U.S. patent application number 11/583407 was filed with the patent office on 2007-02-15 for signal processing for measurement of physiological analytes.
Invention is credited to Bret Berner, Timothy C. Dunn, Kathleen C. Farinas, Michael D. Garrison, Ronald T. Kurnik, Matthew J. Lesho, Russell O. Potts, Janet A. Tamada, Michael J. Tierney.
Application Number | 20070038053 11/583407 |
Document ID | / |
Family ID | 22190986 |
Filed Date | 2007-02-15 |
United States Patent
Application |
20070038053 |
Kind Code |
A1 |
Berner; Bret ; et
al. |
February 15, 2007 |
Signal processing for measurement of physiological analytes
Abstract
A method is provided for continually or continuously measuring
the concentration of target chemical analytes present in a
biological system, and processing analyte-specific signals to
obtain a measurement value that is closely correlated with the
concentration of the target chemical analyte in the biological
system. One important application of the invention involves a
method for signal processing in a system for monitoring blood
glucose values.
Inventors: |
Berner; Bret; (El Granada,
CA) ; Dunn; Timothy C.; (Menlo Park, CA) ;
Farinas; Kathleen C.; (San Carlos, CA) ; Garrison;
Michael D.; (Seattle, WA) ; Kurnik; Ronald T.;
(Foster City, CA) ; Lesho; Matthew J.; (San Mateo,
CA) ; Potts; Russell O.; (San Francisco, CA) ;
Tamada; Janet A.; (Mountain View, CA) ; Tierney;
Michael J.; (San Jose, CA) |
Correspondence
Address: |
RATNERPRESTIA
P O BOX 980
VALLEY FORGE
PA
19482-0980
US
|
Family ID: |
22190986 |
Appl. No.: |
11/583407 |
Filed: |
October 19, 2006 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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10384443 |
Mar 7, 2003 |
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11583407 |
Oct 19, 2006 |
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09894783 |
Jun 28, 2001 |
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10384443 |
Mar 7, 2003 |
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09309728 |
May 11, 1999 |
6233471 |
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09894783 |
Jun 28, 2001 |
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60085344 |
May 13, 1998 |
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Current U.S.
Class: |
600/345 |
Current CPC
Class: |
A61B 5/1477 20130101;
A61B 5/14532 20130101; A61B 5/1486 20130101; A61B 5/14542
20130101 |
Class at
Publication: |
600/345 |
International
Class: |
A61B 5/05 20060101
A61B005/05 |
Claims
1. A method of converting post-calibration data to provide a
measurement of analyte amount or concentration in a subject, said
method comprising: obtaining a raw signal from the analyte, wherein
(i) interstial fluid of the subject comprises said analyte, (ii)
obtaining said raw signal comprises contacting said interstitial
fluid with a sensing device, and (iii) said raw signal is related
to analyte amount or concentration in the biological system;
subjecting the raw signal to at least one conversion step in order
to convert said raw signal to an initial signal output which is
indicative of the amount or concentration of analyte; calibrating
the sensing device by performing at least one calibration step that
converts the initial signal output to a measurement value
indicative of the amount or concentration of analyte present in the
biological system; and converting a raw signal or initial signal
output obtained after the calibration of the sensing device to a
measurement value indicative of the amount or concentration of
analyte present in the subject.
2. The method of claim 1, wherein after calibration of the sensing
device the method further comprises obtaining a series of raw
signals over time, subjecting each raw signal to at least one
conversion step in order to convert said raw signal to an initial
signal output which is indicative of the amount or concentration of
analyte.
3. The method of claim 1, wherein the converting the raw signal or
initial signal output after the calibration of the sensing device
further comprises subjecting the raw signal to at least one
conversion step to convert the raw signal to an initial signal
output, and converting the initial signal output to a measurement
value indicative of the amount or concentration of analyte present
in the subject.
4. The method of claim 1, wherein said calibrating uses a
mathematical transformation to model a relationship between a raw
signal and a corresponding measurement value indicative of the
amount or concentration of analyte present in the subject.
5. The method of claim 4, wherein said mathematical transformation
comprises a mathematical transformation selected from the group
consisting of linear regression, non-linear regression, and neural
network algorithms.
6. The method of claim 3, wherein said calibrating uses a
mathematical transformation to model a relationship between an
initial signal output and a corresponding measurement value
indicative of the amount or concentration of analyte present in the
subject.
7. The method of claim 6, wherein said mathematical transformation
comprises a mathematical transformation selected from the group
consisting of linear regression non-linear regression, and neural
network algorithms.
8. The method of claim 1, wherein said calibrating uses a
single-point or multi-point calibration.
9. The method of claim 1, wherein said converting the raw signal or
initial signal output obtained after the calibration of the sensing
device uses correlation factors, time corrections, and/or constants
to obtain a corresponding measurement value indicative of the
amount or concentration of analyte present in the subject.
10. The method of claim 1, comprising further signal processing to
refine said measurement value indicative of the amount or
concentration of analyte present in the subject.
11. The method of claim 10, wherein said further signal processing
comprises a signal processing step to correct for signal
differences due to variable conditions of the sensing device used
to obtain the raw signal.
12. The method of claim 11, wherein said signal processing step is
used to correct for signal decline.
13. The method of claim 11, wherein said signal processing step
employs a constant offset term.
14. The method of claim 13, wherein said offset is added to the raw
signal or initial signal output to account for a non-zero signal at
an estimated zero analyte concentration.
15. The method of claim 1, wherein said sensing device comprises an
electrochemical sensing element.
Description
CROSS-REFERENCE TO RELATED APPLICATIONS
[0001] This application is a divisional of U.S. patent application
Ser. No. 10/384,443, filed Mar. 7, 2003, which is a continuation of
U.S. patent application Ser. No. 09/894,783, filed Feb. 27, 2001,
now U.S. Pat. No. 6,595,919, which is a continuation of U.S. patent
application Ser. No. 09/309,728, filed May 11, 1999, now U.S. Pat.
No. 6,233,471, which claims priority to U.S. Provisional Patent
Application Ser. No. 60/085,344, filed May 13, 1998, and which
applications are incorporated herein by reference in their
entireties.
FIELD OF THE INVENTION
[0002] The invention relates generally to methods for continually
or continuously measuring the concentration of target chemical
analytes present in a biological system. More particularly, the
invention relates to methods for processing signals obtained during
measurement of physiological analytes. One important application of
the invention involves a method for monitoring blood glucose
concentrations.
