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WO2010054408A1 - Procédé et système pour permettre une détection de perte de niveau dans des capteurs de substances à analyser - Google Patents

Procédé et système pour permettre une détection de perte de niveau dans des capteurs de substances à analyser Download PDF

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Publication number
WO2010054408A1
WO2010054408A1 PCT/US2009/063936 US2009063936W WO2010054408A1 WO 2010054408 A1 WO2010054408 A1 WO 2010054408A1 US 2009063936 W US2009063936 W US 2009063936W WO 2010054408 A1 WO2010054408 A1 WO 2010054408A1
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WIPO (PCT)
Prior art keywords
processors
analyte
signal
dropout
data stream
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Ceased
Application number
PCT/US2009/063936
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English (en)
Inventor
Marc Barry Taub
Kenneth J. Doniger
Quan SHEN
Si-zhao QIN
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Abbott Diabetes Care Inc
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Abbott Diabetes Care Inc
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Filing date
Publication date
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Publication of WO2010054408A1 publication Critical patent/WO2010054408A1/fr
Anticipated expiration legal-status Critical
Ceased legal-status Critical Current

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Classifications

    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/0002—Remote monitoring of patients using telemetry, e.g. transmission of vital signals via a communication network
    • A61B5/0031—Implanted circuitry
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/145—Measuring characteristics of blood in vivo, e.g. gas concentration or pH-value ; Measuring characteristics of body fluids or tissues, e.g. interstitial fluid or cerebral tissue
    • A61B5/14532—Measuring characteristics of blood in vivo, e.g. gas concentration or pH-value ; Measuring characteristics of body fluids or tissues, e.g. interstitial fluid or cerebral tissue for measuring glucose, e.g. by tissue impedance measurement
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/145—Measuring characteristics of blood in vivo, e.g. gas concentration or pH-value ; Measuring characteristics of body fluids or tissues, e.g. interstitial fluid or cerebral tissue
    • A61B5/1468—Measuring characteristics of blood in vivo, e.g. gas concentration or pH-value ; Measuring characteristics of body fluids or tissues, e.g. interstitial fluid or cerebral tissue using chemical or electrochemical methods, e.g. by polarographic means
    • A61B5/1486—Measuring characteristics of blood in vivo, e.g. gas concentration or pH-value ; Measuring characteristics of body fluids or tissues, e.g. interstitial fluid or cerebral tissue using chemical or electrochemical methods, e.g. by polarographic means using enzyme electrodes, e.g. with immobilised oxidase
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B2560/00—Constructional details of operational features of apparatus; Accessories for medical measuring apparatus
    • A61B2560/02—Operational features
    • A61B2560/0223—Operational features of calibration, e.g. protocols for calibrating sensors
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B2560/00—Constructional details of operational features of apparatus; Accessories for medical measuring apparatus
    • A61B2560/02—Operational features
    • A61B2560/0266—Operational features for monitoring or limiting apparatus function
    • A61B2560/0276—Determining malfunction
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/145—Measuring characteristics of blood in vivo, e.g. gas concentration or pH-value ; Measuring characteristics of body fluids or tissues, e.g. interstitial fluid or cerebral tissue
    • A61B5/1495—Calibrating or testing of in-vivo probes
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
    • A61B5/7203—Signal processing specially adapted for physiological signals or for diagnostic purposes for noise prevention, reduction or removal
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
    • A61B5/7235—Details of waveform analysis
    • A61B5/7253—Details of waveform analysis characterised by using transforms
    • A61B5/726—Details of waveform analysis characterised by using transforms using Wavelet transforms

Definitions

  • Diabetes Mellitus is chronic, costly, increasingly prevalent, and emerges as an alarming public health challenge because of its burden of microvascular complications and contribution to cardiovascular disease. Diabetes occurs due to inadequate control of blood glucose level or glycemia. High blood glucose levels, or hyperglycemia, may cause damage to the retina, kidneys, nerves, and circulatory system, resulting in, among others, blindness, renal disease, and limb amputations. On the other hand, low blood glucose levels, or hypoglycemia, may have acute effects such as fainting, coma, and even death.
  • Controlling blood glucose levels within tight limits near the normal blood glucose levels can drastically reduce the health complications associated with diabetes.
  • Commercially available testing kits and systems exist to control glucose levels including discrete in vitro blood glucose testing using blood glucose meters, or periodic or continuous in vivo monitoring of glucose levels using continuous glucose monitoring systems (CGM) that use subcutaneously or transcutaneously positioned analyte sensors, such as glucose sensors, over a predetermined time period such as five days.
  • CGM continuous glucose monitoring systems
  • Such CGM systems which allow the user or the patient to continuously monitor the fluctuation in the level of glucose, inform the user or the patient of such fluctuation or variation including, providing warnings of actual or impending hyperglycemia or hypoglycemia.
  • Certain in vivo analyte sensors experience signal dropout conditions or false indications of the detected signal level, where the signal output indication does not correlate with the actual monitored or detected analyte level.
  • signal dropouts for example, a dropout of a sequence or series of points
  • These signal dropouts may be caused by extraordinary changes, such as extra pressure which may reduce the capillary blood transportation around the location where the sensor is implanted.
  • these changes in the measurements of the analyte sensors do not reflect the actual glucose level and may result in significant challenges in real time glucose level calculation, including sensor calibration and/or lag correction.
  • the signal dropout detector may be configured to provide a notification or a warning to the user or the patient, and further, in substantially real time upon detection of the signal dropout condition, such that proper control actions or therapy decisions may be made to maintain the patient's glucose level within acceptable or desired limits.
