzing data from a continuous analyte sensor is provided, the method comprising receiving data from an analyte sensor; detecting an occurrence of a signal artifact event; and processing the received data, wherein the processing is based at least in part upon whether the signal artifact event occurs. [0009] In an embodiment of the first aspect, detecting an occurrence of a signal artifact event comprises determining an amplitude of sensor data and determining an amplitude of a signal artifact. [0010] In an embodiment of the first aspect, detecting an occurrence of a signal artifact event comprises detecting a start of a signal artifact event when an amplitude of a signal artifact meets a first predetermined condition. [0011] In an embodiment of the first aspect, detecting an occurrence of a signal artifact event comprises detecting an end of a signal artifact event when an amplitude of a signal artifact meets a second predetermined condition. [0012] In an embodiment of the first aspect, the method further comprises filtering the received data to generate filtered data, wherein detecting an occurrence of a signal artifact event comprises comparing received data with filtered data to obtain at least one residual. [0013] In an embodiment of the first aspect, the first predetermined condition is a residual amplitude that is at least about 5% of a sensor data amplitude, and wherein the second predetermined condition is a residual amplitude that is no more than about 5% of a sensor data amplitude. [0014] In an embodiment of the first aspect, 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. [0015] In an embodiment of the first aspect, the first predetermined condition is a differential amplitude that is at least about 5% of a sensor data amplitude, and wherein the second predetermined condition is a differential amplitude that is no more than about 5% of a sensor data amplitude. [0016] In an embodiment of the first aspect, the first predetermined condition is different from the second predetermined condition. [0017] In an embodiment of the first aspect, the method further comprises filtering the received data to generate filtered data, wherein processing the received data comprises displaying a graphical representation of the filtered data responsive to a determination of a start of a signal artifact event. [0018] In an embodiment of the first aspect, the received data comprises an unfiltered signal, and wherein processing the received data comprises displaying a graphical representation of the unfiltered data responsive to a determination of an end of a signal artifact event. [0019] In an embodiment of the first aspect, the received data comprises an unfiltered signal, and wherein processing the received data comprises displaying a graphical representation of the unfiltered data except when a signal artifact event occurs. [0020] In a second aspect, a device is provided 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; and instructions for processing the received data, wherein the processing is based at least in part upon whether a signal artifact event has occurred. [0021] In an embodiment of the second aspect, 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. [0022] In an embodiment of the second aspect, the instructions for detecting an occurrence of a signal artifact event comprise instructions for detecting a start of a signal artifact event when an amplitude of a signal artifact meets a first predetermined condition. [0023] In an embodiment of the second aspect, the instructions for detecting an occurrence of a signal artifact event comprise instructions for detecting an end of a signal artifact event when an amplitude of a signal artifact meets a second predetermined condition. [0024] In an embodiment of the second aspect, the code further comprises instructions for filtering the received data to generate filtered data, wherein 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. [0025] In an embodiment of the second aspect, the first predetermined condition is a residual amplitude that is at least about 5% of a sensor data amplitude, and wherein the second predetermined condition is a residual amplitude that is no more than about 5% of a sensor data amplitude. [0026] In an embodiment of the second aspect, the instructions for detecting an occurrence of a signal artifact event comprise instructions for determining a differential between a first residual at a first time point and a second residual at a second time point. [0027] In an embodiment of the second aspect, the first predetermined condition is a differential amplitude that is at least about 5% of a sensor data amplitude, and wherein the second predetermined condition is a differential amplitude that is no more than about 5% of a sensor data amplitude. [0028] In an embodiment of the second aspect, the first predetermined condition is different from the second predetermined condition. [0029] In an embodiment of the second aspect, the code further comprises instructions for filtering the received data to generate filtered data, wherein the instructions for processing the received data comprise instructions for displaying a graphical representation of filtered data responsive to a determination of a start of a signal artifact event. [0030] In an embodiment of the second aspect, the received data