Patent Publication Number: US-2018045707-A1

Title: Analyte Sensor with Lag Compensation

Description:
RELATED APPLICATIONS 
     The present application is a continuation of U.S. patent application Ser. No. 15/141,820 filed Apr. 29, 2016, now U.S. Pat. No. 9,804,148, which is a continuation of U.S. patent application Ser. No. 13/763,518 filed Feb. 8, 2013, now U.S. Pat. No. 9,332,934, which is a continuation of U.S. patent application Ser. No. 12/257,356 filed Oct. 23, 2008, now U.S. Pat. No. 8,374,668, entitled “Analyte Sensor with Lag Compensation”, which claims priority under 35 U.S.C. § 119(e) to U.S. Provisional Application No. 60/982,095 filed Oct. 23, 2007, entitled “Evaluation Of Time Shift And Time Constant In Capillary To Interstitial Glucose Dynamics Using a Continuous Glucose Monitoring System”, and to U.S. Provisional Application No. 61/025,290 filed Jan. 31, 2008, entitled “Analyte Sensor with Time Lag Compensation” each assigned to the Assignee of the present application, Abbott Diabetes Care Inc., of Alameda, Calif., the disclosures of each of which are incorporated herein by reference for all purposes. The present application is also related to U.S. application Ser. No. 11/537,991 filed Oct. 02, 2006, now U.S. Pat. No. 7,618,369, the disclosure of which is incorporated by reference for all purposes. 
    
    
     BACKGROUND 
     Analyte, e.g., glucose monitoring systems including continuous and discrete monitoring systems generally include a small, lightweight battery powered and microprocessor controlled system which is configured to detect signals proportional to the corresponding measured glucose levels using an electrometer, and RF signals to transmit the collected data. One aspect of certain analyte monitoring systems include a transcutaneous or subcutaneous analyte sensor configuration which is, for example, partially mounted on the skin of a subject whose analyte level is to be monitored. The sensor cell may use a two or three-electrode (work, reference and counter electrodes) configuration driven by a controlled potential (potentiostat) analog circuit connected through a contact system. 
     The analyte sensor may be configured so that at least a portion thereof is placed under the skin of the patient so as to detect the analyte levels of the patient. In embodiments in which a portion is below the skin and a portion is above, the portion above the skin may be directly or indirectly connected with the transmitter unit. The transmitter unit is configured to transmit the analyte levels, e.g., in the form of current, detected by the sensor over a wireless (or wired) communication link such as an RF (radio frequency) communication link to a receiver/monitor unit. The receiver/monitor unit performs data analysis, among others on the received analyte levels to generate information pertaining to the monitored analyte levels. 
     To obtain accurate data from the analyte sensor, calibration may be necessary. In certain instances, blood glucose measurements are periodically obtained using, for example, a conventional analyte test strip and blood glucose meter, and the measured blood glucose values are used to calibrate the sensors. Indeed, the patient may calibrate each new analyte sensor using for example, capillary blood glucose measurements. Due to a lag factor between the monitored data and the measured blood glucose values, an error may be introduced in the monitored data. 
     SUMMARY 
     In one embodiment, method, device and system for calibrating analyte data associated with a monitored analyte level received from an analyte sensor based on a reference measurement, determining a lag time constant associated with the calibrated analyte data, and performing lag correction of the calibrated analyte data based on the determined time lag constant are provided. 
     In another embodiment, method, device and system for calibrating analyte data associated with a monitored analyte level received from an analyte sensor based on a reference measurement, determining a time constant component and a time shift component associated with the calibrated analyte data, and estimating a lag time based on the determined time constant component and the time shift component are provided. 
     These and other objects, features and advantages of the present disclosure will become more fully apparent from the following detailed description of the embodiments, the appended claims and the accompanying drawings. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  illustrates a block diagram of a data monitoring and management system for practicing one or more embodiments of the present disclosure; 
         FIG. 2  is a block diagram of the transmitter unit of the data monitoring and management system shown in  FIG. 1  in accordance with one embodiment of the present disclosure; 
         FIG. 3  is a block diagram of the receiver/monitor unit of the data monitoring and management system shown in  FIG. 1  in accordance with one embodiment of the present disclosure; 
         FIG. 4  is a flowchart illustrating an overall dynamically updating calibration in accordance with one embodiment of the present disclosure; 
         FIG. 5  is a flowchart illustrating the lag correction and calibration routine of the overall dynamically updating calibration shown in  FIG. 4  in accordance with one embodiment of the present disclosure; 
         FIG. 6  is a flowchart illustrating the lag correction and dynamically updating calibration routine of the overall dynamically updating calibration shown in  FIG. 4  in accordance with one embodiment of the present disclosure; 
         FIG. 7  is a flowchart illustrating a method for estimating the lag time constant from sensor analyte data and reference measurements in accordance with one embodiment of the present disclosure; and 
         FIG. 8  is a flowchart illustrating lag time constant compensated analyte sensor data in accordance with one embodiment of the present disclosure. 
     
    
    