  • FIG. 1 shows a block diagram of an embodiment of a data monitoring and management system with which a sensor according to the present disclosure is usable;
  • FIG. 2 shows a block diagram of an embodiment of the data processing unit of the data monitoring and management system of FIG. 1;
  • FIG. 3 shows a block diagram of an embodiment of the receiver/monitor unit of the data monitoring and management system of FIG. 1;
  • FIG. 4 is a diagram illustrating discrete wavelet decomposition
  • FIG. 5 is a diagram illustrating frequencies of components at different levels in discrete wavelet transform
  • FIG. 6A illustrates a Haar wavelet for use with a discrete wavelet transform based signal dropout detector in one aspect of the present disclosure
  • FIG. 6B illustrates examples of various wavelets for use with a discrete wavelet transform based signal dropout detector in various embodiments of the present disclosure
  • FIG. 7 is a flow chart illustrating a recursive algorithm for detecting signal dropouts with discrete wavelet transform in one embodiment of the present disclosure
  • FIG. 8 illustrates a moving window of the recursive algorithm of FIG. 7 in one embodiment
  • FIG. 9 A illustrates a signal drop which activates a warning trigger of a possible/potential signal dropout condition in one embodiment
  • FIG. 9B illustrates a confirmation of a detected signal dropout in one embodiment
  • FIG. 9C illustrates the complete signal dropout detected in FIG. 9B
  • FIG. 10 illustrates a distribution of the mean glucose variation compared to the mean (dG) of the signal dropouts in one embodiment of the present disclosure
  • FIG. 11 illustrates an example of an exponential-shape dropout in one embodiment
  • FIGS. 12 and 13 are graphical illustrations of signal dropout detections in one embodiment
  • FIG. 14 illustrates a determination of true (or confirmed) signal dropouts in sensor signals in one embodiment
  • FIG. 15 illustrates a wavelet decomposition of analyte sensor measurements in one embodiment of the present disclosure
  • FIG. 16 illustrates a comparison between a sum of details of analyte sensor signals and a single detail level of the analyte sensor signals in one embodiment
  • FIG. 17 is a graphical illustration of a valley of an analyte sensor signals in one aspect of the present disclosure
  • FIGS. 18 and 19 illustrate derivatives of analyte sensor signals for use in refining boundaries of a valley of the analyte sensor signals in one embodiment
  • FIG. 20 illustrates the effect of "back searching" in dropout detection in one embodiment
  • FIGS. 21A and 21B are graphical and tabular illustrations, respectively of the results obtained from the signal dropout detector in accordance with one aspect
  • FIGS. 22-26 graphically illustrate a pattern of signal dropouts preceded by a small, fast peak in the signal
  • FIGS. 27 and 28 are graphical illustrations of signal dropout detection in one embodiment
  • FIGS. 29 and 30 are graphical illustrations of the signal dropout detector in another embodiment
  • FIG. 31 illustrates monitored signal levels from an analyte sensor in embodiment of the present disclosure
  • FIG. 32 illustrates the corresponding detected signal dropouts in the monitored analyte sensor signals of FIG. 31.
  • FIGS 33-35 illustrate the relationship between signal-noise ratio (SNR), drop variation-noise variation ratio (DNR) and detected signal dropouts in one embodiment of the present disclosure.
  • SNR signal-noise ratio
  • DNR drop variation-noise variation ratio
  • embodiments of the present disclosure relate to methods and devices for detecting at least one analyte, such as glucose, in body fluid.
  • Embodiments relate to the continuous and/or automatic in vivo monitoring of the level of one or more analytes using a continuous analyte monitoring system that includes an analyte sensor for the in vivo detection, of an analyte, such as glucose, lactate, and the like, in a body fluid.
  • Embodiments include wholly implantable analyte sensors and analyte sensors in which only a portion of the sensor is positioned under the skin and a portion of the sensor resides above the skin, e.g., for contact to a control unit, transmitter, receiver, transceiver, processor, etc.
  • At least a portion of a sensor may be, for example, subcutaneously positionable in a patient for the continuous or semi-continuous monitoring of a level of an analyte in a patient's interstitial fluid.
  • semi-continuous monitoring and continuous monitoring will be used interchangeably, unless noted otherwise.
  • the sensor response may be correlated and/or converted to analyte levels in blood or other fluids.
  • an analyte sensor may be positioned in contact with interstitial fluid to detect the level of glucose, which detected glucose may be used to infer the glucose level in the patient's bloodstream.
  • Analyte sensors may be insertable into a vein, artery, or other portion of the body containing fluid.
  • Embodiments of the analyte sensors of the subject disclosure may be configured for monitoring the level of the analyte over a time period which may range from minutes, hours, days, weeks, or longer.
  • temperature, perspiration or other characteristics of a patient such as, for example, other measurable characteristics are monitored concurrently with the monitored analyte level, and used to, in one embodiment, either confirm or reject notifications associated with the medically significant condition such as the onset or impending hypoglycemic condition initially detected based on the monitored analyte level.
  • the hypoglycemic condition may be associated with a low blood glucose level such as, for example, 40-50 mg/dL or less (depending upon, for example, age, gender, and the like).
  • alarms or notifications may be configured, as a default setting or programmed specific to each patient, to be triggered when the monitored glucose level decreases at a rate that approaches the hypoglycemic condition within a defined time period to enable the patient or the user (or the healthcare provider) to timely take corrective actions.
  • each alarm or notification may be programmed to be asserted or triggered when the monitored glucose level reaches approximately 80 to 100 mg/dL, and decreasing at a rate of 2 mg/dL/minute or more.
  • analyte monitoring system 100 such as, for example, an analyte (e.g., glucose) monitoring system 100 in accordance with certain embodiments.
  • analyte e.g., glucose
  • Embodiments of the subject disclosure are further described primarily with respect to glucose monitoring devices and systems, and methods of glucose detection, for convenience only and such description is in no way intended to limit the scope of the disclosure. It is to be understood that the analyte monitoring system may be configured to monitor a variety of analytes instead of or in addition to glucose, e.g., at the same time or at different times.
  • Analytes that may be monitored include, but are not limited to, acetyl choline, amylase, bilirubin, cholesterol, chorionic gonadotropin, creatine kinase (e.g., CK- MB), creatine, creatinine, DNA, fructosamine, glucose, glutamine, growth hormones, hormones, ketone bodies, lactate, oxygen, peroxide, prostate-specific antigen, prothrombin, RNA, thyroid stimulating hormone, and troponin.