BACKGROUND OF THE INVENTION
[0003] A number of diagnostic tests are routinely performed on
humans to evaluate the amount or existence of substances present in
blood or other body fluids. These diagnostic tests typically rely
on physiological fluid samples removed from a subject, either using
a syringe or by pricking the skin. One particular diagnostic test
entails self-monitoring of blood glucose levels by diabetics.
[0004] Diabetes is a major health concern, and treatment of the
more severe form of the condition, Type I (insulin-dependent)
diabetes, requires one or more insulin injections per day. Insulin
controls utilization of glucose or sugar in the blood and prevents
hyperglycemia which, if left uncorrected, can lead to ketosis. On
the other hand, improper administration of insulin therapy can
result in hypoglycemic episodes, which can cause coma and death.
Hyperglycemia in diabetics has been correlated with several
long-term effects of diabetes, such as heart disease,
atherosclerosis, blindness, stroke, hypertension and kidney
failure.
[0005] The value of frequent monitoring of blood glucose as a means
to avoid or at least minimize the complications of Type I diabetes
is well established. Patients with Type II (non-insulin-dependent)
diabetes can also benefit from blood glucose monitoring in the
control of their condition by way of diet and exercise.
[0006] Conventional blood glucose monitoring methods generally
require the drawing of a blood sample (e.g., by fingerprick) for
each test, and a determination of the glucose level using an
instrument that reads glucose concentrations by electrochemical or
calorimetric methods. Type I diabetics must obtain several
fingerprick blood glucose measurements each day in order to
maintain tight glycemic control. However, the pain and
inconvenience associated with this blood sampling, along with the
fear of hypoglycemia, has led to poor patient compliance, despite
strong evidence that tight control dramatically reduces long-term
diabetic complications. In fact, these considerations can often
lead to an abatement of the monitoring process by the diabetic.
See, e.g., The Diabetes Control and Complications Trial Research
Group (1993) New Engl. J. Med. 329:977-1036.
[0007] Recently, various methods for determining the concentration
of blood analytes without drawing blood have been developed. For
example, U.S. Patent No. 5,267,152 to Yang et al. describes a
noninvasive technique of measuring blood glucose concentration
using near-IR radiation diffuse-reflection laser spectroscopy.
Similar near-IR spectrometric devices are also described in U.S.
Pat. No. 5,086,229 to Rosenthal et al. and U.S. Pat. No. 4,975,581
to Robinson et al.
[0008] U.S. Pat. No. 5,139,023 to Stanley et al., and U.S. Pat. No.
5,443.080 to D'Angelo et al. describe transdermal blood glucose
monitoring devices that rely on a permeability enhancer (e.g., a
bile salt) to facilitate transdermal movement of glucose along a
concentration gradient established between interstitial fluid and a
receiving medium. U.S. Pat. No. 5,036,861 to Sembrowich describes a
passive glucose monitor that collects perspiration through a skin
patch, where a cholinergic agent is used to stimulate perspiration
secretion from the eccrine sweat gland. Similar perspiration
collection devices are described in U.S. Pat. No. 5,076,273 to
Schoendorfer and U.S. Pat. No. 5,140,985 to Schroeder.
[0009] In addition, U.S. Pat. No. 5,279,543 to Glikfeld et al.
describes the use of iontophoresis to noninvasively sample a
substance through skin into a receptacle on the skin surface.
Glikfeld teaches that this sampling procedure can be coupled with a
glucose-specific biosensor or glucose-specific electrodes in order
to monitor blood glucose. Finally, International Publication No. WO
96/00110, published 4 Jan. 1996, describes an iontophoretic
apparatus for transdermal monitoring of a target substance, wherein
an iontophoretic electrode is used to move an analyte into a
collection reservoir and a biosensor is used to detect the target
analyte present in the reservoir.
SUMMARY OF THE INVENTION
[0010] The present invention provides a method for continually or
continuously measuring the concentration of an analyte present in a
biological system. The method entails continually or continuously
detecting an analyte from the biological system and deriving a raw
signal therefrom, wherein the raw signal is related to the analyte
concentration. A number of signal processing steps are then carried
out in order to convert the raw signal into an initial signal
output that is indicative of an analyte amount. The converted
signal is then further converted into a value indicative of the
concentration of analyte present in the biological system.
[0011] The raw signal can be obtained using any suitable sensing
methodology including, for example, methods which rely on direct
contact of a sensing apparatus with the biological system; methods
which extract samples from the biological system by invasive,
minimally invasive, and non-invasive sampling techniques, wherein
the sensing apparatus is contacted with the extracted sample;
methods which rely on indirect contact of a sensing apparatus with,
the biological system; and the like. In preferred embodiments of
the invention, methods are used to extract samples from the
biological sample using minimally invasive or non-invasive sampling
techniques. The sensing apparatus used with any of the above-noted
methods can employ any suitable sensing element to provide the raw
signal including, but not limited to, physical, chemical,
electrochemical, photochemical, spectrophotometric, polarimetric,
calorimetric, radiometric, or like elements. In preferred
embodiments of the invention, a biosensor is used which comprises
an electrochemical sensing element.
[0012] In one particular embodiment of the invention, the raw
signal is obtained using a transdermal sampling system that is
placed in operative contact with a skin or mucosal surface of the
biological system. The sampling system transdermally extracts the
analyte from the biological system using any appropriate sampling
technique, for example, iontophoresis. The transdermal sampling
system is maintained in operative contact with the skin or mucosal
surface of the biological system to provide for such continual or
continuous analyte measurement.
[0013] The analyte can be any specific substance or component that
one is desirous of detecting and/or measuring in a chemical,
physical, enzymatic, or optical analysis. Such analytes include,
but are not limited to, amino acids, enzyme substrates or products
indicating a disease state or condition, other markers of disease
states or conditions, drugs of abuse, therapeutic and/or
pharmacologic agents, electrolytes, physiological analytes of
interest (e.g., calcium, potassium, sodium, chloride, bicarbonate
(Co.sub.2), glucose, urea (blood urea nitrogen), lactate,
hematocrit, and hemoglobin), lipids, and the like. In preferred
embodiments, the analyte is a physiological analyte of interest,
for example glucose, or a chemical that has a physiological action,
for example a drug or pharmacological agent.
[0014] Accordingly, it is an object of the invention to provide a
method for continually or continuously measuring an analyte present
in a biological system, wherein raw signals are obtained from a
suitable sensing apparatus, and then subjected to signal processing
is techniques. More particularly, the raw signals undergo a data
screening method in order to eliminate outlier signals and/or poor
(incorrect) signals using a predefined set of selection criteria.