comprises an unfiltered signal, and wherein the instructions for processing the received data comprise instructions for displaying a graphical representation of the unfiltered data responsive to a determination of an end of a signal artifact event. [0031] In an embodiment of the second aspect, the received data comprises an unfiltered signal, and wherein 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. [0032] In a third aspect, a system configured to continuously measure an analyte in a host is provided, the system comprising an analyte sensor configured to provide sensor data indicative of an analyte concentration in a host; electronics operably connected to the sensor and comprising programming configured to detect a signal artifact event, wherein the electronics further comprise programming configured to process the sensor data, wherein the processing is based at least in part upon whether the signal artifact event is detected. [0033] In an embodiment of the third aspect, the programming configured to detect a signal artifact event comprises programming configured to determine an amplitude of the sensor data and programming configured to determine an amplitude of a signal artifact. [0034] In an embodiment of the third aspect, the programming configured to detect a signal artifact event comprises programming configured to detect a start of a signal artifact event when then an amplitude of a signal artifact meets a first predetermined condition. [0035] In an embodiment of the third aspect, the programming configured to detect a signal artifact event comprises programming configured to detect an end of a signal artifact event when an amplitude of a signal artifact meets a second predetermined condition. [0036] In an embodiment of the third aspect, the first predetermined condition is different from the second predetermined condition. [0037] In an embodiment of the third aspect, 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. [0038] In an embodiment of the third aspect, the sensor data comprises unfiltered data, and wherein the programming configured to process the sensor data comprises programming configured to display a graphical representation of the unfiltered data responsive to a determination of an end of a signal artifact event. [0039] In an embodiment of the third aspect, the sensor data comprises unfiltered data, and wherein the programming configured to process the sensor data comprises programming configured to display a graphical representation of the unfiltered data except when a signal artifact event has occurred. [0040] In an embodiment of the third aspect, the sensor is configured to be transcutaneously implanted or intravascularly implanted. [0041] In an embodiment of the third aspect, the sensor has a variable stiffness. [0042] In an embodiment of the third aspect, the sensor further comprises a membrane configured to block passage therethrough of at least one interferent. [0043] In an embodiment of the third aspect, the interferent comprises at least one substance selected from the group consisting of hydrogen peroxide, reactive oxygen species, and reactive nitrogen species. [0044] In an embodiment of the third aspect, the interferent comprises at least one substance selected from the group consisting of acetaminophen, ascorbic acid, dopamine, ibuprofen, salicylic acid, tolbutamide, tetracycline, creatinine, uric acid, ephedrine, L-dopa, methyl dopa, and tolazamide. [0045] In an embodiment of the third aspect, the membrane is configured to substantially block passage therethrough of at least one non-constant noise causing interferent.
BRIEF DESCRIPTION OF THE DRAWINGS [0046] FIG. 1A is an exploded perspective view of a glucose sensor in one embodiment. [0047] FIG. 1B is side view of a distal portion of a transcutaneously inserted sensor in one embodiment. [0048] FIG. 2 is a block diagram that illustrates sensor electronics in one embodiment. [0049] FIGS. 3A to 3D are schematic views of a receiver in first, second, third, and fourth embodiments, respectively. [0050] FIG. 4A is a block diagram of receiver electronics in one embodiment. [0051] FIG. 4B is an illustration of the receiver in one embodiment showing an analyte trend graph, including measured analyte values, estimated analyte values, and a clinical risk zone. [0052] FIG. 4C is an illustration of the receiver in another embodiment showing a representation of analyte concentration and directional trend using a gradient bar. [0053] FIG. 4D is an illustration of the receiver in yet another embodiment, including a screen that shows a numerical representation of the most recent measured analyte value. [0054] FIG. 5 is a flow chart that illustrates the process of calibrating the sensor data in one embodiment. [0055] FIG. 6 is a graph that illustrates a linear regression used to calibrate the sensor data in one embodiment. [0056] FIG. 7A is a graph illustrating the components of a signal measured by a transcutaneous glucose sensor (after sensor break-in was complete), implanted in a non-diabetic, human volunteer host. [0057] FIG. 7B is a graph that shows a raw data stream obtained from a glucose sensor over a 4 hour time span in one example. [0058] FIG. 7C is a graph that shows a raw data stream obtained from a glucose sensor over a 36 hour time span in another example. [0059] FIG. 8 is a