  • 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 data processing unit 102 connectable to the sensor 101, and a primary receiver unit 104 which is configured to communicate with the data processing 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 to evaluate or otherwise process or format data received by the primary receiver unit 104.
  • the data processing terminal 105 may be configured to receive data directly from the data processing unit 102 via a communication link which may optionally be configured for bi-directional communication.
  • the data processing unit 102 may include a transmitter or a transceiver to transmit and/or receive data to and/or from the primary receiver unit
  • the electrochemical sensors of the present disclosure may employ any suitable measurement technique, e.g., may detect current, may employ potentiometry, etc.
  • sensing systems may be optical, colorimetric, and the like.
  • an optional secondary receiver unit 106 which is operatively coupled to the communication link 103 and configured to receive data transmitted from the data processing unit 102.
  • the secondary receiver unit 106 may be configured to communicate with the primary receiver unit 104, as well as the data processing terminal 105.
  • the secondary receiver unit 106 may be configured for bidirectional wireless communication with each of the primary receiver unit 104 and the data processing terminal 105.
  • the secondary receiver unit 106 may be a de-featured receiver as compared to the primary receiver 104, i.e., the secondary receiver 106 may include a limited or minimal number of functions and features as compared with the primary receiver unit 104.
  • the secondary receiver unit 106 may include a smaller (in one or more, including all, dimensions), compact housing or embodied in a device such as a wrist watch, arm band, etc., for example.
  • the secondary receiver unit 106 may be configured with the same or substantially similar functions and features as the primary receiver unit 104.
  • the secondary receiver unit 106 may include a docking portion to be mated with a docking cradle unit for placement by, e.g., the bedside for nighttime monitoring, and/or a bi-directional communication device.
  • a docking cradle may recharge a powers supply.
  • the analyte monitoring system 100 may include more than one sensor 101 and/or more than one data processing unit 102, and/or more than one data processing terminal 105. Multiple sensors may be positioned in a patient for analyte monitoring at the same or different times. In certain embodiments, analyte information obtained by a first positioned sensor may be employed as a comparison to analyte information obtained by a second sensor. This may be useful to confirm or validate analyte information obtained from one or both of the sensors. Such redundancy may be useful if analyte information is contemplated in critical therapy-related decisions.
  • the analyte monitoring system 100 may be a continuous monitoring system or semi-continuous.
  • each component may be configured to be uniquely identified by one or more of the other components in the system so that communication conflict may be readily resolved between the various components within the analyte monitoring system 100.
  • unique identification codes IDs
  • communication channels and the like, may be used.
  • the senor 101 is physically positioned in and/or on the body of a user whose analyte level is being monitored.
  • the sensor 101 may be configured to continuously or semi-continuously sample the analyte level of the user automatically (without the user initiating the sampling), based on a programmed intervals such as, for example, but not limited to, once every minute, once every five minutes and so on, and convert the sampled analyte level into a corresponding signal for transmission by the data processing unit 102.
  • the data processing unit 102 is coupleable to the sensor 101 so that both devices are positioned in or on the user's body, with at least a portion of the analyte sensor 101 positioned transcutaneous Iy.
  • the data processing unit may include a fixation element such as adhesive or the like to secure it to the user's body.
  • a mount (not shown) attachable to the user and mateable with the unit 102 may be used.
  • a mount may include an adhesive surface.
  • the data processing unit 102 performs data processing functions, where such functions may include but are not limited to, filtering and encoding of 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.
  • the sensor 101 or the data processing unit 102 or a combined sensor/data processing unit may be wholly implantable under the skin layer of the user.
  • the primary receiver unit 104 may include a signal interface section including and RF receiver and an antenna that is configured to communicate with the data processing unit 102 via the communication link 103, and a data processing section for processing the received data from the data processing unit 102 such as data decoding, error detection and correction, data clock generation, data bit recovery, etc., or any combination thereof.
  • the primary receiver unit 104 in certain embodiments is configured to synchronize with the data processing unit 102 to uniquely identify the data processing unit 102, based on, for example, an identification information of the data processing unit 102, and thereafter, to continuously or semi-continuously receive signals transmitted from the data processing unit 102 associated with the monitored analyte levels detected by the sensor 101.
  • 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 phone (e.g., a multimedia and Internet-enabled mobile phone such as an iPhone, Blackberry device or similar phone), mp3 player, pager, global position system (GPS), drug delivery device, 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, updating, and/or analyzing data corresponding to the detected analyte level of the user.
  • a data network not shown
  • 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 insulin to patients, and which may be configured to communicate with the primary receiver unit
  • the primary receiver unit 104 for receiving, among others, the measured analyte level.
  • the primary receiver unit 104 may be configured to integrate an infusion device therein so that the primary receiver unit 104 is configured to administer insulin (or other appropriate drug) 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 data processing unit 102.
  • An infusion device may be an external device or an internal device (wholly implantable in a user).
  • the data processing terminal 105 which may include an insulin pump, may be configured to receive the analyte signals from the data processing unit 102, and thus, incorporate the functions of the primary receiver unit 104 including data processing for managing the patient's insulin therapy and analyte monitoring.
  • the communication link 103 as well as one or more of the other communication interfaces shown in FIG. 1, may use one or more of: an RF communication protocol, an infrared communication protocol, a Bluetooth enabled communication protocol, an 802.1 Ix wireless communication protocol, or an equivalent wireless communication protocol which would allow secure, wireless communication of several units (for example, per HIPPA requirements), while avoiding potential data collision and interference.
  • FIG. 2 shows a block diagram of an embodiment of a data processing unit of the data monitoring and detection system shown in FIG. 1.
  • the data processing unit 102 thus may include one or more of an analog interface 201 configured to communicate with the sensor 101 (FIG. 1), a user input 202, and a temperature measurement section 203, each of which is operatively coupled to a processor 204 such as a central processing unit (CPU).
  • a processor 204 such as a central processing unit (CPU).
  • CPU central processing unit
  • User input and/or interface components may be included or a data processing unit may be free of user input and/or interface components.
  • one or more application-specific integrated circuits (ASIC) may be used to implement one or more functions or routines associated with the operations of the data processing unit (and/or receiver unit) using for example one or more state machines and buffers.