In addition, or alternatively, the raw signal can be converted in a
conversion step which (i) removes or corrects for background
information, (ii) integrates the raw signal over a sensing time
period, (iii) performs any process which converts the raw signal
from one signal type to another, or (iv) performs any combination
of steps (i), (ii) and/or (iii). In preferred embodiments, the
conversion step entails a baseline background subtraction method to
remove background from the raw signal and an integration step. In
other embodiments, the conversion step can be tailored for use with
a sensing device that provides both active and reference (blank)
signals; wherein mathematical transformations are used to
individually smooth active and reference signals, and/or to
subtract a weighted reference (blank) signal from the active
signal. In still further embodiments, the conversion step includes
correction functions which account for changing conditions in the
biological system and/or the biosensor system (e.g., temperature
fluctuations in the biological system, temperature fluctuations in
the sensor element, skin conductivity fluctuations, or combinations
thereof). The result of the conversion step is an initial signal
output which provides a value which can be correlated with the
concentration of the target analyte in the biological sample.
[0015] It is also an object of the invention to provide a signal
processing calibration step, wherein the raw or initial signals
obtained as described above are converted into an analyte-specific
value of known units to provide an interpretation of the signal
obtained from the sensing device. The interpretation uses a
mathematical transformation to model the relationship between a
measured response in the sensing device and a corresponding
analyte-specific value. Such mathematical transformations can
entail the use of linear or nonlinear regressions, or neural
network algorithms. In one embodiment, the calibration step entails
calibrating the sensing device using a single- or multi-point
calibration, and then converting post-calibration data using
correlation factors, time corrections and constants to obtain an
analyte-specific value. Further signal processing can be used to
refine the information obtained in the calibration step, for
example, where a signal processing step is used to correct for
signal differences due to variable conditions unique to the sensor
element used to obtain the raw signal. In one embodiment, this
further step is used to correct for signal time-dependence,
particularly signal decline. In another embodiment, a constant
offset term is obtained, which offset is added to the signal to
account for a non-zero signal at an estimated zero analyte
concentration.
[0016] Further, the methods of the present invention include
enhancement of skin permeability by pricking the skin with
micro-needles. In addition, the sampling system can be programed to
begin execution of sampling and sensing at a defined time(s).
[0017] It is yet a further object of the invention to provide a
monitoring system for continually or continuously measuring an
analyte present in a biological system. The monitoring system
comprises, in operative combination: (a) a sampling means for
continually or continuously extracting the analyte from the
biological system, (b) a sensing means in operative contact with
the analyte extracted by the sampling means, and (c) a
microprocessor means in operative communication with the sensing
means. The sampling means is adapted for extracting the analyte
across a skin or mucosal surface of a biological system. The
sensing means is used to obtain a raw signal from the extracted
analyte, wherein the raw signal is specifically related to the
analyte. The microprocessor means is used to subject the raw signal
to a conversion step, thereby converting the same into an initial
signal output which is indicative of the amount of analyte
extracted by the sampling means, and then perform a calibration
step which correlates the initial signal output with a measurement
value indicative of the concentration of analyte present in the
biological system at the time of extraction. In one embodiment, the
monitoring system uses iontophoresis to extract the analyte from
the biological system. In other embodiments, the monitoring system
is used to extract a glucose analyte from the biological system.
Further, the microprocessor can be programed to begin execution of
sampling and sensing at a defined time(s).
[0018] Additional objects, advantages and novel features of the
invention will be set forth in part in the description which
follows, and in part will become apparent to those skilled in the
art upon examination of the following, or may be learned by
practice of the invention.
BRIEF DESCRIPTION OF THE DRAWINGS
[0019] FIG. 1A depicts a top plan view of an iontophoretic
collection reservoir and electrode assembly for use in a
transdermal sampling device constructed according to the present
invention.
[0020] FIG. 1B depicts the side view of the iontophoretic
collection reservoir and electrode assembly shown in FIG. 1A.
[0021] FIG. 2 is a pictorial representation of an iontophoretic
sampling device which includes the iontophoretic collection
reservoir and electrode assembly of FIGS. 1A and 1B.
[0022] FIG. 3 is a representation of one embodiment of a bimodal
electrode design. The figure presents an overhead and schematic
view of the electrode assembly 33. In the figure, the bimodal
electrode is shown at 30 and can be, for example, a Ag/AgCl
iontophoretic/counter electrode. The sensing or working electrode
(made from, for example, platinum) is shown at 31. The reference
electrode is shown at 32 and can be, for example, a Ag/AgCl
electrode. The components are mounted on a suitable nonconductive
substrate 34, for example, plastic or ceramic. The conductive leads
37 leading to the connection pad 35 are covered by a second
nonconductive piece 36 of similar or different material. In this
example of such an electrode the working electrode area is
approximately 1.35 cm.sup.2. The dashed line in FIG. 3 represents
the plane of the cross-sectional schematic view presented in FIG.
4.
[0023] FIG. 4 is a representation of a cross-sectional schematic
view of the bimodal electrodes as they may be used in conjunction
with a reference electrode and a hydrogel pad. In the figure, the
components are as follows: bimodal electrodes 40 and 41; sensing
electrodes 42 and 43; reference electrodes 44 and 45; a substrate
46; and hydrogel pads 47 and 48.
[0024] FIG. 5 is an exploded pictorial representation of components
from a preferred embodiment of the automatic sampling system of the
present invention.
DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS
[0025] Before describing the present invention in detail, it is to
be understood that this invention is not limited to particular
compositions or biological systems as such may, of course, vary. It
is also to be understood that the terminology used herein is for
the purpose of describing particular embodiments only, and is not
intended to be limiting.
[0026] It must be noted that, as used in this specification and the
appended claims, the singular forms "a", "an" and "the" include
plural referents unless the content clearly dictates otherwise.
Thus, for example, reference to "a time-dependent variable"
includes a mixture of two or more such variables, reference to "an
electrochemically active species" includes two or more such
species, reference to "an analyte" includes mixtures of analytes,
and the like.
[0027] All publications, patents and patent applications cited
herein, whether supra or infra, are hereby incorporated by
reference in their entirety.
[0028] Unless defined otherwise, all technical and scientific terms
used herein have the same meaning as commonly understood by one of
ordinary skill in the art to which the invention pertains. Although
any methods and materials similar or equivalent to those described
herein can be used in the practice for testing of the present
invention, the preferred materials and methods are described
herein.
[0029] In describing and claiming the present invention, the
following terminology will be used in accordance with the
definitions set out below.