     DETAILED DESCRIPTION 
     As described in further detail below, in accordance with the various embodiments of the present disclosure, there is provided a method and system for calibration of analyte sensors to reduce errors in the sensor measurements. In particular, within the scope of the present disclosure, there are provided method and system for calibrating subcutaneous or transcutaneously positioned analyte sensors to compensate for time lag errors associated with an analyte sensor. 
       FIG. 1  illustrates a data monitoring and management system such as, for example, analyte (e.g., glucose) monitoring system  100  in accordance with one embodiment of the present disclosure. The subject invention is further described primarily with respect to a glucose monitoring system for convenience and such description is in no way intended to limit the scope of the invention. It is to be understood that the analyte monitoring system may be configured to monitor a variety of analytes, e.g., lactate, and the like. For example, analytes that may be monitored include but are not limited to, for example, acetyl choline, amylase, bilirubin, cholesterol, chorionic gonadotropin, creatine kinase (e.g., CK-MB), creatine, DNA, fructosamine, glucose, glutamine, growth hormones, hormones, ketones, lactate, peroxide, prostate-specific antigen, prothrombin, RNA, thyroid stimulating hormone, and troponin. The concentration of drugs, such as, for example, antibiotics (e.g., gentamicin, vancomycin, and the like), digitoxin, digoxin, drugs of abuse, theophylline, and warfarin, may also be monitored. 
     The analyte monitoring system  100  includes a sensor  101 , a transmitter unit  102  directly or indirectly coupled to the sensor  101 , and a primary receiver unit  104  which is configured to communicate with the transmitter unit  102  via a communication link  103 . The primary receiver unit  104  may be further configured to transmit data to a data processing terminal  105  for evaluating the data received by the primary receiver unit  104 . Moreover, the data processing terminal in one embodiment may be configured to receive data directly from the transmitter unit  102  via a communication link which may optionally be configured for bi-directional communication. 
     Also shown in  FIG. 1  is an optional secondary receiver unit  106  which is operatively coupled to the communication link and configured to receive data transmitted from the transmitter unit  102 . Moreover, as shown in the Figure, the secondary receiver unit  106  is configured to communicate with the primary receiver unit  104  as well as the data processing terminal  105 . Indeed, the secondary receiver unit  106  may be configured for bi-directional wireless communication with each of the primary receiver unit  104  and the data processing terminal  105 . As discussed in further detail below, in one embodiment of the present disclosure, the secondary receiver unit  106  may be configured to include a limited number of functions and features as compared with the primary receiver unit  104 . As such, the secondary receiver unit  106  may be configured substantially in a smaller compact housing or embodied in a device such as a wrist watch, for example. Alternatively, the secondary receiver unit  106  may be configured with the same or substantially similar functionality as the primary receiver unit  104 , and may be configured to be used in conjunction with a docking cradle unit for placement by bedside, for night time monitoring, and/or bi-directional communication device. 
     Only one sensor  101 , transmitter unit  102 , communication link  103 , and data processing terminal  105  are shown in the embodiment of the analyte monitoring system  100  illustrated in  FIG. 1 . However, it will be appreciated by one of ordinary skill in the art that the analyte monitoring system  100  may include one or more sensor  101 , transmitter unit  102 , communication link  103 , and data processing terminal  105 . Moreover, within the scope of the present disclosure, the analyte monitoring system  100  may be a continuous monitoring system, or semi-continuous, or a discrete monitoring system. In a multi-component environment, each device is configured to be uniquely identified by each of the other devices in the system so that communication conflict is readily resolved between the various components within the analyte monitoring system  100 . 
     In one embodiment of the present disclosure, the sensor  101  is physically positioned in or on the body of a user whose analyte level is being monitored. In one aspect, the sensor  101  may be configured to use one or more of coulometric, amperometric, potentiometric or conductimetric approaches to measure the analyte level being monitored. The sensor  101  may be configured to continuously sample the analyte of the user and the sampled analyte may be converted into a corresponding data signal for transmission by the transmitter unit  102 . In one embodiment, the transmitter unit  102  is mounted on the sensor  101  so that both devices are positioned on the user&#39;s body. The transmitter unit  102  performs data processing such as filtering and encoding on data signals, each of which corresponds to a sampled analyte level of the user, for transmission to the primary receiver unit  104  via the communication link  103 . 
     In one embodiment, the analyte monitoring system  100  is configured as a one-way RF communication path from the transmitter unit  102  to the primary receiver unit  104 . In such embodiment, the transmitter unit  102  may transmit the sampled data signals received from the sensor  101  without acknowledgement from the primary receiver unit  104  that the transmitted sampled data signals have been received (in other embodiments there may be acknowledgement). For example, the transmitter unit  102  may be configured to transmit the encoded sampled data signals at a fixed rate (e.g., at one minute intervals or other interval) after the completion of the initial power on procedure. Likewise, the primary receiver unit  104  may be configured to detect such transmitted encoded sampled data signals at predetermined time intervals. Alternatively, the analyte monitoring system  100  may be configured with a bi-directional RF (or otherwise) communication between the transmitter unit  102  and the primary receiver unit  104 . 
     Additionally, in one aspect, the primary receiver unit  104  may include two sections. The first section is an analog interface section that is configured to communicate with the transmitter unit  102  via the communication link  103 . In one embodiment, the analog interface section may include an RF receiver and an antenna for receiving and amplifying the data signals from the transmitter unit  102 , which are thereafter, demodulated with a local oscillator and filtered through a band-pass filter. The second section of the primary receiver unit  104  is a data processing section which is configured to process the data signals received from the transmitter unit  102  such as by performing data decoding, error detection and correction, data clock generation, and data bit recovery. 
     In operation in certain embodiments, upon completing a power-on procedure if required, the primary receiver unit  104  is configured to detect the presence of the transmitter unit  102  within its range based on, for example, the strength of the detected data signals received from the transmitter unit  102  or a predetermined transmitter identification information. Upon successful synchronization with the corresponding transmitter unit  102 , the primary receiver unit  104  is configured to begin receiving from the transmitter unit  102  data signals corresponding to the user&#39;s detected analyte level. More specifically, the primary receiver unit  104  in one embodiment is configured to perform synchronized time hopping with the corresponding synchronized transmitter unit  102  via the communication link  103  to obtain the user&#39;s detected analyte level. 
     Referring again to  FIG. 1 , the data processing terminal  105  may include a personal computer, a portable computer such as a laptop or a handheld device (e.g., personal digital assistants (PDAs) telephone such as a cellular telephone), and the like, each of which may be configured for data communication with the receiver via a wired or a wireless connection. Additionally, the data processing terminal  105  may further be connected to a data network (not shown) for storing, retrieving and updating data corresponding to the detected analyte level of the user. 
     Within the scope of the present disclosure, the data processing terminal  105  may include an infusion device such as an insulin infusion pump or the like, which may be configured to administer a drug such as, for example insulin, to users, and which may be configured to communicate with the receiver unit  104  for receiving, among others, the measured analyte level. Alternatively, the receiver unit  104  may be configured to integrate an infusion device therein so that the receiver unit  104  is configured to administer insulin therapy to patients, for example, for administering and modifying basal profiles, as well as for determining appropriate boluses for administration based on, among others, the detected analyte levels received from the transmitter unit  102 . 
     Additionally, the transmitter unit  102 , the primary receiver unit  104  and the data processing terminal  105  may each be configured for bi-directional wireless communication such that each of the transmitter unit  102 , the primary receiver unit  104  and the data processing terminal  105  may be configured to communicate (that is, transmit data to and receive data from) with each other via the wireless communication link  103 . More specifically, the data processing terminal  105  may in one embodiment be configured to receive data directly from the transmitter unit  102  via a communication link, where the communication link, as described above, may be configured for bi-directional communication. 
     In this embodiment, the data processing terminal  105  which may include an insulin pump, may be configured to receive the analyte signals from the transmitter unit  102 , and thus, incorporate the functions of the receiver unit  104  including data processing for managing the patient&#39;s insulin therapy and analyte monitoring. In one embodiment, the communication link  103  may include one or more of an RF communication protocol, an infrared communication protocol, a Bluetooth® enabled communication protocol, an 802.11x wireless communication protocol, or an equivalent wireless communication protocol which would allow secure, wireless communication of several units (for example, per HIPAA requirements) while avoiding potential data collision and interference. 