  • ASIC application-specific integrated circuits
  • a transmitter serial communication section 205 and an RF transmitter 206 each of which is also operatively coupled to the processor 204.
  • the RF transmitter 206 may be configured as an RF receiver or an RF transmitter/receiver, such as a transceiver, to transmit and/or receive data signals.
  • a power supply 207 such as a battery, may also be provided in the data processing unit 102 to provide the necessary power for the data processing unit 102.
  • clock 208 may be provided to, among others, supply real time information to the processor 204.
  • the sensor unit 101 includes four contacts, three of which are electrodes - working 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 data processing unit 102.
  • reference electrode (R) 212, and counter electrode (C) 213 may be made using a non-corroding conductive material that may be applied by, e.g., chemical vapor deposition (CVD), physical vapor deposition, sputtering, reactive sputtering, printing, coating, ablating (e.g., laser ablation), painting, dip coating, etching, and the like.
  • CVD chemical vapor deposition
  • Materials include, but are not limited to, carbon (such as graphite), gold, iridium, ruthenium, palladium, platinum, rhenium, rhodium, silver, mixtures thereof, and alloys thereof, and metallic oxides, like ruthenium dioxide or iridium dioxide, of these elements.
  • a unidirectional input path is established from the sensor 101 (FIG. 1) and/or manufacturing and testing equipment to the analog interface 201 of the data processing unit 102, while a unidirectional output is established from the output of the RF transmitter 206 of the data processing unit 102 for transmission to the primary receiver unit 104.
  • 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.
  • the data processing unit 102 is configured to transmit to the primary receiver unit 104 (FIG. 1), via the communication link 103 (FIG.
  • the processor 204 may be configured to transmit control signals to the various sections of the data processing unit 102 during the operation of the data processing unit 102.
  • the processor 204 also includes memory (not shown) for storing data such as the identification information for the data processing 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 processor 204.
  • the power supply 207 may include a commercially available battery.
  • the data processing unit 102 is also configured such that the power supply section 207 is capable of providing power to the data processing unit 102 for a minimum period of time, e.g., at least about one month, e.g., at least about three months or more, of continuous operation.
  • the minimum time period may be after (i.e., in addition to), a period of time, e.g., up to about eighteen months, of being stored in a low- or no- power (non-operating) mode. In certain embodiments, this may be achieved by the processor 204 operating in low power modes in the non- operating state, for example, drawing no more than minimal current, e.g., approximately 1 ⁇ A of current or less. In certain embodiments, a manufacturing process of the data processing unit 102 may place the data processing unit 102 in the lower power, non-operating state (i.e., post-manufacture sleep mode). In this manner, the shelf life of the data processing unit 102 may be significantly improved. Moreover, as shown in FIG.
  • 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 data processing unit 102 shown in FIG. 2.
  • the power supply section 207 of the data processing 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 data processing unit 102 may be powered for a longer period of usage time.
  • the data processing unit 102 may be configured without a battery in the power supply section 207, in which case the data processing unit 102 may be configured to receive power from an external power supply source (for example, a battery, electrical outlet, etc.) as discussed in further detail below.
  • an external power supply source for example, a battery, electrical outlet, etc.
  • a temperature detection section 203 of the data processing 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 temperature measurement or reading generated from the temperature detection section 203 may be used in conjunction with the received analyte data to determine or confirm a monitored condition such as an impending or onset of hypoglycemic condition as discussed in further detail below.
  • the temperature measurement section may include a thermistor to monitor the on-skin (or ambient) temperature in direct or indirect contact with the patient's skin.
  • Example embodiments of temperature measurement section are provided in, for example, US Patent No. 6,175,752, and application no.
  • the temperature measurement or reading may be generated or determined from a different area of the body such as the ear canal, rectum, mouth, other body cavity, or forehead using a suitable temperature measuring device or components which incorporate the temperature measurement functionalities and capable of transmitting (wirelessly or via wired connection) the determined temperature information to the receiver unit 104/106 (FIG.1) and/or data processing terminal/infusion section 105 (FIG. 1) for further processing.
  • the data processing unit 102 may also include a condition monitoring unit 215 in signal communication with the processor 204, and configured to monitor one or more physiological or other characteristics of the patient or the user of the data processing unit 102.
  • the perspiration level may be monitored by the condition monitoring unit 215 in one embodiment by detecting or determining conductance signal levels that vary depending upon the presence or absence of perspiration on skin, for example, using electrodes or probes or contacts on the skin of the patient.
  • the electrodes, probes or contacts to determine or monitor the one or more physiological characteristics such as level of perspiration may be provided on the housing the data processing unit 102, or alternatively, may be provided as a separate unit that is configured to provide or transfer the monitored characteristics information or data to the processor 204 of the data processing unit 102.
  • the microprocessor based logic provided to the processor 204 may be configured to process the detected conductance signal levels to determine the presence of absence of perspiration and/or, to determine the level of and change in perspiration based on, for example, monitored or detected conductance signal level.
  • the RF transmitter 206 of the data processing unit 102 may be configured for operation in a certain frequency band, e.g., the frequency band of 315 MHz to 322 MHz, for example, in the United States.
  • the operating frequency band may vary depending upon the location of use, communication protocol used, components used to implement the RF communication, and accordingly, the present disclosure contemplates varying ranges of operating frequency bands.
  • the RF transmitter 206 is configured to modulate the carrier frequency by performing, e.g., Frequency Shift Keying and Manchester encoding, and/or other protocol(s).
  • the data transmission rate is set for efficient and effective transmission.
  • the data transmission rate may be about 19,200 symbols per second, with a minimum transmission range for communication with the primary receiver unit 104.
  • FIG. 3 shows a block diagram of an embodiment of a receiver/monitor unit such as the primary receiver unit 104 of the data monitoring and management system shown in FIG. 1.
  • the primary receiver unit 104 may include one or more of: a blood glucose test strip interface 301 for in vitro testing, an RF receiver 302, an input 303, a temperature detection section 304, and a clock 305, each of which is operatively coupled to a processing and storage section 307.