Definitions
[0030] The terms "analyte" and "target analyte" are used herein to
denote any physiological analyte of interest that is a specific
substance or component that is being detected and/or measured in a
chemical, physical, enzymatic, or optical analysis. A detectable
signal (e.g., a chemical signal or electrochemical signal) can be
obtained, either directly or indirectly, from such an analyte or
derivatives thereof. Furthermore, the terms "analyte" and
"substance" are used interchangeably herein, and are intended to
have the same meaning, and thus encompass any substance of
interest. In preferred embodiments, the analyte is a physiological
analyte of interest, for example, glucose, or a chemical that has a
physiological action, for example, a drug or pharmacological agent.
Interstitial fluid may comprise the analyte (for example,
glucose).
[0031] A "sampling device" or "sampling system" refers to any
device for obtaining a sample from a biological system for the
purpose of determining the concentration of an analyte of interest.
As used herein, the term "sampling" means invasive, minimally
invasive or non-invasive extraction of a substance from the
biological system, generally across a membrane such as skin or
mucosa. The membrane can be natural or artificial, and can be of
plant or animal nature, such as natural or artificial skin, blood
vessel tissue, intestinal tissue, and the like. Typically, the
sampling means are in operative contact with a "reservoir," or
"collection reservoir," wherein the sampling means is used for
extracting the analyte from the biological system into the
reservoir to obtain the analyte in the reservoir. A "biological
system" includes both living and artificially maintained systems.
Examples of minimally invasive and noninvasive sampling techniques
include iontophoresis, sonophoresis, suction, electroporation,
thermal poration, passive diffusion, microfine (miniature) lances
or cannulas, subcutaneous implants or insertions, and laser
devices. Sonophoresis uses ultrasound to increase the permeability
of the skin (see, e.g., Menon et al. (1994) Skin Pharmacology
7:130-139). Suitable sonophoresis sampling systems are described in
International Publication No. WO 91/12772, published 5 Sep. 1991.
Passive diffusion sampling devices are described, for example, in
International Publication Nos.: WO 97/38126 (published 16 Oct.
1997); WO 97/42888, WO 97/42886, WO 97/42885, and WO 97/42882 (all
published 20 Nov. 1997); and WO 97/43962 (published 27 Nov. 1997).
Laser devices use a small laser beam to burn a hole through the
upper layer of the patient's skin (see, e.g., Jacques et al. (1978)
J. Invest. Dermatology 88:88-93). Examples of invasive sampling
techniques include traditional needle and syringe or vacuum sample
tube devices.
[0032] The term "collection reservoir" is used to describe any
suitable containment means for containing a sample extracted from a
biological system. For example, the collection reservoir can be a
receptacle containing a material which is ionically conductive
(e.g., water with ions therein), or alternatively, it can be a
material, such as, a sponge-like material or hydrophilic polymer,
used to keep the water in place. Such collection reservoirs can be
in the form of a hydrogel (for example, in the form of a disk or
pad). Hydrogels are typically referred to as "collection inserts."
Other suitable collection reservoirs include, but are not limited
to, tubes, vials, capillary collection devices, cannulas, and
miniaturized etched, ablated or molded flow paths.
[0033] A "housing" for the sampling system can further include
suitable electronics (e.g., microprocessor, memory, display and
other circuit components) and power sources for operating the
sampling system in an automatic fashion.
[0034] A "monitoring system," as used herein, refers to a system
useful for continually or continuously measuring a physiological
analyte present in a biological system. Such a system typically
includes, but is not limited to, sampling means, sensing means, and
a microprocessor means in operative communication with the sampling
means and the sensing means.
[0035] The term "artificial," as used herein, refers to an
aggregation of cells of monolayer thickness or greater which are
grown or cultured in vivo or in vitro, and which function as a
tissue of an organism but are not actually derived, or excised,
from a pre-existing source or host.
[0036] The term "subject" encompasses any warm-blooded animal,
particularly including a member of the class Mammalia such as,
without limitation, humans and nonhuman primates such as
chimpanzees and other apes and monkey species; farm animals such as
cattle, sheep, pigs, goats and horses; domestic mammals such as
dogs and cats; laboratory animals including rodents such as mice,
rats and guinea pigs, and the like. The term does not denote a
particular age or sex. Thus, adult and newborn subjects, as well as
fetuses, whether male or female, are intended to be covered.
[0037] As used herein, the term "continual measurement" intends a
series of two or more measurements obtained from a particular
biological system, which measurements are obtained using a single
device maintained in operative contact with the biological system
over the time period in which the series of measurements is
obtained. The term thus includes continuous measurements.
[0038] The term "transdermal," as used herein, includes both
transdermal and transmucosal techniques, i.e., extraction of a
target analyte across skin or mucosal tissue. Aspects of the
invention which are described herein in the context of
"transdermal," unless otherwise specified, are meant to apply to
both transdermal and transmucosal techniques.
[0039] The term "transdermal extraction," or "transdermally
extracted" intends any noninvasive, or at least minimally invasive
sampling method, which entails extracting and/or transporting an
analyte from beneath a tissue surface across skin or mucosal
tissue. The term thus includes extraction of an analyte using
iontophoresis (reverse iontophoresis), electroosmosis,
sonophoresis, microdialysis, suction, and passive diffusion. These
methods can, of course, be coupled with application of skin
penetration enhancers or skin permeability enhancing technique such
as tape stripping or pricking with micro-needles. The term
"transdermally extracted" also encompasses extraction techniques
which employ thermal poration, electroporation, microfine lances,
microfine canulas, subcutaneous implants or insertions, and the
like.
[0040] The term "iontophoresis" intends a method for transporting
substances across tissue by way of an application of electrical
energy to the tissue. In conventional iontophoresis, a reservoir is
provided at the tissue surface to serve as a container of material
to be transported. Iontophoresis can be carried out using standard
methods known to those of skill in the art, for example, by
establishing an electrical potential using a direct current (DC)
between fixed anode and cathode "iontophoretic electrodes,"
alternating a direct current between anode and cathode
iontophoretic electrodes, or using a more complex waveform such as
applying a current with alternating polarity (AP) between
iontophoretic electrodes (so that each electrode is alternately an
anode or a cathode)
[0041] The term "reverse iontophoresis" refers to the movement of a
substance from a biological fluid across a membrane by way of an
applied electric potential or current. In reverse iontophoresis, a
reservoir is. provided at the tissue surface to receive the
extracted material.
[0042] "Electroosmosis" refers to the movement of a substance
through a membrane by way of an electric field-induced convective
flow. The terms iontophoresis, reverse iontophoresis, and
electroosmosis, will be used interchangeably herein to refer to
movement of any ionically charged or uncharged substance across a
membrane (e.g., an epithelial membrane) upon application of an
electric potential to the membrane through an ionically conductive
medium.