       FIG. 2  is a block diagram of the transmitter of the data monitoring and detection system shown in  FIG. 1  in accordance with one embodiment of the present disclosure. Referring to the Figure, the transmitter unit  102  in one embodiment includes an analog interface  201  configured to communicate with the sensor  101  ( FIG. 1 ), a user input  202 , and a temperature detection section  203 , each of which is operatively coupled to a transmitter processor  204  such as a central processing unit (CPU). As can be seen from  FIG. 2 , there are provided four contacts, three of which are electrodes—work electrode (W)  210 , guard contact (G)  211 , reference electrode (R)  212 , and counter electrode (C)  213 , each operatively coupled to the analog interface  201  of the transmitter unit  102  for connection to the sensor  101  ( FIG. 1 ). In one embodiment, each of the work electrode (W)  210 , guard contact (G)  211 , reference electrode (R)  212 , and counter electrode (C)  213  may be made using a conductive material that may be applied in any suitable manner, e.g., printed or etched, for example, such as carbon, gold, and the like, which may be printed, or metal foil (e.g., gold) which may be etched. 
     Further shown in  FIG. 2  are a transmitter serial communication section  205  and an RF transmitter  206 , each of which is also operatively coupled to the transmitter processor  204 . Moreover, a power supply  207  such as a battery is also provided in the transmitter unit  102  to provide the necessary power for the transmitter unit  102 . Additionally, as can be seen from the Figure, clock  208  is provided to, among others, supply real time information to the transmitter processor  204 . 
     In one embodiment, an unidirectional input path is established from the sensor  101  ( FIG. 1 ) and/or manufacturing and testing equipment to the analog interface  201  of the transmitter unit  102 , while an unidirectional output is established from the output of the RF transmitter  206  of the transmitter unit  102  for transmission to the primary receiver unit  104 . In this manner, a data path is shown in  FIG. 2  between the aforementioned unidirectional input and output via a dedicated link  209  from the analog interface  201  to serial communication section  205 , thereafter to the processor  204 , and then to the RF transmitter  206 . As such, in one embodiment, via the data path described above, the transmitter unit  102  is configured to transmit to the primary receiver unit  104  ( FIG. 1 ), via the communication link  103  ( FIG. 1 ), processed and encoded data signals received from the sensor  101  ( FIG. 1 ). Additionally, the unidirectional communication data path between the analog interface  201  and the RF transmitter  206  discussed above allows for the configuration of the transmitter unit  102  for operation upon completion of the manufacturing process as well as for direct communication for diagnostic and testing purposes. 
     As discussed above, the transmitter processor  204  is configured to transmit control signals to the various sections of the transmitter unit  102  during the operation of the transmitter unit  102 . In one embodiment, the transmitter processor  204  also includes a memory (not shown) for storing data such as the identification information for the transmitter unit  102 , as well as the data signals received from the sensor  101 . The stored information may be retrieved and processed for transmission to the primary receiver unit  104  under the control of the transmitter processor  204 . Furthermore, the power supply  207  may include a commercially available battery. 
     The transmitter unit  102  is also configured such that the power supply section  207  is capable of providing power to the transmitter for a predetermined minimum continuous operation time period and also with a predetermined minimum shelf life time period such as, for example, a minimum of about three months of continuous operation after having been stored for about eighteen months in a low-power (non-operating) mode. In one embodiment, this may be achieved by the transmitter processor  204  operating in low power modes in the non-operating state, for example, drawing no more than approximately 1 μA of current. Indeed, in one embodiment, the final step during the manufacturing process of the transmitter unit  102  may place the transmitter unit  102  in the lower power, non-operating state (i.e., post-manufacture sleep mode). In this manner, the shelf life of the transmitter unit  102  may be significantly improved. Moreover, as shown in  FIG. 2 , while the power supply unit  207  is shown as coupled to the processor  204 , and as such, the processor  204  is configured to provide control of the power supply unit  207 , it should be noted that within the scope of the present disclosure, the power supply unit  207  is configured to provide the necessary power to each of the components of the transmitter unit  102  shown in  FIG. 2 . 
     Referring back to  FIG. 2 , the power supply section  207  of the transmitter unit  102  in one embodiment may include a rechargeable battery unit that may be recharged by a separate power supply recharging unit (for example, provided in the receiver unit  104 ) so that the transmitter unit  102  may be powered for a longer period of usage time. Moreover, in one embodiment, the transmitter unit  102  may be configured without a battery in the power supply section  207 , in which case the transmitter unit  102  may be configured to receive power from an external power supply source (for example, a battery) as discussed in further detail below. 
     Referring yet again to  FIG. 2 , the optional temperature detection section  203  of the transmitter unit  102  is configured to monitor the temperature of the skin near the sensor insertion site. The temperature reading may be used to adjust the analyte readings obtained from the analog interface  201 . The RF transmitter  206  of the transmitter unit  102  may be configured for operation in the frequency band of 315 MHz to 322 MHz, for example, in the United States. Further, in one embodiment, the RF transmitter  206  is configured to modulate the carrier frequency by performing Frequency Shift Keying and Manchester encoding. In one embodiment, the data transmission rate is 19,200 symbols per second, with a minimum transmission range for communication with the primary receiver unit  104 . 
     Referring yet again to  FIG. 2 , also shown is a leak detection circuit  214  coupled to the guard contact (G)  211  and the processor  204  in the transmitter unit  102  of the data monitoring and management system  100 . The leak detection circuit  214  in accordance with one embodiment of the present disclosure may be configured to detect leakage current in the sensor  101  to determine whether the measured sensor data is corrupt or whether the measured data from the sensor  101  is accurate. 
     Additional detailed description of the continuous analyte monitoring system, its various components including the functional descriptions of the transmitter are provided in U.S. Pat. No. 6,175,752 issued Jan. 16, 2001 entitled “Analyte Monitoring Device and Methods of Use”, and in U.S. application Ser. No. 10/745,878 filed Dec. 26, 2003, now U.S. Pat. No. 7,811,231, entitled “Continuous Glucose Monitoring System and Methods of Use”, each assigned to the Assignee of the present application. 
       FIG. 3  is a block diagram of the receiver/monitor unit of the data monitoring and management system shown in  FIG. 1  in accordance with one embodiment of the present disclosure. Referring to  FIG. 3 , the primary receiver unit  104  includes a blood glucose test strip interface  301 , an RF receiver  302 , an input  303 , a temperature detection section  304 , and a clock  305 , each of which is operatively coupled to a receiver processor  307 . As can be further seen from the Figure, the primary receiver unit  104  also includes a power supply  306  operatively coupled to a power conversion and monitoring section  308 . Further, the power conversion and monitoring section  308  is also coupled to the receiver processor  307 . Moreover, also shown are a receiver serial communication section  309 , and an output  310 , each operatively coupled to the receiver processor  307 . 
     In one embodiment, the test strip interface  301  includes a glucose level testing portion to receive a manual insertion of a glucose test strip, and thereby determine and display the glucose level of the test strip on the output  310  of the primary receiver unit  104 . This manual testing of glucose can be used to calibrate sensor  101 . The RF receiver  302  is configured to communicate, via the communication link  103  ( FIG. 1 ) with the RF transmitter  206  of the transmitter unit  102 , to receive encoded data signals from the transmitter unit  102  for, among others, signal mixing, demodulation, and other data processing. The input  303  of the primary receiver unit  104  is configured to allow the user to enter information into the primary receiver unit  104  as needed. In one aspect, the input  303  may include one or more keys of a keypad, a touch-sensitive screen, or a voice-activated input command unit. The temperature detection section  304  is configured to provide temperature information of the primary receiver unit  104  to the receiver processor  307 , while the clock  305  provides, among others, real time information to the receiver processor  307 . 
     Each of the various components of the primary receiver unit  104  shown in  FIG. 3  is powered by the power supply  306  which, in one embodiment, includes a battery. Furthermore, the power conversion and monitoring section  308  is configured to monitor the power usage by the various components in the primary receiver unit  104  for effective power management and to alert the user, for example, in the event of power usage which renders the primary receiver unit  104  in sub-optimal operating conditions. An example of such sub-optimal operating condition may include, for example, operating the vibration output mode (as discussed below) for a period of time thus substantially draining the power supply  306  while the processor  307  (thus, the primary receiver unit  104 ) is turned on. Moreover, the power conversion and monitoring section  308  may additionally be configured to include a reverse polarity protection circuit such as a field effect transistor (FET) configured as a battery activated switch. 