  • 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.
  • a receiver serial communication section 309, and an output 310 each operatively coupled to the processing and storage unit 307.
  • the receiver may include user input and/or interface components or may be free of user input and/or interface components.
  • the interface includes a glucose level testing portion to receive a blood (or other body fluid sample) glucose test or information related thereto.
  • the interface may include a test strip port to receive an in vitro glucose test strip.
  • the device may determine the glucose level of the test strip, and optionally display (or otherwise report or output) the glucose level on the output 310 of the primary receiver unit 104.
  • Any suitable test strip may be employed, e.g., test strips that only require a very small amount (e.g., one microliter or less, e.g., 0.5 microliter or less, e.g., 0.1 microliter or less), of applied sample to the strip in order to obtain accurate glucose information, e.g.
  • Glucose information obtained by the in vitro glucose testing device may be used for a variety of purposes, computations, etc.
  • the information may be used to calibrate sensor 101 (however, calibration of the subject sensors may not be necessary), confirm results of the sensor 101 to increase the confidence thereof (e.g., in instances in which information obtained by sensor 101 is employed in therapy related decisions), etc.
  • Exemplary blood glucose monitoring systems are described, e.g., in U.S. Patent Nos. 6,071,391; 6,120,676; 6,338,790; and 6,616,819; and in U.S.
  • the RF receiver 302 is configured to communicate, via the communication link 103 (FIG. 1) with the RF transmitter 206 of the data processing unit 102, to receive encoded data signals from the data processing 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.
  • the input 303 may include keys of a keypad, a touch-sensitive screen, and/or a voice-activated input command unit, and the like.
  • the temperature monitor section 304 is configured to provide temperature information of the primary receiver unit 104 to the receiver processing and storage unit 307, while the clock 305 provides, among others, real time information to the receiver processing and storage unit 307.
  • Each of the various components of the primary receiver unit 104 shown in FIG. 3 is powered by the power supply 306 (and/or other power supply) which, in certain embodiments, includes a battery.
  • 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 may 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 processing and storage unit 307 (thus, the primary receiver unit 104) is turned on.
  • 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.
  • FET field effect transistor
  • 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 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.
  • GUI graphical user interface
  • 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, pagers, etc.
  • 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.
  • the primary receiver unit 104 may also include a storage section such as a programmable, non- volatile memory device as part of the processing and storage unit 307, or provided separately in the primary receiver unit
  • the processing and storage unit 307 may be configured to perform Manchester decoding (or other protocol(s)) as well as error detection and correction upon the encoded data signals received from the data processing unit 102 via the communication link 103.
  • the data processing unit 102 and/or the primary receiver unit 104 and/or the secondary receiver unit 106, and/or the data processing terminal/infusion section 105 may be configured to receive the blood glucose value from a wired connection or wirelessly over a communication link from, for example, a blood glucose meter.
  • a user manipulating or using the analyte monitoring system 100 FIG.
  • a user interface for example, a keyboard, keypad, voice commands, and the like
  • the data processing unit 102 may manually input the blood glucose value using, for example, a user interface (for example, a keyboard, keypad, voice commands, and the like) incorporated in the one or more of the data processing unit 102, the primary receiver unit 104, secondary receiver unit 106, or the data processing terminal/infusion section 105.
  • a user interface for example, a keyboard, keypad, voice commands, and the like
  • the data processing unit 102 (FIG. 1) is configured to detect the current signal from the sensor unit 101 (FIG. 1) and optionally the skin and/or ambient temperature near the sensor unit 101, which may be preprocessed by, for example, the data processing unit processor 204 (FIG. 2) and transmitted to the receiver unit (for example, the primary receiver unit 104 (FIG. I)) at least at a predetermined time interval, such as for example, but not limited to, once per minute, once every two minutes, once every five minutes, or once every ten minutes.
  • the data processing unit 102 may be configured to perform sensor insertion detection and data quality analysis, information pertaining to which may also transmitted to the receiver unit 104 periodically at the predetermined time interval.
  • the receiver unit 104 may be configured to perform, for example, skin temperature compensation as well as calibration of the sensor data received from the data processing unit 102.
  • the subject analyte measurement systems may include an alarm system that, e.g., based on information from a processor, warns the patient of a potentially detrimental condition of the analyte. For example, if glucose is the analyte, an alarm system may warn a user of conditions such as hypoglycemia and/or hyperglycemia and/or impending hypoglycemia, and/or impending hyperglycemia. An alarm system may be triggered when analyte levels approach, reach or exceed a threshold value. An alarm system may also, or alternatively, be activated when the rate of change, or the acceleration of the rate of change in the analyte level increase or decrease approaches, reaches or exceeds a threshold rate or acceleration.
  • an alarm system may also, or alternatively, be activated when the rate of change, or the acceleration of the rate of change in the analyte level increase or decrease approaches, reaches or exceeds a threshold rate or acceleration.
  • a system may also include system alarms that notify a user of system information such as battery condition, calibration, sensor dislodgment, sensor malfunction, etc.
  • Alarms may be, for example, auditory and/or visual.
  • Other sensory-stimulating alarm systems may be used including alarm systems which heat, cool, vibrate, or produce a mild electrical shock when activated.
  • the subject disclosure also includes sensors used in sensor-based drug delivery systems.
  • the system may provide a drug to counteract the high or low level of the analyte in response to the signals from one or more sensors. Alternatively, the system may monitor the drug concentration to ensure that the drug remains within a desired therapeutic range.
  • the drug delivery system may include one or more (e.g., two or more) sensors, a processing unit such as a transmitter, a receiver/display unit, and a drug administration system. In some cases, some or all components may be integrated in a single unit.
  • a sensor-based drug delivery system may use data from the one or more sensors to provide necessary input for a control algorithm/mechanism to adjust the administration of drugs, e.g., automatically or semi-automatically.
  • a glucose sensor may be used to control and adjust the administration of insulin from an external or implanted insulin pump.
  • analyte sensor signal dropout detection routine, technique and/or devices embodying software algorithms for executing the signal dropout detections for execution and/or implementation are provided.