[0043] The term "sensing device," "sensing means," or "biosensor
device" encompasses any device that can be used to measure the
concentration of an analyte, or derivative thereof, of interest.
Preferred sensing devices for detecting blood analytes generally
include electrochemical devices and chemical devices. Examples of
electrochemical devices include the Clark electrode system (see,
e.g., Updike, et al., (1967) Nature 214:986-988), and other
amperometric, coulometric, or potentiometric electrochemical
devices. Examples of chemical devices include conventional
enzyme-based reactions as used in the Lifescan.RTM. glucose monitor
(Johnson and Johnson, New Brunswick, N.J.) (see, e.g., U.S. Pat.
No. 4,935,346 to Phillips, et al.).
[0044] A "biosensor" or "biosensor device" includes, but is not
limited to, a "sensor element" which includes, but is not limited
to, a "biosensor electrode" or "sensing electrode" or "working
electrode" which refers to the electrode that is monitored to
determine the amount of electrical signal at a point in time or
over a given time period, which signal is then correlated with the
concentration of a chemical compound. The sensing electrode
comprises a reactive surface which converts the analyte, or a
derivative thereof, to electrical signal. The reactive surface can
be comprised of any electrically conductive material such as, but
not limited to, platinum-group metals (including, platinum,
palladium, rhodium, ruthenium, osmium, and iridium), nickel,
copper, silver, and carbon, as well as, oxides, dioxides,
combinations or alloys thereof. Some catalytic materials,
membranes, and fabrication technologies suitable for the
construction of amperometric biosensors were described by Newman,
J. D., et al. (Analytical Chemistry 67(24), 4594-4599, 1995).
[0045] The "sensor element" can include components in addition to a
biosensor electrode, for example, it can include a "reference
electrode," and a "counter electrode." The term "reference
electrode" is used herein to mean an electrode that provides a
reference potential, e.g., a potential can be established between a
reference electrode and a working electrode. The term "counter
electrode" is used herein to mean an electrode in an
electrochemical circuit which acts as a current source or sink to
complete the electrochemical circuit. Although it is not essential
that a counter electrode be employed where a reference electrode is
included in the circuit and the electrode is capable of performing
the function of a counter electrode, it is preferred to have
separate counter and reference electrodes because the reference
potential provided by the reference electrode is most stable when
it is at equilibrium. If the reference electrode is required to act
further as a counter electrode, the current flowing through the
reference electrode may disturb this equilibrium. Consequently,
separate electrodes functioning as counter and reference electrodes
are most preferred.
[0046] In one embodiment, the "counter electrode" of the "sensor
element" comprises a "bimodal electrode." The term "bimodal
electrode" as used herein typically refers to an electrode which is
capable of functioning non-simultaneously as, for example, both the
counter electrode (of the "sensor element") and the iontophoretic
electrode (of the "sampling means").
[0047] The terms "reactive surface," and "reactive face" are used
interchangeably herein to mean the surface of the sensing electrode
that: (1) is in contact with the surface of an electrolyte
containing material (e.g. gel) which contains an analyte or through
which an analyte, or a derivative thereof, flows from a source
thereof; (2) is comprised of a catalytic material (e.g., carbon,
platinum, palladium, rhodium, ruthenium, or nickel and/or oxides,
dioxides and combinations or alloys thereof) or a material that
provides sites for electrochemical reaction; (3) converts a
chemical signal (e.g. hydrogen peroxide) into an electrical signal
(e.g., an electrical current); and (4) defines the electrode
surface area that, when composed of a reactive material, is
sufficient to drive the electrochemical reaction at a rate
sufficient to generate a detectable, reproducibly measurable,
electrical signal that is correlatable with the amount of analyte
present in the electrolyte.
[0048] The term "collection reservoir" and "collection insert" are
used to describe any suitable containment means for containing a
sample extracted from a biological system. The reservoir can
include a material which is ionically conductive (e.g., water with
ions therein), wherein another material such as a sponge-like
material or hydrophilic polymer is used to keep the water in place.
Such collection reservoirs can be in the form of a hydrogel (for
example, in the shape of a disk or pad). Other suitable collection
reservoirs include, but are not limited to, tubes, vials, capillary
collection devices, cannulas, and miniaturized etched, ablated or
molded flow paths.
[0049] An "ionically conductive material" refers to any material
that provides ionic conductivity, and through which
electrochemically active species can diffuse. The ionically
conductive material can be, for example, a solid, liquid, or
semi-solid (e.g., in the form of a gel) material that contains an
electrolyte, which can be composed primarily of water and ions
(e.g., sodium chloride), and generally comprises 50% or more water
by weight. The material can be in the form of a gel, a sponge or
pad (e.g., soaked with an electrolytic solution), or any other
material that can contain an electrolyte and allow passage
therethrough of electrochemically active species, especially the
analyte of interest.
[0050] The term "physiological effect" encompasses effects produced
in the subject that achieve the intended purpose of a therapy. In
preferred embodiments, a physiological effect means that the
symptoms of the 5 subject. being treated are prevented or
alleviated. For example, a physiological effect would be one that
results in the prolongation of survival in a patient.
[0051] A "laminate", as used herein, refers to structures comprised
of at least two bonded layers. The layers may be bonded by welding
or through the use of adhesives. Examples of welding include, but
are not limited to, the following: ultrasonic welding, heat
bonding, and inductively coupled localized heating followed by
localized flow. Examples of common adhesives include, but are not
limited to, pressure sensitive adhesives, thermoset adhesives,
cyanocrylate adhesives, epoxies, contact adhesives, and heat
sensitive adhesives.
[0052] A "collection assembly", as used herein, refers to
structures comprised of several layers, where the assembly includes
at least one collection insert, for example a hydrogel. An example
of a collection assembly of the present invention is a mask layer,
collection inserts, and a retaining layer where the layers are held
in appropriate, functional relationship to each other but are not
necessarily a laminate, i.e., the layers may not be bonded
together. The layers may, for example, be held together by
interlocking geometry or friction.
[0053] An "autosensor assembly", as used herein, refers to
structures generally comprising a mask layer, collection inserts, a
retaining layer, an electrode assembly, and a support tray. The
autosensor assembly may also include liners. The layers of the
assembly are held in appropriate, functional relationship to each
other.