     The serial communication section  309  in the primary receiver unit  104  is configured to provide a bi-directional communication path from the testing and/or manufacturing equipment for, among others, initialization, testing, and configuration of the primary receiver unit  104 . Serial communication section  309  can also be used to upload data to a computer, such as time-stamped blood glucose data. The communication link with an external device (not shown) can be made, for example, by cable, infrared (IR) or RF link. The output  310  of the primary receiver unit  104  is configured to provide, among others, a graphical user interface (GUI) such as a liquid crystal display (LCD) for displaying information. Additionally, the output  310  may also include an integrated speaker for outputting audible signals as well as to provide vibration output as commonly found in handheld electronic devices, such as mobile telephones presently available. In a further embodiment, the primary receiver unit  104  also includes an electro-luminescent lamp configured to provide backlighting to the output  310  for output visual display in dark ambient surroundings. 
     Referring back to  FIG. 3 , the primary receiver unit  104  in one embodiment may also include a storage section such as a programmable, non-volatile memory device as part of the processor  307 , or provided separately in the primary receiver unit  104 , operatively coupled to the processor  307 . The processor  307  may be configured to perform Manchester decoding as well as error detection and correction upon the encoded data signals received from the transmitter unit  102  via the communication link  103 . 
       FIG. 4  is a flowchart illustrating an overall dynamically updating calibration in accordance with various embodiments of the present disclosure. Referring to  FIG. 4 , a counter such as a calibration counter is triggered to perform calibration of the monitored data such as the analyte data received from the transmitter unit  102  ( FIG. 1 ). In one aspect, the calibration may be performed on calibrated data, or on the uncalibrated data, for example, on the analyte data received prior to the initial calibration performed. In one embodiment, the calibration counter may include a timer or a clock which may be configured to prompt the user or the patient to initiate the acquisition of reference data (e.g., blood glucose reference data, e.g., obtained using a test strip/meter system) at a predetermined time interval. When the calibration counter is initially triggered, the time counter T is initialized to zero (0) ( 410 ). Thereafter, a calibration parameter is determined based on, for example, the acquired reference data and the monitored sensor data at time T=0 ( 420 ). Moreover, in one embodiment, the monitored sensor data may be updated based on the calibration parameter. In one embodiment, the calibration parameter may include a sensor sensitivity value associated with the analyte sensor  101  ( FIG. 1 ) configured to monitor the analyte levels of the patient. 
     As described in further detail below, for example, in conjunction with  FIG. 5 , in particular embodiments, during the initial calibration stage at T=0, a reference glucose value is determined, for example, such as a capillary blood glucose value using a blood glucose determination system such as FreeStyle® blood glucose monitoring system or Precision® blood glucose monitoring system available from Abbott Diabetes Care Inc., Alameda, Calif. In certain embodiments, a blood glucose meter may be integrated into the receiver unit. In addition, the monitored sensor data at or near the calibration time (T=0) is retrieved which may include the monitored sensor data at time T=T−1, at time T=T+1, or any other suitable time period (for example, from the processing and storage unit  307  ( FIG. 3 ) of the receiver unit  104  ( FIG. 1 )). 
     More specifically, in one embodiment, the monitored sensor data at the calibration time (T=0) may include one or more monitored sensor data in addition to the monitored sensor data point at the calibration time (T=0). That is, in one embodiment, the monitored sensor data at the calibration time (T=0) may include all monitored sensor data available for retrieval from the receiver unit  104  ( FIG. 1 ) at the calibration time (T=0). For example, to reduce the contribution of noise in the measured sensor data, an average of the two most recent sensor data may be associated with the monitored sensor data at the calibration time (T=0). Within the scope of the present disclosure, the average of more than two sensor data may be used. 
     The monitored sensor data at a predetermined time may include, in particular embodiments, an estimate of the sensor data at the predetermined time as determined by the one or more filters which may be configured to use the monitored sensor data up to and including the data point at the predetermined time (for example, up to the data point at calibration time (T=0)). In one embodiment, one or more filters such as a finite impulse response (FIR) filter may be used to determine the best estimate at a predetermined time using a finite window of monitored sensor data up to the current or most recent monitored sensor data point. 
     Referring back to  FIG. 4 , after determining the calibration parameter and updating the monitored data at the calibration time (T=0), the counter is incremented by one (1) ( 430 ), and dynamic, real-time update of the calibration parameter is performed ( 440 ). In one embodiment, the counter may be configured to increment by one with each reception of sensor data from the transmitter unit  102  ( FIG. 1 ). After dynamically updating the calibration parameter at the subsequent incremented time (T=1), it is determined whether the counter has reached a predetermined count ( 450 ) (for example, set at seven (7)). If it is determined that the counter has not reached the predetermined count, then the routine in one embodiment returns to step  430  where the counter is incremented by one (1) and the dynamically updating calibration parameter and monitored sensor data is performed for monitored data at the second subsequent incremented time (T=2). 
     On the other hand, if it is determined that the counter has reached the predetermined count, then in one embodiment, subsequent monitored sensor data may be updated based on the dynamically updated calibration parameter and/or updated monitored sensor data ( 460 ). Thereafter, in particular embodiments, it is determined whether further or subsequent lag correction will likely not yield more accurate monitored data values (or with less errors). Therefore, in one embodiment, the routine terminates and awaits for the subsequent calibration time, for example, to repeat the processes described above in conjunction with  FIG. 4 . When a procedure such as described in  FIG. 4  has been successfully completed, routines such as determining lag time constant based on calibrated sensor data  740  ( FIG. 7 ) may be updated in order to obtain a better estimate. 
     In this manner, there are provided methods and system for dynamically, and in particular embodiments, in real-time, obtaining reference data at a first predetermined time, receiving measured data prior to and including (or near) the first predetermined time, calculating a first calibration parameter (or parameters) using the data, calibrating the measured data based on the calibration parameter, receiving measured data at a second predetermined time, updating the calibration parameter based on all of the previous data and the newly received measured data, calibrating the newly received measured data based on the updated calibration parameter, and repeating a number of times the process of receiving new measurement data, updating the calibration parameter, calibrating the newly received measurement data, and calibrating any newly received measurement data with the fully updated calibration parameter. 
     A method in a further embodiment may include performing lag compensation on the measured data that is used to update the calibration parameter. Lag compensation may optionally be performed on the measured data that is calibrated or on uncalibrated data. A method in a further embodiment includes filtering the measured data that is used to update the calibration parameter. 
       FIG. 5  is a flowchart illustrating the lag correction and calibration routine of the overall dynamically updating calibration shown in  FIG. 4  in accordance with one embodiment of the present disclosure. Referring to  FIG. 5 , the determination of calibration parameter and updating the monitored analyte level at the calibration time (T=0) is described in further detail. More specifically, in one embodiment, a capillary blood glucose value is determined at the calibration time (T=0) ( 510 ), and the monitored analyte value at the calibration time is retrieved ( 520 ) from the receiver unit  104  of the monitoring system  100  ( FIG. 1 ). 
     Thereafter, a rate of change of the monitored data at the calibration time (T=0) is determined ( 530 ). In one embodiment, the rate of change of the monitored data at the calibration time (T=0) may be determined using one or more filters including, but not limited to infinite impulse response (IIR) filter, finite impulse response (FIR) filter, backward and/or forward smoothing techniques (e.g., Kalman filtering technique), or any other equivalent one or more causal filters that balance signal noise reduction with lag correction. 
     Upon determining the rate of change of the monitored data at the calibration time (T=0), the monitored data at the calibration time (T=0) is updated. In one embodiment, the updated monitored sensor data may include lag corrected monitored data at the calibration time (T=0) ( 540 ). Optionally, the lag correction for the monitored data at the calibration time (T=0) may be skipped and not performed. In one embodiment, the lag corrected monitored data at the calibration time (T=0) may be determined by applying the determined rate of change of the monitored data at the calibration time (T=0) to a predetermined constant value. In one embodiment, the predetermined constant value may include, a predetermined time constant. 