flow chart that illustrates the process of detecting and replacing transient non-glucose related signal artifacts in a data stream in one embodiment. [0060] FIG. 9 is a graph that illustrates the correlation between the counter electrode voltage and signal artifacts in a data stream from a glucose sensor in one embodiment. [0061] FIG. 10A is a circuit diagram of a potentiostat that controls a typical three-electrode system in one embodiment. [0062] FIG. 10B is a diagram known as Cyclic-Voltammetry (CV) curve, which illustrates the relationship between applied potential (VBIAS) and signal strength of the working electrode (ISENSE) and can be used to detect signal artifacts. [0063] FIG. 10C is a diagram showing a CV curve that illustrates an alternative embodiment of signal artifacts detection, wherein pH and/or temperature can be monitoring using the CV curve. [0064] FIG. 11 is a graph and spectrogram that illustrate the correlation between high frequency and signal artifacts observed by monitoring the frequency content of a data stream in one embodiment. [0065] FIG. 12 is a graph that illustrates a data stream obtained from a glucose sensor and a signal smoothed by trimmed linear regression that can be used to replace some of or the entire raw data stream in one embodiment. [0066] FIG. 13 is a graph that illustrates a data stream obtained from a glucose sensor and a FIR-smoothed data signal that can be used to replace some of or the entire raw data stream in one embodiment. [0067] FIG. 14 is a graph that illustrates a data stream obtained from a glucose sensor and an IIR-smoothed data signal that can be used to replace some of or the entire raw data stream in one embodiment. [0068] FIG. 15 is a flowchart that illustrates the process of selectively applying signal estimation based on the severity of signal artifacts on a data stream. [0069] FIG. 16 is a graph that illustrates selectively applying a signal estimation algorithm responsive to positive detection of signal artifacts on the raw data stream. [0070] FIG. 17 is a graph that illustrates selectively applying a plurality of signal estimation algorithm factors responsive to a severity of signal artifacts on the raw data stream. [0071] FIG. 18 is a flow chart that illustrates dynamic and intelligent estimation algorithm selection process in an alternative embodiment. [0072] FIG. 19 is a graph that illustrates dynamic and intelligent estimation algorithm selection applied to a data stream in one embodiment. [0073] FIG. 20 is a flow chart that illustrates the process of dynamic and intelligent estimation and evaluation of analyte values in one embodiment. [0074] FIG. 21 is a graph that illustrates an evaluation of the selected estimative algorithm in one embodiment. [0075] FIG. 22 is a flow chart that illustrates the process of analyzing a variation of estimated future analyte value possibilities in one embodiment. [0076] FIG. 23 is a graph that illustrates variation analysis of estimated glucose values in one embodiment. [0077] FIG. 24 is a graph that illustrates variation of estimated analyte values in another embodiment. [0078] FIG. 25 is a flow chart that illustrates the process of estimating, measuring, and comparing analyte values in one embodiment. [0079] FIG. 26 is a graph that illustrates comparison of estimated analyte values in one embodiment. [0080] 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. [0081] FIG. 28 is a flow chart that illustrates the evaluation of calibrated sensor data for aberrant values in one embodiment. [0082] FIG. 29 provides a flow chart that illustrates a self-diagnostic of sensor data in one embodiment. [0083] FIG. 30 is a flow chart that illustrates the process of detecting and processing signal artifacts in certain embodiments. [0084] FIG. 31 is a graph that illustrates a raw data stream from a glucose sensor for approximately 24 hours with a filtered version of the same data stream superimposed on the same graph. [0085] FIG. 32 is a flowchart that illustrates a method for processing data from a glucose sensor in certain embodiments.
DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS [0086] The following description and examples illustrate some exemplary embodiments of the disclosed invention in detail. Those of skill in the art will recognize that there are numerous variations and modifications of this invention that are encompassed by its scope. Accordingly, the description of a certain exemplary embodiment should not be deemed to limit the scope of the present invention. DEFINITIONS [0087] In order to facilitate an understanding of the preferred embodiments, a number of terms are defined below. [0088] The term “analyte” as used herein 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 substance or chemical constituent in a biological fluid (for example, blood, interstitial fluid, cerebral spinal fluid, lymph fluid or urine) that can be analyzed. Analytes can include naturally occurring substances, artificial substances, metabolites, and/or reaction products. In some embodiments, the analyte for measurement by the sensor heads, devices, and methods is analyte. However, other analytes are contemplated as well, including but not limited to acarboxyprothrombin; acylcarnitine; adenine phosphoribosyl transferase; adenosine deaminase; albumin; alpha-fetoprotein; amino acid