[0054] The mask and retaining layers are preferably composed of
materials that are substantially impermeable to the analyte
(chemical signal) to be detected (e.g., glucose); however, the
material can be permeable to other substances. By "substantially
impermeable" is meant that the material reduces or eliminates
chemical signal transport (e.g., by diffusion). The material can
allow for a low level of chemical signal transport, with the
proviso that chemical signal that passes through the material does
not cause significant edge effects at the sensing electrode.
[0055] "Substantially planar" as used herein, includes a planar
surface that contacts a slightly curved surface, for example, a
forearm or upper arm of a subject. A "substantially planar" surface
is, for example, a surface having a shape to which skin can
conform, i.e., contacting contact between the skin and the
surface.
[0056] By the term "printed" as used herein is meant a
substantially uniform deposition of an electrode formulation onto
one surface of a substrate (i.e., the base support). It will be
appreciated by those skilled in the art that a variety of
techniques may be used to effect substantially uniform deposition
of a material onto a substrate, e.g., Gravure-type printing,
extrusion coating, screen coating, spraying, painting, or the
like.
[0057] The term "enzyme" intends any compound or material which
catalyzes a reaction between molecules to produce one or more
reaction products. The term thus includes protein enzymes, or
enzymatically active portions (fragments) thereof, which proteins
and/or protein fragments may be isolated from a natural source, or
recombinantly or synthetically produced. The term also encompasses
designed synthetic enzyme mimetics.
[0058] The term "time-dependent signal decline" refers to a
detectable decrease in measured signal over time when no decrease
or change in analyte concentration is actually occurring. The
decrease in signal over time may be due to a number of different
phenomena.
[0059] The term "signal-to-noise ratio" describes the relationship
between the actual signal intended to be measured and the variation
in signal in the absence of the analyte. The terms "S/N" and "SNR"
are also used to refer to the signal-to-noise ratio. "Noise," as
used herein, refers to any undesirable signal which is measured
along with the intended signal.
General Methods
[0060] The present invention relates to use of a device for
transdermally extracting and measuring the concentration of a
target analyte present in a biological system. In preferred
embodiments, the sensing device comprises a biosensor. In other
preferred embodiments, a sampling device is used to extract small
amounts of a target analyte from the biological system, and then
sense and/or quantify the concentration of the target analyte.
Measurement with the biosensor and/or sampling with the sampling
device can be carried out in a continual or continuous manner.
Continual or continuous measurements allow for closer monitoring of
target analyte concentration fluctuations.
[0061] The analyte can be any specific substance or component that
one is desirous of detecting and/or measuring in a chemical,
physical, enzymatic, or optical analysis. Such analytes include,
but are not limited to, amino acids, enzyme substrates or products
indicating a disease state or condition, other markers of disease
states or conditions, drugs of abuse, therapeutic and/or
pharmacologic agents (e.g., theophylline, anti-HIV drugs, lithium,
anti-epileptic drugs, cyclosporin, chemotherapeutics),
electrolytes, physiological analytes of interest (e.g., urate/uric
acid, carbonate, calcium, potassium, sodium, chloride, bicarbonate
(CO.sub.2), glucose, urea (blood urea nitrogen), lactate/lactic
acid, hydroxybutyrate, cholesterol, triglycerides, creatine,
creatinine, insulin, hematocrit, and hemoglobin), blood gases
(carbon dioxide, oxygen, pH), lipids, heavy metals (e.g., lead,
copper), and the like. In preferred embodiments, the analyte is a
physiological analyte of interest, for example glucose, or a
chemical that has a physiological action, for example a drug or
pharmacological agent.
[0062] In order to facilitate detection of the analyte, an enzyme
can be disposed in the collection reservoir, or, if several
collection reservoirs are used, the enzyme can be disposed in
several or all of the reservoirs. The selected enzyme is capable of
catalyzing a reaction with the extracted analyte (in this case
glucose) to the extent that a product of this reaction can be
sensed, e.g., can be detected electrochemically from the generation
of a current which current is detectable and proportional to the
concentration or amount of the analyte which is reacted. A suitable
enzyme is glucose oxidase which oxidizes glucose to gluconic acid
and hydrogen peroxide. The subsequent detection of hydrogen
peroxide on an appropriate biosensor electrode generates two
electrons per hydrogen peroxide molecule which create a current
which can be detected and related to the amount of glucose entering
the device. Glucose oxidase (GOx) is readily available commercially
and has well known catalytic characteristics. However, other
enzymes can also be used, so long as they specifically catalyze a
reaction with an analyte or substance of interest to generate a
detectable product in proportion to the amount of analyte so
reacted.
[0063] In like manner, a number of other analyte-specific enzyme
systems can be used in the invention, which enzyme systems operate
on much the same general techniques. For example, a biosensor
electrode that detects hydrogen peroxide can be used to detect
ethanol using an alcohol oxidase enzyme system, or similarly uric
acid with urate oxidase system, urea with a urease system,
cholesterol with a cholesterol oxidase system, and theophylline
with a xanthine oxidase system.
[0064] In addition, the oxidase enzyme (used for hydrogen
peroxide-based detection) can be replaced with another redox
system, for example, the dehydrogenase-enzyme NAD-NADH, which
offers a separate route to detecting additional analytes.
Dehydrogenase-based sensors can use working electrodes made of gold
or carbon (via mediated chemistry). Examples of analytes suitable
for this type of monitoring include, but are not limited to,
cholesterol, ethanol, hydroxybutyrate, phenylalanine,
triglycerides, and urea. Further, the enzyme can be eliminated and
detection can rely on direct electrochemical or potentiometric
detection of an analyte. Such analytes include, without limitation,
heavy metals (e.g., cobalt, iron, lead, nickel, zinc), oxygen,
carbonate/carbon dioxide, chloride, fluoride, lithium, pH,
potassium, sodium, and urea. Also, the sampling system described
herein can be used for therapeutic drug monitoring, for example,
monitoring anti-epileptic drugs (e.g., phenytion), chemotherapy
(e.g., adriamycin), hyperactivity (e.g., ritalin), and
anti-organ-rejection (e.g., cyclosporin).
[0065] The methods for measuring the concentration of a target
analyte can be generalized as follows. An initial step (Step A)
entails obtaining a raw signal from a sensing device, which signal
is related to a target analyte present in the biological system.
The raw signal can be obtained using any suitable sensing
methodology including, for example, methods which rely on direct
contact of a sensing apparatus with the biological system; methods
which extract samples from the biological system by invasive,
minimally invasive, and non-invasive sampling techniques, wherein
the sensing apparatus is contacted with the extracted sample;
methods which rely on indirect contact of a sensing apparatus with
the biological system; and the like. In preferred embodiments of
the invention, methods are used to extract samples from the
biological sample using minimally invasive or non-invasive sampling
techniques. The sensing apparatus used with any of the above-noted
methods can employ any suitable sensing element to provide the
signal including, but not limited to, physical, chemical,
electrochemical, photochemical, spectrophotometric, polarimetric,
colorimetric, radiometric, or like elements. In preferred
embodiments of the invention, a biosensor is used which comprises
an electrochemical sensing element.