  • the signal dropout detector may include a software algorithm executed by the processor of either the data processing unit 102 (FIG. 1) or the receiver unit 104, 106.
  • the signal dropout detector may be configured to monitor the data received from the sensor 101 to detect presence of one or more signal dropout conditions in the sensor data.
  • the processor unit 102 or the receiver unit 104, 106 may further be configured to confirm, store, and/or report the detection of a signal dropout.
  • a signal dropout detector includes a discrete wavelet transform (DWT) signal dropout detector.
  • DWT discrete wavelet transform
  • All wavelet transforms may be considered forms of time-frequency representation and so are related to harmonic analysis.
  • discrete wavelet transform can be formulated as:
  • ⁇ • , • > is the inner product
  • a is the scaling parameter (to capture different frequency)
  • b is the shifting parameter (in time domain, to capture local changes)
  • ⁇ (t) is the mother wavelet
  • ⁇ a ,b(t) are baby wavelets. Different levels of baby wavelets can capture local changes or discontinuities at different scales.
  • discrete wavelet transform may be performed with multi-stages.
  • an analyte sensor signal may be decomposed into details at different scales or frequencies, as shown in FIGS. 4 and 5. More specifically, FIG. 4 illustrates discrete wavelet decomposition in one aspect, and FIG. 5 illustrates frequencies of components at different levels in discrete wavelet transform (DWT).
  • a higher level detail e.g. D 3
  • Di is the finest level detail.
  • Applications for the wavelet analysis include, but are not limited to, denoising, data compression, signal analysis, image processing, multi-fractal analysis, as well as the signal dropout detector of the present disclosure.
  • FIG. 6A further illustrates examples of wavelets that may be implemented in a discrete wavelet transform signal dropout detector in aspects of the present disclosure.
  • the signal dropout detector implements or executes an algorithm or a routine using the five finest levels of discrete wavelet transform (DWT). Data observation has shown that signal dropouts can be captured by the details of the five finest levels.
  • FIG. 7 is a flow chart illustrating a recursive algorithm for implementing discrete wavelet transforms for detecting signal dropouts in one embodiment of the present disclosure. Referring to FIG. 7, before the signal dropout detection starts, the detector is initialized based on training data (710), which includes data associated with the normal glucose level.
  • the dropout detection process begins.
  • a moving window is constructed with dyadic width and an end at the current data point (720).
  • FIG. 8 illustrates an example of a moving window described above in conjunction with step 720 of FIG. 7.
  • discrete wavelet transform is applied inside the moving window using a Haar wavelet (730) to extract the details of the signal.
  • wavelet thresholding is applied to the coefficients of the wavelet (740).
  • the signal includes a Gaussian noise component, whose effects are minimized by the application of the wavelet thresholding.
  • the same thresholds may be applied for details at different levels as the Gaussian noise has a flat spectrum.
  • the wavelet threshold is calculated as shown in the following expression:
  • the threshold is updated with an exponentially weighted moving average (EWMA) technique based on the following expression:
  • the detector determines the left boundary L of the signal drop, as shown in FIG. 9A.
  • a warning delay may be defined as the difference in time between the current time and the left boundary L as can be seen in FIG. 9A.
  • the warning trigger stays active until the dropout completes.
  • a right boundary R is moved forward as new data points are received until it is determined to be the boundary where the signal dropout completes, as can be seen in FIGS. 9B and 9C.
  • confirmation rules are applied to the detected dropout to determine if the dropout is a signal dropout (760).
  • the confirmation rules may be applied to the signal to determine whether the signal decline is a signal dropout (760) or in fact is a normal, but possibly fast, change in glucose level, such as after events including meals (carbohydrate intake), exercise, or administration of medication such as insulin.
  • the rules for determining if a signal decline is a signal dropout may include, among others, rule-based evaluators that are Boolean valued composite measures based on the boundaries, nadir, width of the possible signal dropout, and normal signal variations prior to the dropout.
  • One or more of the following rules may be applied for the confirmation of a signal dropout, where L, R, and B are the left boundary, right boundary, and bottom of a dropout, respectively:
  • dG m a x which is a constant value.
  • the normal rate of change of glucose levels was determined to be Gaussian dG normal ⁇ N if), 1.5) .
  • the dropouts had a standard deviation of 37, much larger than 7.5. If a large upper boundary dG max value is chosen such as 60, the signal dropout detection rule may result in minimal number of false alarms or indications associated with signal dropout conditions.
  • This rule #2 is a constraint upon the glucose rate of change inside the signal drop. If the average glucose rate of change for both the dropping and rising regions are larger than a cut-off value dG c estimated from the training data, it is determined the dropout is a signal dropout.
  • the horizon (h) set to be 17 based on averaged experimental data.
  • main ⁇ dG) ⁇ G e u h/2 i+h/2 i is found to be approximately Gaussian N(0,2.36).
  • the averages of dG of the dropouts are calculated for the dropping side and recovering side, and it is determined that -5 is a dividing line of negative dG of the normal variations and dropouts, as can be seen in the distribution charts of FIG. 10.
  • a conservative value of dG c % is chosen to reduce the possibility of false alarm occurrences.
  • a signal dropout is confirmed if G(L,B) can be fit well by a first order
  • FIG. 11 illustrates an example of an exponential-shaped dropout.
  • a signal dropout is confirmed based on the following expression:
  • Rule #4 is based on the assumption that the depth of a signal dropout is larger than the normal glucose variations.
  • each of the four rules may be sequentially applied, and if any of the rules are satisfied, the detected or suspected signal dropout may be confirmed to be a signal dropout.
  • a confirmation delay in one aspect may be defined as the time between when a signal decline starts and the time in which the signal decline is confirmed to be a signal dropout. Smaller confirmation delays and warning delays may be preferable in certain embodiments, as smaller delays allow for faster control actions in response to the detected dropouts. If a signal decline is confirmed as a signal dropout, as shown in FIGS. 9B and 9C, the signal dropout may be recorded/stored and/or reported to the patient, and/or a notification to the user or the patient may be generated and output. After confirmation of the presence, or lack thereof, of a signal dropout, the detector in one embodiment is configured to measure a next data point (770) and the signal dropout detection process as described above is repeated.