     For example, in one embodiment, the predetermined time constant may include a fixed time constant in the range of approximately four to fifteen minutes, and which may be associated with the one or more of the patient physiological profile, one or more attributes associated with the monitoring system  100  (including, for example but not limited to, the characteristics of the analyte sensor  101 ). In a further aspect, the predetermined time constant may vary based on one or more factors including, for example, but not limited to the timing and amount of food intake by the patient, exogenous insulin intake, physical activities by the patient such as exercise, or any other factors that may affect the time constant, and which may be empirically determined. 
     Referring again to  FIG. 5 , the calibration parameter (for example, the sensitivity of the analyte sensor  101  ( FIG. 1 )), may be determined ( 550 ) for example, in one embodiment, by determining the ratio of the monitored data (optionally lag corrected) at the calibration time (T=0) and the reference data obtained using, for example, the blood glucose meter as described above. In one embodiment, the calibration parameter may be determined by dividing the monitored data at the calibration time (T=0) by the reference data such as the capillary blood glucose value at the calibration time (T=0). 
     Thereafter, in one embodiment, the calibrated and updated monitored sensor data at the calibration time (T=0) is determined based upon the monitored data (optionally lag corrected) and the calibration parameter as determined above ( 560 ). For example, in one embodiment, the calibrated and updated monitored sensor data at the calibration time (T=0) may be determined by dividing the lag corrected monitored data at calibration time (T=0) by the determined calibration parameter. 
       FIG. 6  is a flowchart illustrating the lag correction and dynamically updating calibration routine of the overall dynamically updating calibration shown in  FIG. 4  in accordance with one embodiment of the present disclosure. Referring to  FIGS. 4 and 6 , with the counter incremented by one (see step  430  of  FIG. 4  and step  610   b  of  FIG. 6 ), the analyte value at the subsequent incremented time (T=1) is retrieved from, for example, the processing and storage unit  307  ( FIG. 3 ) of the receiver unit  104 . In particular, in one embodiment, the rate of change of the monitored data at the calibration time (T=0) is updated based on the monitored data value at the subsequent incremented time (T=1). In other words, the process starts with the determination of glucose rate of change at calibration time (T=0)  610  in FIG. 6 . At the next time increment, this value is revised using the monitored data values at calibration time (T=0) and prior data and at the subsequent incremented time (T=1) (see  620  and  630  of  FIG. 6 ), the rate of change of the monitored data at the calibration time (T=0) may be estimated with an improved accuracy at T=1 (see  640  of  FIG. 6 ). Again, in one embodiment, the rate of change may be determined based on one or more of, but not limited to, infinite impulse response (IIR) filter, finite impulse response (FIR) filter, backward and/or forward smoothing techniques (e.g., Kalman filtering technique), or any other equivalent filtering or smoothing techniques. 
     With the updated rate of change at the calibration time (T=0) determined, monitored data (optionally lag corrected) at calibration time (T=0) is updated. That is, in one embodiment, the lag corrected sensor data at the calibration time (T=0) is updated based on the prior lag corrected and calibrated data at calibration time (T=0), and in conjunction with the predetermined constant (for example, the predetermined time constant discussed above), and the updated rate of change of the monitored data at the calibration time (T=0). For example, in one embodiment, the lag corrected monitored data at the calibration time (T=0) is updated or determined by taking the sum of the lag corrected and calibration sensor value at calibration time (T=0) as determined above, with the updated rate of change of monitored data at calibration time (T=0) multiplied by the predetermined constant. In other words, in one embodiment, the updated rate of change of the monitored data at calibration time (T=0) may be multiplied by the predetermined constant, and thereafter, the resulting value is added to the lag corrected and calibrated monitored data at the calibration time (T=0) previously determined (see for example, step  420 ). 
     Referring again to  FIG. 6 , after determining the updated lag corrected monitored data at calibration time (T=0) based on monitored data at the subsequent incremented time (T=1) as described above, in one embodiment, the calibration parameter (for example, the sensitivity of the sensor  101  ( FIG. 1 )) is updated based on the updated lag corrected monitored data at calibration time (T=0) described above. In particular, in one embodiment, the calibration parameter may be updated by determining the ratio of the updated lag corrected monitored data at calibration time (T=0) and the reference value (for example, the capillary blood glucose value) determined at calibration time (T=0). 
     After updating the calibration parameter as described above, in one embodiment, the lag corrected and calibrated monitored data at the subsequent incremented time (T=1) is determined based on the updated calibration parameter value. For example, in one embodiment, the monitored sensor data at the subsequent incremented time (T=1) in one embodiment may be divided by the updated sensitivity to determine the dynamically lag corrected and calibrated monitored sensor data at the subsequent incremented time (T=1). 
     In another embodiment, the dynamically lag corrected and calibrated monitored sensor data at the subsequent incremented time (T=1) may be determined based on the updated calibration parameter and the dynamically lag corrected monitored sensor data at the subsequent incremented time (T=1). In this case, the dynamically updated sensor data at the subsequent incremented time (T=1) in one embodiment may be determined by calculating the rate of change of the monitored data at the subsequent incremented time (T=1) using similar filtering techniques as described above, and applying the predetermined constant (for example, the predetermined time constant discussed above), the result of which is then added to the detected or monitored data at the subsequent incremented time (T=1). In other words, in one embodiment, the calculated rate of change of the monitored data at the subsequent incremented time (T=1) is multiplied by the predetermined time constant, and the resulting value is added to the monitored data value at the subsequent incremented time (T=1). This sum in one embodiment represents the dynamically updated monitored sensor data at the subsequent incremented time (T=1). 
     In this manner, in one embodiment, lag correction of analyte sensor data may be pseudo-retrospectively (or substantially in real time) updated using the monitored analyte data stream substantially continuously detected by the sensor  101  ( FIG. 1 ) with the dynamic updating of the calibration parameter. Thus, in one aspect, lag error or error due to lag compensation may be overcome by, for example, updating the sensor sensitivity retrospectively with each value of the detected or monitored analyte levels. Accordingly, in one embodiment, calibration inaccuracies due to change (for example, rapid acceleration) of analyte levels after performing discrete calibration may be mitigated by updating the calibration routine taking into consideration the near immediate post calibration analyte sensor data to obtain a more reliable and accurate value associated with the rate of change of the monitored analyte levels. In one embodiment, the overall system accuracy of the monitored and detected analyte values may be improved. 
       FIG. 7  is a flowchart illustrating a method for estimating the lag time constant from sensor analyte data and reference measurements in accordance with one embodiment of the present disclosure. This method can be employed in real-time or offline, or components of the method may be one or the other. Referring to  FIG. 7 , measured analyte sensor data for example, from a transcutaneously positioned analyte sensor may be obtained and/or retrieved from a memory or storage device operatively coupled to the analyte sensor ( 710 ). Thereafter, a reference glucose data measurement is performed and received, for example, from a blood glucose meter ( 720 ). Each of the reference glucose data is paired with one or more measured analyte sensor data in the same time vicinity as the reference data ( 730 ). That is, the pairing may be to select a single analyte sensor data closest in time to the time of the reference data. Alternatively, the pairing could be a number of analyte sensor data occurring before and after the reference data. Alternatively, the pairing could be between a value that is the result of a calculation using a number of analyte sensor data occurring before and after the reference data; the calculation could be an average or some other appropriate form of filtering. 
     Referring back to  FIG. 7 , thereafter, these pairs are used to estimate the lag time constant ( 740 ). This estimate could be performed using standard system identification techniques. For instance, a two or more dimensional least squares technique may be used to estimate the time constant, along with the sensor sensitivity. One embodiment for estimating the time constant may separate the estimation of the sensitivity from the estimation of the lag time constant, and is illustrated in  FIG. 8 . 
     Once the estimate of the lag time constant is determined, it may be used to correct the sensor data for lag effects ( 750 ). In one aspect, calibration for lag effects may be corrected. In another aspect, each calibrated sensor data may be corrected for lag effects as described below. Given a calibrated sensor providing a continuous stream of interstitial glucose G i  , lag correction routine used to improve calibration may also be used to provide a blood glucose estimate G b . For example, consider a simple continuous time domain model of blood glucose-to-interstitial glucose dynamics: 
       τ Ġ   i ( t )=− G   i ( t )+ G   b ( t )
 