[0066] After the raw signal has been obtained, the signal can
undergo a data screening method (Step B) in order to eliminate
outlier signals and/or poor (incorrect) signals using a predefined
set of selection criteria. In addition, or alternatively, the raw
signal can be converted in a conversion step (Step C) which can (i)
remove or correct for background information, (ii) integrate the
signal over a sensing time period, (iii) perform any process which
converts the signal from one signal type to another, or (iv)
perform any combination of steps (i), (ii) and/or (iii). In
preferred embodiments, the conversion step entails a baseline
background subtraction method to remove background from the raw
signal and an integration step. In other embodiments, the
conversion step can be tailored for use with a sensing device that
provides both active and reference (blank) signals; wherein
mathematical transformations are used to individually smooth active
and reference signals, and/or to subtract a weighted reference
(blank) signal from the active signal. In still further
embodiments, the conversion step includes correction functions
which account for changing conditions in the biological system
and/or the biosensor system (e.g., temperature fluctuations in the
biological system, temperature fluctuations in the sensor element,
skin conductivity fluctuations, or combinations thereof). The
result of the conversion step is an initial signal output which
provides a value which can be correlated with the concentration of
the target analyte in the biological sample.
[0067] In a calibration step (Step D), the raw signal obtained from
Step A, or the initial signal obtained from Step B and/or Step C,
is converted into an analyte-specific value of known units to
provide an interpretation of the signal obtained from the sensing
device. The interpretation uses a one-to-one mathematical
transformation to model the relationship between a measured
response in the sensing device and a corresponding analyte-specific
value. Thus, the calibration step is used herein to relate, for
example, an electrochemical signal (detected by a biosensor) with
the concentration of a target analyte in a biological system. In
one embodiment, the calibration step entails calibrating the
sensing device using a single- or multi-point calibration, and then
converting post-calibration data using correlation factors, time
corrections and constants to obtain an analyte-specific value.
Further signal processing can be used to refine the information
obtained in the calibration step, for example, where a signal
processing step is used to correct for signal differences due to
variable conditions unique to the sensor element used to obtain the
raw signal. In one embodiment, this further step is used to correct
for signal time-dependence, particularly signal decline. In another
embodiment, a constant offset term is obtained, which offset is
added to the signal to account for a non-zero signal at an
estimated zero analyte concentration.
[0068] The analyte value obtained using the above techniques can
optionally be used in a subsequent step (Step E) to predict future
(time forecasting) or past (calibration) measurements of the target
analyte concentration in the biological system. For example, a
series of analyte values are obtained by performing any combination
of Steps A, B, C, and/or D in an iterative manner. This measurement
series is then used to predict unmeasured analyte values at
different points in time, future or past. In this manner, lag times
inherent in certain sampling and/or sensing techniques can be
reduced or eliminated to provide real time measurement
predictions.
[0069] In another optional step, analyte values obtained using the
above techniques can be used in a subsequent step (Step F) to
control an aspect of the biological system. In one embodiment, the
analyte value obtained in Step D is used to determine when, and at
what level, a constituent should be added to the biological system
in order to control an aspect of the biological system. In a
preferred embodiment, the analyte value can be used in a feedback
control loop to control a physiological effect in the biological
system.
[0070] The above general methods (Steps A through F) are each
independently useful in analyte sensing systems and can, of course,
be used in a wide variety of combinations selected for a particular
biological system, target analyte, and/or sensing technique. For
example, in certain applications, a measurement sequence can
include Steps A, C, D, E and F, in other applications, a
measurement sequence can include Steps A, B, C and D, and the like.
The determination of particularly suitable combinations is within
the skill of the ordinarily skilled artisan when directed by the
instant disclosure. Furthermore, Steps C through F are preferably
embodied as one or more mathematical functions as described herein
below. These functions can thus be carried out using a
microprocessor in a monitoring system. Although these methods are
broadly applicable to measuring any chemical analyte and/or
substance in a biological system, the invention is expressly
exemplified for use in a non-invasive, transdermal sampling system
which uses an electrochemical biosensor to quantify or qualify
glucose or a glucose metabolite.
Step A: Obtaining the Raw Signal.
[0071] The raw signal can be obtained using any sensing device that
is operatively contacted with the biological system. Such sensing
devices can employ physical, chemical, electrochemical,
spectrophotometric, polarimetric, calorimetric, radiometric, or
like measurement techniques. In addition, the sensing device can be
in direct or indirect contact with the biological system, or used
with a sampling device which extracts samples from the biological
system using invasive, minimally invasive or non-invasive sampling
techniques. In preferred embodiments, a minimally invasive or
non-invasive sampling device is used to obtain samples from the
biological system, and the sensing device comprises a biosensor
with an electrochemical sensing element. In particularly preferred
embodiments, a sampling device is used to obtain continual
transdermal or transmucosal samples from a biological system, and
the analyte of interest is glucose.
[0072] More specifically, a non-invasive glucose monitoring device
is used to measure changes in glucose levels in an animal subject
over a wide range of glucose concentrations. The sampling method is
based on transdermal glucose extraction and the sensing method is
based on electrochemical detection technology. The device can be
contacted with the biological system continuously, and
automatically obtains glucose samples in order to measure glucose
concentration at preprogrammed intervals.
[0073] Sampling is carried out continually by non-invasively
extracting glucose through the skin of the patient. More
particularly, an iontophoretic current is applied to a surface of
the skin of a subject. When the current is applied, ions or charged
molecules pull along other uncharged molecules or particles such as
glucose which are drawn into a collection reservoir placed on the
surface of the skin. The collection reservoir may comprise any
ionically conductive material and is preferably in the form of a
hydrogel which is comprised of a hydrophilic material, water and an
electrolyte.
[0074] The collection reservoir may further contain an enzyme which
catalyzes a reaction of glucose to form an easily detectable
species. The enzyme is preferably glucose oxidase (GOx) which
catalyzes the reaction between glucose and oxygen and results in
the production of hydrogen peroxide. The hydrogen peroxide reacts
at a catalytic surface of a biosensor electrode, resulting in the
generation of electrons which create a detectable biosensor current
(raw signal). Based on the amount of biosensor current created over
a given period of time, a measurement is taken, which measurement
is related to the amount of glucose drawn into the collection
reservoir over a given period of time. In a preferred embodiment,
the reaction is allowed to continue until substantially all of the
glucose in the collection reservoir has been subjected to a
reaction and is therefore no longer detectable, and the biosensor
current generated is related to the concentration of glucose in the
subject at the approximate time of sample collection.