  • the detector continues to measure and detect signal dropouts until the moving window reaches the end of the sensor lifetime (which may be, for example, approximately three days, five days or seven days, or more), at which point a new sensor may be implanted or transcutaneously positioned in the patient, and the signal dropout detector is reinitialized for continued detection of signal dropouts.
  • the end of the sensor lifetime which may be, for example, approximately three days, five days or seven days, or more
  • FIGS. 12 and 13 and the Table 1 below illustrate the results of a signal dropout detector applied to one of three analyte sensors in one embodiment.
  • the signal dropout detector was applied to sensor 3, and sensors 1 and 5 were for visual comparison check.
  • signal dropouts were detected as marked.
  • FIG. 14 illustrates the true dropouts of the sensor data from sensor 3
  • S is the raw data
  • D 7 are the details (higher frequency parts)
  • a 7 are the approximations (lower frequency parts).
  • the signal dropout detection rules may be implemented, such that for a given signal dropout: 2G(B)+ G(L)+ G(R) . f_ _
  • [lowG left, highG left] and mG left are the confidence interval and mean for the data left of the valley, and similarly
  • [lowG right, highG right] and mG right are the confidence interval and mean for the data right of the valley, as illustrated in FIG. 17.
  • the peaks will be excluded from the dropout set.
  • another rule for the dropout detection routine is G(i) ⁇ G(R) if
  • the wavelet details accumulation D c does not provide adequate estimation of the boundaries of a valley.
  • derivatives may be used to refine the boundaries, such as using the second derivatives to refine the boundaries as the dropouts start or end at where changes fastest, i.e. dt where the second derivative reaches it local minimum or maximum, as shown in FIG.
  • FIG. 19 illustrates using derivatives to refine the boundaries.
  • the first and second derivative of the measurement may be used to refine the left and right boundaries of a valley as follows:
  • FIGS. 21 A and 2 IB illustrate the success rate of the signal dropout detector in the experimental studies. As can be seen in FIG. 21A and the table of FIG. 21B, it was determined that a correct detection was made 78% of the time and misidentif ⁇ cation was made 5% of the time. Furthermore experimental observations indicated that many dropouts were lead by a small but fast peak, as illustrated in the table of FIG. 2 IB and in FIGS. 22- 26. Such patterns may be implemented in the detection of dropouts.
  • FIGS. 27 and 28 and Table 2 below illustrate the results of a signal dropout detector applied to a glucose sensor measuring blood glucose levels in a human body.
  • two situations were found where accuracy of the signal dropout detector was lower.
  • FIG. 29 in one case, as shown, abnormal amounts of noise appeared in the sensor data on the last day of the sensor lifetime (illustrated by the circled region of FIG. 29). Accordingly, warnings for signal dropouts were triggered frequently by the signal dropout detector. The abnormal frequency of warning triggers resulted in the signal dropout detector determining some of the signal drops as signal dropouts and others as not.
  • FIG. 30 in another case, as shown, normal glucose variations around the dropouts were hardly noticeable. As such, the signal dropout detector was unable to estimate normal glucose variation parameters accurately and therefore, the signal dropout detector detected some of the signal dropouts, but possibly missed others.
  • the abnormal data from both special cases can be regarded as the breaking down of the sensor because of the extended periods of time of abnormal measurements.
  • SNR signal-noise ratio
  • DNR drop variation-noise variation ratio
  • dG glucose rate of change
  • Noise covariance may be determined using the first level discrete wavelet transform (DWT) details within the moving window such that , where di is the first level discrete wavelet transform (DWT) details within the moving window with width W
  • the ratio between signal variance and noise variance may be determined based on the following expression:
  • the ratio between drop variance and noise variance may be determined based on the following expression:
  • FIG. 31 illustrates the sensor data from one of the sensors in the experiment and FIG. 32 illustrates the detected dropouts in the sensor data of FIG. 31.
  • the results of the experimental data indicated a detection of both confirmed signal dropouts as well as warned, but unconfirmed, dropouts. Both the confirmed dropouts and the unconfirmed dropouts give warnings, thus the number of total detected dropouts is equal to the number of confirmed dropouts added to the number of warned but unconfirmed dropouts.
  • the end result of the experiment was successful detection of 76 dropouts and missed detection of 6 dropouts.
  • FIGS. 33-35 illustrate the relationship between SNR and DNR and detected signal dropouts. As can be seen in FIG. 33, the majority of the false alarms and missed dropouts occurred at lower DNR and SNR values.
  • the missed dropouts as can be seen in FIG. 34, have either lower DNR or small indicating that the missed dropouts have either a small magnitude or change slowly compared with most other signal dropouts.
  • kits that (1) detect signal dropouts online for a transcutaneously positioned analyte sensor, (2) detect different sizes of signal dropouts; and (3) detect signal dropouts as quickly as possible contemporaneous to the occurrence of the dropout condition. More specifically, embodiment include an online signal dropout detection routine based on the discrete wavelet transform
  • DWT a signal processing technique which can effectively extract the signal component at a specific frequency range and a specific time region with scaled and shifted wavelets.
  • D c sum(Di : D 5 ).
  • the cumulative detail D c exceeds its confidence interval (CI)
  • an abrupt dropout is warned, which will be confirmed as a signal dropout or otherwise by a rule-based evaluator.
  • the rule-based evaluator may be a Boolean valued composite measure based on the boundaries, minimum, width of the signal dropout, normal signal variations around the signal dropout, and the like.
  • the evaluator may be configured to exclude normal changes or variations in the glucose level such as a real but fast rise in glucose level after a meal.
  • One embodiment may comprise receiving a data stream including a plurality of monitored analyte signals from an analyte monitoring device, performing a recursive signal processing routine on the received data stream to detect a signal dropout condition in the data stream, generating a signal dropout condition notif ⁇ cation when the signal dropout condition is detected, applying one or more dropout confirmation routines to the data stream to confirm the detected dropout condition, outputting a notification associated with the confirmed dropout condition, and modifying one or more operational parameters of the analyte monitoring device when the detected dropout condition is confirmed.