     where τ is the time constant, and Ġ i  is the rate of change of glucose level G i . Given that glucose G i  is available or determined by another module made possible by the latest calibration procedure, the rate of change of the glucose level Ġ i  may be determined in one or more retrospectively or in real-time. The time constant τ is available or computed by another module. 
     One real-time example to obtain a sampled data estimate of the rate of change Ġ i  at any time n is to use the average first difference of the past N sampled G i  data: 
     
       
         
           
             
               
                 
                   
                     G 
                     . 
                   
                   ^ 
                 
                 i 
               
                
               
                 ( 
                 n 
                 ) 
               
             
             ≈ 
             
               
                 
                   
                     ∑ 
                     
                       n 
                       = 
                       1 
                     
                     N 
                   
                    
                   
                       
                   
                    
                   
                     
                       G 
                       i 
                     
                      
                     
                       ( 
                       n 
                       ) 
                     
                   
                 
                 - 
                 
                   
                     G 
                     i 
                   
                    
                   
                     ( 
                     
                       n 
                       - 
                       1 
                     
                     ) 
                   
                 
               
               
                 NT 
                 s 
               
             
           
         
       
     
     where T s  is the sample time or the time interval between the sampled values of G i (n). The notation   signifies that it is an estimate for Ġ i . Other approaches to compute Ġ i  include any other Finite Impulse Response (FIR) filters, Infinite Impulse Response (IIR) filters, Wiener filter, and Kalman filter. 
     Given these available and/or computed values, blood glucose estimate G b  can be computed by rearranging the blood glucose-to-interstitial glucose model in the following manner: 
         Ġ   b ( n )=G i ( n )+τ   i ( n )
 
     where n is any time instance. Note that for improved noise rejection, the first term on the right hand side, G i (n), may be replaced by a filtered version. The filter can take any eligible forms such as the FIR filter, IIR filter, Wiener filter, or the Kalman filter. 
     In one embodiment, steps  730  and  740  are performed as a batch process. For instance, the receiver unit  104 , may perform the routines described at steps  730  and  740  periodically or continuously, after each reference measurement, with a batch of data saved on the receiver unit  104 . After each batch process, the lag time constant may be updated and used for subsequent real-time lag correction processes. Another embodiment may include the batch process to be performed external to the receiver unit in a computer terminal, for example. After the time constant is estimated, it may be downloaded or transmitted to the receiver unit to be used in subsequent real time lag correction processes. 
     In a further aspect, the routine illustrated in  FIG. 7  may be performed retrospectively, for example, using a computer or server terminal. This may be used in applications where lag correction operations may be performed retrospectively. 
       FIG. 8  illustrates estimation of the lag time constant from the pairs (step  740  of  FIG. 7 ) in one embodiment. The generation of a subset of pairs ( 810 ) may include generating a subset from pairs associated with sensor data absolute rate of change that is below a predetermined threshold. An example of the predetermined threshold includes 0.1 mg/dL/minute. An alternative predetermined threshold may be 6% change in the glucose estimate per minute. That is, the lag effects may be minimized if the rate of change is relatively low, and the subsequent estimation of the sensitivity ( 820 ) may be minimally affected by lag. Additionally, the included pairs may form a cluster of data in time. Other criteria for creating a subset of pairs may include a) data from a single sensor, b) data from a single subject, c) data from groups of patients or users, d) data associated with any particular conditions, such as data where all glucose is above some value, e) data that meets certain data quality conditions. 
     In the case where the criteria for generating a subset of pairs includes determining the glucose rate of change is below a predetermined glucose rate of change threshold, then the measured sensor data may be converted to preliminary glucose estimates using a preliminary sensitivity. The preliminary sensitivity may be determined by a variety of ways, including: the use of a nominal sensitivity assigned at the factory for a particular lot of sensors, the use of the median of the sensitivities calculated for each pair, or any other suitable means. In the case where the criteria includes a percentage of signal change, then the determination of the preliminary sensitivity is not needed. 
     The pair subset criterion in one aspect may include a condition that the data be from one sensor. In this embodiment, the sensitivity is estimated ( 820 ) for one sensor and the lag time constant is subsequently estimated ( 830 - 850 ). The receiver unit may subsequently use this time constant in real time to correct calibration and glucose calculations for lag effects. Further, this process may be repeated for multiple sensors, and the resulting multiple lag time constant estimates could be combined to create a single estimate used for lag correction. A simple combination calculation may include an average; other combination calculations, such as an FIR filter or a median or a time weighted average. 
     The pair subset criterion may alternatively include that the data must be from one patient, over multiple sensors. This subset may be further segregated by sensor when determining sensor sensitivity ( 820 ) and applying the determined sensitivity to the analyte sensor data ( 830 ). 
     Referring back to  FIG. 8 , the sensitivity of a subset of pairs is determined ( 820 ). In one aspect, a ratio for each reference data and associated sensor data (or value representing a combination of sensor data) are determined, and the median from this set of ratios is determined. 
     The sensitivity estimated is applied to the analyte sensor data ( 830 ) to generate the calibrated sensor data. Specifically, each sensor data point is divided by the sensitivity ratio to convert the sensor data from its native measurement units into glucose units. If the pair subsets ( 810 ) include data from multiple sensors, then the sensitivity estimated for each sensor is applied to the sensor data associated with that sensor. 
     Referring still to  FIG. 8 , the calibrated sensor data is paired with reference data ( 840 ). In a further aspect, another pair subset may be created to only include pairs where the glucose absolute rate of change based on the sensor exceeded a predetermined threshold, for example 0.75 mg/dL. In this case, the pairs associated with low glucose rate of change may be minimally informative to the lag time constant estimation. This may not be necessary to produce a result, but may be useful to reduce the amount of computation needed if this is desirable. 
     In step  850 , the pairs of calibrated sensor data and reference data are used to estimate the lag time constant. One embodiment of the time constant calculation is to assume that the interstitial fluid glucose level is connected to the blood glucose level by a simple first order differential expression: 
     
       
         
           
             
               τ 
                
               
                 
                   dI 
                   NAV 
                 
                 dt 
               
             
             = 
             
               
                 - 
                 
                   I 
                   NAV 
                 
               
               + 
               
                 
                   SG 
                   REF 
                 
                 . 
               