profiles (arginine (Krebs cycle), histidine/urocanic acid, homocysteine, phenylalanine/tyrosine, tryptophan); andrenostenedione; antipyrine; arabinitol enantiomers; arginase; benzoylecgonine (cocaine); biotinidase; biopterin; c-reactive protein; carnitine; carnosinase; CD4; ceruloplasmin; chenodeoxycholic acid; chloroquine; cholesterol; cholinesterase; conjugated 1-β hydroxy-cholic acid; cortisol; creatine kinase; creatine kinase MM isoenzyme; cyclosporin A; d-penicillamine; de-ethylchloroquine; dehydroepiandrosterone sulfate; DNA (acetylator polymorphism, alcohol dehydrogenase, alpha 1-antitrypsin, cystic fibrosis, Duchenne/Becker muscular dystrophy, analyte-6-phosphate dehydrogenase, hemoglobin A, hemoglobin S, hemoglobin C, hemoglobin D, hemoglobin E, hemoglobin F. D-Punjab, beta-thalassemia, hepatitis B virus, HCMV, HIV-1, HTLV-1, Leber hereditary optic neuropathy, MCAD, RNA, PKU, Plasmodium vivax, sexual differentiation, 21-deoxycortisol); desbutylhalofantrine; dihydropteridine reductase; diptheria/tetanus antitoxin; erythrocyte arginase; erythrocyte protoporphyrin; esterase D; fatty acids/acylglycines; free β-human chorionic gonadotropin; free erythrocyte porphyrin; free thyroxine (FT4); free tri-iodothyronine (FT3); fumarylacetoacetase; galactose/gal-1-phosphate; galactose-1-phosphate uridyltransferase; gentamicin; analyte-6-phosphate dehydrogenase; glutathione; glutathione perioxidase; glycocholic acid; glycosylated hemoglobin; halofantrine; hemoglobin variants; hexosaminidase A; human erythrocyte carbonic anhydrase I; 17-alpha-hydroxyprogesterone; hypoxanthine phosphoribosyl transferase; immunoreactive trypsin; lactate; lead; lipoproteins ((a), B/A-1, β); lysozyme; mefloquine; netilmicin; phenobarbitone; phenytoin; phytanic/pristanic acid; progesterone; prolactin; prolidase; purine nucleoside phosphorylase; quinine; reverse tri-iodothyronine (rT3); selenium; serum pancreatic lipase; sissomicin; somatomedin C; specific antibodies (adenovirus, anti-nuclear antibody, anti-zeta antibody, arbovirus, Aujeszky's disease virus, dengue virus, Dracunculus medinensis, Echinococcus granulosus, Entamoeba histolytica, enterovirus, Giardia duodenalisa, Helicobacter pylori, hepatitis B virus, herpes virus, HIV-1, IgE (atopic disease), influenza virus, Leishmania donovani, leptospira, measles/mumps/rubella, Mycobacterium leprae, Mycoplasma pneumoniae, Myoglobin, Onchocerca volvulus, parainfluenza virus, Plasmodium falciparum, poliovirus, Pseudomonas aeruginosa, respiratory syncytial virus, rickettsia (scrub typhus), Schistosoma mansoni, Toxoplasma gondii, Trepenoma pallidium, Trypanosoma cruzi/rangeli, vesicular stomatis virus, Wuchereria bancrofti, yellow fever virus); specific antigens (hepatitis B virus, HIV-1); succinylacetone; sulfadoxine; theophylline; thyrotropin (TSH); thyroxine (T4); thyroxine-binding globulin; trace elements; transferrin; UDP-galactose-4-epimerase; urea; uroporphyrinogen I synthase; vitamin A; white blood cells; and zinc protoporphyrin. Salts, sugar, protein, fat, vitamins, and hormones naturally occurring in blood or interstitial fluids can also constitute analytes in certain embodiments. The analyte can be naturally present in the biological fluid, for example, a metabolic product, a hormone, an antigen, an antibody, and the like. Alternatively, 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, psilocybin); narcotics (heroin, codeine, morphine, opium, meperidine, Percocet, Percodan, Tussionex, Fentanyl, Darvon, Talwin, Lomotil); designer drugs (analogs of fentanyl, meperidine, amphetamines, methamphetamines, and phencyclidine, for example, Ecstasy); anabolic steroids; and nicotine. The metabolic products of drugs and pharmaceutical compositions are also contemplated analytes. Analytes such as neurochemicals and other chemicals generated within the body can also be analyzed, such as, for example, ascorbic acid, uric acid, dopamine, noradrenaline, 3-methoxytyramine (3MT), 3,4-Dihydroxyphenylacetic acid (DOPAC), Homovanillic acid (HVA), 5-Hydroxytryptamine (5HT), and 5-Hydroxyindoleacetic acid (FHIAA). [0089] The term “EEPROM” as used herein 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 electrically erasable programmable read-only memory, which is user-modifiable read-only memory (ROM) that can be erased and reprogrammed (e.g., written to) repeatedly through the application of higher than normal electrical voltage. [0090] The term “SRAM” as used herein 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 static random access memory (RAM) that retains data bits in its memory as long as power is being supplied. [0091] The term “ROM” as used herein 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 read-only memory, which is a type of data storage device manufactured with fixed contents. ROM is broad enough to include EEPROM, for example, which is electrically erasable programmable read-only memory (ROM). [0092] The term “RAM” as used herein 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 data storage device for which the order of access to different locations does not affect the speed of access. RAM is broad enough to include SRAM, for example, which is static random access memory that retains data bits in its memory as long as power is being supplied. [0093] The term “A/D Converter” as used herein 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 