  • the modified one or more operational parameters may include temporarily disabling an output unit of the analyte monitoring device.
  • Modifying the one or more operational parameters may include suppressing output of data associated with the monitored analyte signals. Modifying the one or more operational parameters may include temporarily disabling one or more functions of the analyte monitoring device.
  • one or more functions may include calibration of an analyte sensor.
  • the output notification associated with the confirmed dropout condition may include an error message associated with the monitored analyte signals.
  • the generated signal dropout condition notification or the notification associated with the confirmed dropout condition may include one or more of an audible output, a visual output, a graphical output, a vibratory output, or one or more combinations thereof.
  • Performing the recursive signal processing routine may include applying discrete wavelet transform on the received data stream.
  • the recursive signal processing routine may be performed retrospectively on the received data stream.
  • the recursive signal processing routine may be performed substantially in real time when the data stream is received.
  • the analyte signals may include glucose signals.
  • a further embodiment may include detecting an analyte sensor calibration routine initiation, temporarily suspending the calibration routine for a predetermined time period, and reinitiating the calibration routine when the confirmed dropout condition is no longer present.
  • Calibration routine initiation may include requesting a reference glucose measurement data.
  • the reference glucose measurement data may include a blood glucose measurement data.
  • an apparatus may comprise an analyte sensor, and an analyte monitoring device operatively coupled to the analyte sensor to receive a data stream including a plurality of monitored analyte signals, the analyte monitoring device including one or more processors, and a memory operatively coupled to the one or more processors for storing instructions which, when executed by the one or more processors, causes the one or more processors to perform a recursive signal processing routine on the received data stream to detect a signal dropout condition in the data stream, generate a signal dropout condition notification when the signal dropout condition is detected, apply one or more dropout confirmation routines to the data stream to confirm the detected dropout condition, output a notification associated with the confirmed dropout condition, and modify one or more operational parameters of the analyte monitoring device when the detected dropout condition is confirmed.
  • the memory operatively coupled to the one or more processors for storing instructions which, when executed by the one or more processors, may cause the one or more processors to temporarily disable an output unit operatively coupled to the one or more processors of the analyte monitoring device.
  • the memory operatively coupled to the one or more processors for storing instructions which, when executed by the one or more processors, may cause the one or more processors to suppress output of data associated with the monitored analyte signals.
  • the memory operatively coupled to the one or more processors for storing instructions which, when executed by the one or more processors, may cause the one or more processors to temporarily disable one or more functions of the analyte monitoring device.
  • one or more functions may include calibration of the analyte sensor.
  • the output notification associated with the confirmed dropout condition may include an error message associated with the monitored analyte signals.
  • the generated signal dropout condition notification or the notification associated with the confirmed dropout condition may include one or more of an audible output, a visual output, a graphical output, a vibratory output, or one or more combinations thereof.
  • the memory operatively coupled to the one or more processors for storing instructions which, when executed by the one or more processors, may cause the one or more processors to apply discrete wavelet transform function on the received data stream.
  • the recursive signal processing routine may be performed retrospectively on the received data stream.
  • the recursive signal processing routine may be performed substantially in real time when the data stream is received.
  • the analyte signals may include glucose signals.
  • the memory operatively coupled to the one or more processors for storing instructions which, when executed by the one or more processors, may cause the one or more processors to detect an analyte sensor calibration routine initiation, temporarily suspend the calibration routine for a predetermined time period, and reinitiate the calibration routine when the confirmed dropout condition is no longer present.
  • the memory operatively coupled to the one or more processors for storing instructions which, when executed by the one or more processors, may cause the one or more processors to request a reference glucose measurement data.
  • the reference glucose measurement data may include a blood glucose measurement data.
  • the various processes described above including the processes performed by the processor 204 (FIG. 2) in the software application execution environment in the analyte monitoring system 100 (FIG. 1) as well as any other suitable or similar processing units embodied in the processing and storage unit 307 (FIG. 3) of the primary/secondary receiver unit 104/106, and/or the data processing terminal/infusion section 105, including the processes and routines described hereinabove, may be embodied as computer programs developed using an object oriented language that allows the modeling of complex systems with modular objects to create abstractions that are representative of real world, physical objects and their interrelationships.
  • the software required to carry out the inventive process which may be stored in a memory or storage unit (or similar storage devices in the one or more components of the system 100 and executed by the processor, may be developed by a person of ordinary skill in the art and may include one or more computer program products.
  • 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.
  • 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 invention and that structures and methods within the scope of these claims and their equivalents be covered thereby.

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Abstract

L’invention concerne un procédé comprenant la réception d’un flux de données qui comprend une pluralité de signaux surveillés de substances à analyser provenant d’un dispositif de surveillance de substances à analyser, l’exécution d’une routine de traitement de signaux récursive sur le flux de données reçu afin de détecter une condition de perte de niveau de signal dans le flux de données, la génération d’une notification de condition de perte de niveau de signal lorsque ladite condition est détectée, l’application d’une ou de plusieurs routines de confirmation de perte de niveau au flux de données pour confirmer la condition de perte de données détectée, la sortie d’une notification associée à la condition de perte confirmée, et la modification d’un ou de plusieurs paramètres opérationnels du dispositif de surveillance de substances à analyser lorsque la condition de perte détectée est confirmée. L’invention concerne également des dispositifs, des systèmes et des kits intégrant le procédé.
PCT/US2009/063936 2008-11-10 2009-11-10 Procédé et système pour permettre une détection de perte de niveau dans des capteurs de substances à analyser Ceased WO2010054408A1 (fr)

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EP2890297A4 (fr) * 2012-08-30 2016-04-13 Abbott Diabetes Care Inc Détection de pertes d'information dans des données de surveillance continue d'analyte lors d'excursions des données
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US20240164721A1 (en) * 2014-03-30 2024-05-23 Abbott Diabetes Care Inc. Method and apparatus for determining meal start and peak events in analyte monitoring systems
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