             
           
         
       
     
     Here, τ, is the time constant, I NAV  is the sensor data measurement, S is the ratio of sensor current to blood glucose concentration (sensitivity), and G REF  is a blood glucose reference measurement. This equation may be solved for τ if the values of the other quantities can be estimated. One approach for estimation of the sensor data measurement and its time rate of change may require filtering of the signal in order to reduce the effect of high frequency noise. In one aspect, the sensor data may be fit in a time interval around the reference point, G REF,  to a low order polynomial via the least squares algorithm. Another embodiment is to use a causal or noncausal low-pass filter. 
     If more than one reference point is available, then there will be multiple values of the time constant τ, and in which case, a least squares approach may be applied. 
     Accordingly, the identification of the sensitivity parameter may be isolated from that of the lag parameter. Moreover, in one aspect, data exclusion may be in the routine illustrated in  FIG. 8 . In one embodiment, a paired point exclusion may be added just after calibrated sensor data is paired with reference data, where the exclusion criteria may be a constraint appropriate for improving the estimate of lag. For instance, if the lag time constant at high glucose values is desirable to determine, then a step could be added to exclude pairs associated with a reference below a predetermined glucose threshold. 
     In this manner, the time lag constant determination associated with an analyte sensor for lag correction may be implemented in a real time monitoring system such as one aspect of the analyte monitoring system 100, or alternatively, may be used as an analysis tool, for example, by a patient, a physician or a health care provider to improve the treatment of the patient based on improved physiological data associated with the patient with minimal errors. 
     Indeed, in one aspect, lag compensation of analyte sensor measurements may be tuned or adjusted to the particular analyte sensor in use by the patient or the user, resulting in improved accuracy of the overall analyte monitoring system  100 . 
     In a further aspect, in addition to the time lag or time constant compensation, the lag correction approach may also include a time shift component in determining the interstitial and/or blood glucose level in continuous glucose monitoring systems. Further, one or more filter parameters may be adjusted to vary analyte sensor noise conditions to maximize the effectiveness of the filter in conditioning the sensor signal. That is, by determining or estimating the time shift component, time constant component, signal filter components, as well as analyte sensor calibration parameters, accuracy of the sensor signals in continuous glucose monitoring systems may be achieved. 
     More specifically, in one aspect, a model may be defined which describes the dynamic relationship between the interstitial glucose and blood glucose, where one or more parameters are identified for each analyte sensor. Using a plurality of reference blood glucose measurements (for example, using an in vitro blood glucose meter), and the raw sensor signals from the analyte monitoring system  100  ( FIG. 1 ), the one or more parameters may be identified using a combination of a simultaneous solution of least squares error estimate for the continuously variable parameters, within a layered loop of discretely variable parameters, such that the optimized or optimal parameter values may be ascertained. 
     In one aspect, the lag compensation technique described may be performed retrospectively to improve the accuracy of the continuously monitored and determined interstitial and/or blood glucose estimate. In addition, in a further aspect, the analyte monitoring system  100  may be configured to implement the technique described herein to monitor and refine the relevant parameters over time, during the course of the continuous monitoring of the glucose level using the analyte monitoring system, and as the availability of reference blood glucose measurements (for example, at times of sensor calibration, sensor accuracy confirmation, etc) increases over times. 
     In one aspect, the capillary to interstitial glucose dynamics may be modeled as follows: 
         G   rI ( t )= S   1   G   I ( t )+ S   0   , τĠ   I ( t )=− G   I ( t )+ G   C ( t−δ )
 