hardware and/or software that converts analog electrical signals into corresponding digital signals. [0094] The terms “microprocessor” and “processor” 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 a computer system, state machine, and the like that performs arithmetic and logic operations using logic circuitry that responds to and processes the basic instructions that drive a computer. [0095] The term “RF transceiver” as used herein 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 radio frequency transmitter and/or receiver for transmitting and/or receiving signals. [0096] The term “jitter” as used herein 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 noise above and below the mean caused by ubiquitous noise caused by a circuit and/or environmental effects; jitter can be seen in amplitude, phase timing, or the width of the signal pulse. [0097] The terms “raw data stream” and “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 an analog or digital signal directly related to the measured glucose from the glucose sensor. In one example, the raw data stream is digital data in “counts” converted by an A/D converter from an analog signal (e.g., voltage or amps) and includes one or more data points representative of a glucose concentration. The terms broadly encompass a plurality of time spaced data points from a substantially continuous glucose sensor, which comprises individual measurements taken at time intervals ranging from fractions of a second up to, e.g., 1, 2, or 5 minutes or longer. In another example, the raw data stream includes an integrated digital value, wherein the data includes one or more data points representative of the glucose sensor signal averaged over a time period. [0098] The term “calibration” as used herein 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 process of determining the relationship between the sensor data and the corresponding reference data, which can be used to convert sensor data into meaningful values substantially equivalent to the reference data. In some embodiments, namely, in continuous analyte sensors, calibration can be updated or recalibrated over time as changes in the relationship between the sensor data and reference data occur, for example, due to changes in sensitivity, baseline, transport, metabolism, and the like. [0099] The terms “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. [0100] The terms “smoothed data” and “filtered data” 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 modified to make it smoother and more continuous and/or to remove or diminish outlying points, for example, by performing a moving average of the raw data stream. Examples of data filters include FIR (finite impulse response), IIR (infinite impulse response), moving average filters, and the like. [0101] The terms “smoothing” and “filtering” 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 modification of a set of data to make it smoother and more continuous or to remove or diminish outlying points, for example, by performing a moving average of the raw data stream. [0102] The term “algorithm” as used herein 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 computational process (for example, programs) involved in transforming information from one state to another, for example, by using computer processing. [0103] The term “matched data pairs” as used herein 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 reference data (for example, one or more reference analyte data points) matched with substantially time corresponding sensor data (for example, one or more sensor data points). [0104] The term “counts” as used herein 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 unit of measurement of a digital signal. In one example, a raw data stream measured in counts is directly related to a voltage (e.g., converted by an A/D converter), which is directly related to current from the working electrode. In another example, counter electrode voltage measured in counts is directly related to a voltage. [0105] The term “sensor” as used herein 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 component or region of a device by which an analyte can be quantified. [0106] The term “needle” as used herein 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 slender hollow instrument for introducing material into or removing material from the body. [0107] The terms “glucose sensor” and “member for determining the amount of glucose in a biological sample” 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 any mechanism (e.g., enzymatic or non-enzymatic) by which glucose can be quantified. For example, some embodiments utilize a membrane that contains glucose oxidase that catalyzes the conversion of oxygen and glucose to hydrogen peroxide and gluconate, as illustrated by the following chemical reaction: Glucose+O2→Gluconate+H2O2 [0108] Because for each glucose molecule metabolized, there is a proportional change in the co-reactant � 2 and the product H2O2, one can use an electrode to monitor the current change in either the co-reactant or the product to determine glucose concentration. [0109] The terms “operably connected” and “operably linked” 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 one or more components being linked to another component(s) in a manner that