     where τ (time constant) and δ (time shift) are the two main parameters of interest. The variable G represents glucose, and Ġ its time rate of change. I subscripts correspond to interstitial glucose, and C subscripts to capillary glucose. The first equation models the static relationship of raw interstitial glucose (G rI ), in arbitrary units, to calibrated interstitial glucose (G I  ). The second equation models the dynamic relationship between capillary and interstitial glucose. Two variations of the model will be considered. The first model does not restrict S 0 , while the second assumes S 0 =0. 
     Using the model described above that includes the time constant component and the time shift component, for each analyte sensor, an optimal (τ, δ) pair may be determined based, for example, on a linear relationship where the sum of the time constant (τ), and the time shift (δ) components equal to the total lag time between the capillary and interstitial glucose values, for example, approximately 13 minutes. Accordingly, in one aspect, when the analyte sensor data is received from the analyte monitoring system, for example, the total time lag may be compensated. 
     It is to be noted that, each step or routine associated each of the flowcharts illustrating the various embodiments of the present disclosure as shown in  FIG. 4-8  do not have to be performed in the order shown in the Figures, unless described otherwise, and within the scope of the present disclosure, one or more step or routine in one or more figures described above may be performed or executed in an order different from the order shown in the respective figures. 
     Referring to the Figures above, in particular embodiments, the lag correction and calibration and updating of monitored sensor data may be performed by one or more processing units of the one or more receiver unit ( 104 ,  106 ) the transmitter unit  102  or the data processing terminal/infusion section  105 . In addition, the one or more of the transmitter unit  102 , the primary receiver unit  104 , secondary receiver unit  106 , or the data processing terminal/infusion section  105  may also incorporate a blood glucose meter functionality, such that, the housing of the respective one or more of the transmitter unit  102 , the primary receiver unit  104 , secondary receiver unit  106 , or the data processing terminal/infusion section  105  may include a test strip port configured to receive a blood sample for determining one or more blood glucose levels of the patient. 
     In a further embodiment, the one or more of the transmitter unit  102 , the primary receiver unit  104 , secondary receiver unit  106 , or the data processing terminal/infusion section  105  may be configured to receive the blood glucose value wirelessly over a communication link from, for example, a glucose meter. In still a further embodiment, the user or patient manipulating or using the analyte monitoring system  100  ( FIG. 1 ) may manually input the blood glucose value using, for example, a user interface (for example, a keyboard, keypad, and the like) incorporated in the one or more of the transmitter unit  102 , the primary receiver unit  104 , secondary receiver unit  106 , or the data processing terminal/infusion section  105 . 
     A computer implemented method in accordance with one embodiment includes determining a rate of change of an analyte level monitored by an analyte sensor below a predetermined threshold, calibrating analyte data associated with the monitored analyte level received from analyte sensor based on a reference measurement, determining a time lag constant associated with the calibrated analyte data, and performing lag correction of the calibrated analyte data based on the determined time lag constant. 
     The method may include outputting the lag corrected calibrated analyte data. 
     In one aspect, the reference measurement may include a blood glucose measurement. 
     The reference measurement may be obtained within a predetermined time period relative to the monitored analyte level, where the predetermined time period may be less than approximately 30 minutes or greater than approximately 30 minutes, or any other suitable time range. 
     In one aspect, calibrating the analyte data may include determining a sensitivity value associated with the analyte sensor, where the sensitivity value may include a ratio of the analyte data and the corresponding reference measurement. 
     The analyte level may include glucose level. 
     In a further aspect, determining the rate of change may include determining a rate of increase or a rate of decrease of the monitored analyte level for a predefined time period. 
     An apparatus in accordance with one embodiment may include one or more processing units, and a memory for storing instructions which, when executed by the one or more processors, causes the one or more processing units to determine a rate of change of an analyte level monitored by an analyte sensor below a predetermined threshold, calibrate analyte data associated with the monitored analyte level received from analyte sensor based on a reference measurement, determine a time lag constant associated with the calibrated analyte data, and perform lag correction of the calibrated analyte data based on the determined time lag constant. 
     The memory may be further configured for storing instructions which, when executed by the one or more processing units, causes the one or more processing units to output the lag corrected calibrated analyte data. 
     In a further aspect, the apparatus may include a communication module operatively coupled to the one or more processing units, the communication module configured to receive the reference measurement within a predetermined time period relative to the monitored analyte level, where the predetermined time period may be less than approximately 30 minutes or greater than approximately 30 minutes, or any other suitable time period. 
     In a further aspect, the memory may be further configured for storing instructions which, when executed by the one or more processing units, causes the one or more processing units to determine a sensitivity value associated with the analyte sensor, where the sensitivity value may include a ratio of the analyte data and the corresponding reference measurement. 
     The memory may be further configured for storing instructions which, when executed by the one or more processing units, causes the one or more processing units to determine the rate of change based on determining a rate of increase or a rate of decrease of the monitored analyte level for a predefined time period. 
     A physiological monitoring device in still another aspect may include means for determining a rate of change of an analyte level monitored by an analyte sensor below a predetermined threshold, means for calibrating analyte data associated with the monitored analyte level received from analyte sensor based on a reference measurement, means for determining a time lag constant associated with the calibrated analyte data, and means for performing lag correction of the calibrated analyte data based on the determined time lag constant. 
     A method in accordance with one embodiment of the present disclosure includes obtaining a reference data point at a first predetermined time, receiving a first data at the first predetermined time, calibrating the first data based on the reference data point, receiving a second data at a second predetermined time, updating the calibrated first data based on the second data, and calibrating the second data. 
     The reference data point may include a blood glucose value. 
     The first predetermined time may include a calibration time associated with the calibration of one or more of the first data or the second data. 
     The first data and the second data may include a respective one of a monitored analyte value. 
     In one embodiment, calibrating the first data may include determining a first rate of change of the first data at the first predetermined time, and performing a first lag compensation of the first data based on the first rate of change to generate a first lag compensated first data. In a further embodiment, calibrating the first data may include determining a first calibration parameter associated with the first data based on the reference data point and the first lag compensated first data, and generating a calibrated first data based on the first calibration parameter and the first lag compensated first data. 
     Updating the calibrated first data in one embodiment may include determining a second rate of change of the first data at the first predetermined time based on the second data, and performing a second lag compensation of the first data based on the second rate of change of the first data to generate a second lag compensated first data. 
     Also, calibrating the second data may include determining a second calibration parameter associated with the first data based on the reference data point and the second lag compensated first data, and generating a calibrated second data based on the second calibration parameter and the second lag compensated first data. 
     A method in accordance with another embodiment may include determining a calibration parameter associated with a detected analyte value, calibrating the analyte value based on the calibration parameter, and dynamically updating the calibration parameter. 
     The method in another aspect may include calibrating a second detected analyte value based on the dynamically updated calibration parameter. 
     Further, dynamically updating the calibration parameter may also include determining a rate of change of the detected analyte value, and generating a lag compensated analyte value based on the rate of change. 
     In addition, calibrating the analyte value may further include determining a sensitivity associated with the detected analyte value, and applying the sensitivity to the lag compensated analyte value. 
     Moreover, in still another embodiment, dynamically updating the calibration parameter may include updating the rate of change of the detected analyte value, and updating the lag compensated analyte value, where updating the rate of change may include determining the rate of change of the detected analyte value between a first predetermined time and a second predetermined time. 
     In still another embodiment, calibrating the analyte value may include detecting a calibration data, determining a sensitivity based on the calibration data and the lag compensated analyte value, and generating a lag compensated and calibrated analyte value. 
     An apparatus in accordance with another embodiment may include one or more processing units, and a memory for storing instructions which, when executed by the one or more processors, causes the one or more processing units to obtain a reference data point at a first predetermined time, receive a first data at the first predetermined time, calibrate the first data based on the reference data point; receive a second data at a second predetermined time; update the calibrated first data based on the second data; and calibrate the second data. 
     The memory in another aspect may be configured for storing instructions which, when executed by the one or more processing units, causes the one or more processing units to determine a first rate of change of the first data at the first predetermined time, and to perform a first lag compensation of the first data based on the first rate of change to generate a first lag compensated first data. 
     Moreover, the memory in yet another embodiment may be further configured for storing instructions which, when executed by the one or more processing units, causes the one or more processing units to determine a first calibration parameter associated with the first data based on the reference data point and the first lag compensated first data and to generate a calibrated first data based on the first calibration parameter and the first lag compensated first data. 
     Additionally, the memory may still be further configured for storing instructions which, when executed by the one or more processing units, causes the one or more processing units to determine a second rate of change of the first data at the first predetermined time based on the second data, and to perform a second lag compensation of the first data based on the second rate of change of the first data to generate a second lag compensated first data. 
     In yet still another aspect, the memory may be further configured for storing instructions which, when executed by the one or more processing units, causes the one or more processing units to determine a second calibration parameter associated with the first data based on the reference data point and the second lag compensated first data, and to generate a calibrated second data based on the second calibration parameter and the second lag compensated first data. 
     A method in accordance with still another embodiment of the present disclosure includes, dynamically, and in particular embodiments, in real-time, obtaining reference data at a first predetermined time, receiving measured data prior to and including (or near) the first predetermined time, calculating a first calibration parameter (or parameters) using the data, calibrating the measured data based on the calibration parameter, receiving measured data at a second predetermined time, updating the calibration parameter based on all of the previous data and the newly received measured data, calibrating the newly received measured data based on the updated calibration parameter, and repeating a number of times the process of receiving new measurement data, updating the calibration parameter, calibrating the newly received measurement data, and calibrating any newly received measurement data with the fully updated calibration parameter. 
     A method in a further embodiment includes performing lag compensation on the measured data that is used to update the calibration parameter. Lag compensation may optionally be performed on the measured data that is calibrated. A method in a further embodiment includes filtering the measured data that is used to update the calibration parameter. 
     An apparatus in accordance with yet still another embodiment includes one or more processing units, and a memory for storing instructions which, when executed by the one or more processors, causes the one or more processing units to dynamically, and in particular embodiments, in real-time, obtain reference data at a first predetermined time, retrieve measured data prior to and including (or near) the first predetermined time, calculate a first calibration parameter (or parameters) using the data, calibrate the measured data based on the calibration parameter, retrieve measured data at a second predetermined time, update the calibration parameter based on all of the previous data and the newly received measured data, calibrate the newly received measured data based on the updated calibration parameter, and repeat a number of times the process of receiving new measurement data, updating the calibration parameter, calibrating the newly received measurement data, and calibrating any newly received measurement data with the fully updated calibration parameter. 
     A method in still a further embodiment may include calibrating analyte data associated with a monitored analyte level received from an analyte sensor based on a reference measurement, determining a time constant component and a time shift component associated with the calibrated analyte data, and estimating a lag time based on the determined time constant component and the time shift component. 
     The method in one aspect may include performing lag correction of the calibrated analyte data based on the estimated lag time. 
     The method may also include outputting the lag corrected calibrated analyte data. 
     The reference measurement may include a blood glucose measurement. 
     In one aspect, the reference measurement may be obtained within a predetermined time period relative to the monitored analyte level. 
     In still another aspect, calibrating the analyte data may include determining a sensitivity value associated with the analyte sensor. 
     An apparatus in still yet a further aspect may include one or more processors, and a memory coupled to the one or more processors, the memory for storing instructions which, when executed by the one or more processors, causes the one or more processors to calibrate analyte data associated with a monitored analyte level received from an analyte sensor based on a reference measurement, determine a time constant component and a time shift component associated with the calibrated analyte data, and estimate a lag time based on the determined time constant component and the time shift component. 
     In yet another aspect, the memory for storing instructions which, when executed by the one or more processes, may cause the one or more processors to perform lag correction of the calibrated analyte data based on the estimated lag time. 
     Various other modifications and alterations in the structure and method of operation of this invention will be apparent to those skilled in the art without departing from the scope and spirit of the invention. Although the invention has been described in connection with specific preferred embodiments, it should be understood that the invention as claimed should not be unduly limited to such specific embodiments. It is intended that the following claims define the scope of the present disclosure and that structures and methods within the scope of these claims and their equivalents be covered thereby.