allows transmission of signals between the components. For example, one or more electrodes can be used to detect the amount of glucose in a sample and convert that information into a signal, e.g., an electrical or electromagnetic signal; the signal can then be transmitted to an electronic circuit. In this case, the electrode is “operably linked” to the electronic circuitry. These terms are broad enough to include wireless connectivity. [0110] The term “electronic circuitry” as used herein 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. In the case of a glucose-measuring device, 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. U.S. Pat. Nos. 4,757,022, 5,497,772 and 4,787,398, which are hereby incorporated by reference, describe suitable electronic circuits that can be utilized with devices including the biointerface membrane of a preferred embodiment. [0111] The term “substantially” as used herein 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 being largely but not necessarily wholly that which is specified. [0112] The term “proximal” as used herein 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 near to a point of reference such as an origin, a point of attachment, or the midline of the body. For example, in some embodiments of a glucose sensor, wherein the glucose sensor is the point of reference, an oxygen sensor located proximal to the glucose sensor will be in contact with or nearby the glucose sensor such that their respective local environments are shared (e.g., levels of glucose, oxygen, pH, temperature, etc. are similar). [0113] The term “distal” as used herein 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 spaced relatively far from a point of reference, such as an origin or a point of attachment, or midline of the body. For example, in some embodiments of a glucose sensor, wherein the glucose sensor is the point of reference, an oxygen sensor located distal to the glucose sensor will be sufficiently far from the glucose sensor such their respective local environments are not shared (e.g., levels of glucose, oxygen, pH, temperature, etc. may not be similar). [0114] The term “domain” as used herein 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 region of the membrane system that can be a layer, a uniform or non-uniform gradient (for example, an anisotropic region of a membrane), or a portion of a membrane. [0115] The terms “in vivo portion” and “distal portion” 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 the portion of the device (for example, a sensor) adapted for insertion into and/or existence within a living body of a host. [0116] The terms “ex vivo portion” and “proximal portion” 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 the portion of the device (for example, a sensor) adapted to remain and/or exist outside of a living body of a host. [0117] The term “electrochemical cell” as used herein 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 device in which chemical energy is converted to electrical energy. Such a cell typically consists of two or more electrodes held apart from each other and in contact with an electrolyte solution. Connection of the electrodes to a source of direct electric current renders one of them negatively charged and the other positively charged. Positive ions in the electrolyte migrate to the negative electrode (cathode) and there combine with one or more electrons, losing part or all of their charge and becoming new ions having lower charge or neutral atoms or molecules; at the same time, negative ions migrate to the positive electrode (anode) and transfer one or more electrons to it, also becoming new ions or neutral particles. The overall effect of the two processes is the transfer of electrons from the negative ions to the positive ions, a chemical reaction. [0118] The term “potentiostat” as used herein 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 an electrical system that controls the potential between the working and reference electrodes of a three-electrode cell at a preset value. It forces whatever current is necessary to flow between the working and counter electrodes to keep the desired potential, as long as the needed cell voltage and current do not exceed the compliance limits of the potentiostat. [0119] The term “electrical potential” as used herein 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 electrical potential difference between two points in a circuit which is the cause of the flow of a current. [0120] The term “host” as used herein 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 mammals, particularly humans. [0121] The term “continuous analyte (or glucose) sensor” as used herein 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 device that continuously or continually measures a concentration of an analyte, for example, at time intervals ranging from fractions of a second up to, for example, 1, 2, or 5 minutes, or longer. In one exemplary embodiment, the continuous analyte sensor is a glucose sensor such as described in U.S. Pat. No. 6,001,067, which is incorporated herein by reference in its entirety. [0122] The term “continuous analyte (or glucose) sensing” as used herein 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 period in which monitoring of an analyte is continuously or continually performed, for example, a