[0075] When the reaction is complete, the process is repeated and a
subsequent measurement is obtained. More specifically, the
iontophoretic current is again applied, glucose is drawn through
the skin surface into the collection reservoir, and the reaction is
catalyzed in order to create a biosensor current. These sampling
(extraction) and sensing operations are integrated such that
glucose is extracted into the hydrogel collection pad where it
contacts the GOx enzyme. The GOx enzyme converts glucose and oxygen
in the hydrogel to hydrogen peroxide which diffuses to the sensor
and is catalyzed by the sensor to regenerate oxygen and form
electrons. The electrons generate an electrical signal that can be
measured, analyzed, and correlated to blood glucose. Optionally,
one or more additional "active" collection reservoirs (each
containing the GOx enzyme) can be used to obtain measurements. In
one embodiment, two active collection reservoirs are used, and an
average is taken between signals from the reservoirs for each
measurement time point. Obtaining multiple signals, and then
averaging reads from each signals, allows for signal smoothing of
unusual data points from a sensor that otherwise may not have been
detected by data screening techniques. Furthermore, skin site
variability can be detected, and "lag" and/or "lead" differences in
blood glucose changes relative to extracted glucose changes can be
mitigated. In another embodiment, a second collection reservoir can
be provided which does not contain the GOx enzyme. This second
reservoir can serve as an internal reference (blank) for the
sensing device, where a biosensor is used to measure the "blank"
signal from the reference reservoir which signal is then used in a
blank subtraction step as described below.
[0076] A generalized method for continual monitoring of a
physiological analyte is disclosed in International Publication No.
WO 97/24059, published 10 Jul. 1997, which publication is
incorporated herein by reference. As noted in that publication, the
analyte is extracted into a reservoir containing a hydrogel which
is preferably comprised of a hydrophilic material of the type
described in International Publication No. WO 97/02811, published
30 Jan. 1997, which publication is incorporated herein by
reference. Suitable hydrogel materials include polyethylene oxide,
polyacrylic acid, polyvinylalcohol and related hydrophilic
polymeric materials combined with water to form an aqueous gel.
[0077] In the above non-invasive glucose monitoring device, a
biosensor electrode is positioned on a surface of the hydrogel
opposite the surface contacting the skin. The sensor electrode acts
as a detector which detects current generated by hydrogen peroxide
in the redox reaction, or more specifically detects current which
is generated by the electrons generated by the redox reaction
catalyzed by the platinum surface of the electrode. The details of
such electrode assemblies and devices for iontophoretic extraction
of glucose are disclosed in International Publication No. WO
96/00110, published 4 Jan. 1996, and International Publication No.
WO 97/10499, published 2 Mar. 1997, which publications are also
incorporated herein by reference.
[0078] Referring now to FIGS. 1A and 1B, an iontophoretic
collection reservoir and electrode assembly for use in a
transdermal sensing device is generally indicated at 2. The
assembly comprises two iontophoretic collection reservoirs, 4 and
6, each having a conductive medium 8, and 10 (preferably
cylindrical hydrogel pads), respectively disposed therein. First
(12) and second (14) ring-shaped iontophoretic electrodes are
respectively contacted with conductive medium 8 and 10. The first
iontophoretic electrode 12 surrounds three biosensor electrodes
which are also contacted with the conductive medium 8, a working
electrode 16, a reference electrode 18, and a counter electrode 20.
A guard ring 22 separates the biosensor electrodes from the
iontophoretic electrode 12 to minimize noise from the iontophoretic
circuit. Conductive contacts provide communication between the
electrodes and an associated power source and control means as
described in detail below. A similar biosensor electrode
arrangement can be contacted with the conductive medium 10, or the
medium can not have a sensor means contacted therewith.
[0079] Referring now to FIG. 2, an exploded view of the key
components from a preferred embodiment of an iontophoretic sampling
system is presented. In FIG. 2, the iontophoretic collection
reservoir and electrode assembly 2 of FIGS. 1A and 1B is shown in
exploded view in combination with a suitable iontophoretic sampling
device housing 32. The housing can be a plastic case or other
suitable structure which preferably is configured to be worn on a
subjects arm in a manner similar to a wrist watch. As can be seen,
conductive media 8 and 10 (hydrogel pads) are separable from the
assembly 2; however, when the assembly 2 and the housing 32 are
assembled to provide an operational iontophoretic sampling device
30, the media are in contact with the electrodes to provide a
electrical contact therewith.
[0080] In one embodiment, the electrode assemblies can include
bimodal electrodes as shown in FIG. 3.
[0081] Referring now to FIG. 5, an exploded view of the key
components from one embodiment of an iontophoretic sampling system
(e.g., one embodiment of an autosensor assembly) is presented. The
sampling system components include two biosensor/iontophoretic
electrode assemblies, 504 and 506, each of which have an annular
iontophoretic electrode, respectively indicated at 508 and 510,
which encircles a biosensor 512 and 514. The electrode assemblies
504 and 506 are printed onto a polymeric substrate 516 which is
maintained within a sensor tray 518. A collection reservoir
assembly 520 is arranged over the electrode assemblies, wherein the
collection reservoir assembly comprises two hydrogel inserts 522
and 524 retained by a gel retaining layer 526 and a mask layer
528.
[0082] In one embodiment, the electrode assemblies can include
bimodal electrodes as shown in FIG. 3. Modifications and additions
to the embodiment of FIG. 5 will be apparent to those skilled in
the art in light of the teachings of the present specification.
[0083] The components described herein are intended for use in a
automatic sampling device which is configured to be worn like an
ordinary wristwatch. As described in International Publication No.
WO 96/00110, published 4 Jan. 1996, the wristwatch housing (not
shown) contains conductive leads which communicate with the
iontophoretic electrodes and the biosensor electrodes to control
cycling and provide power to the iontophoretic electrodes, and to
detect electrochemical signals produced at the biosensor electrode
surfaces. The wristwatch housing can further include suitable
electronics (e.g., microprocessor, memory, display and other
circuit components) and power sources for operating the automatic
sampling system.
[0084] Modifications and additions to the embodiment of FIG. 2 will
be apparent to those skilled in the art in light of the teachings
of the present specification.
[0085] A power source (e.g., one or more rechargeable or
nonrechargeable batteries) can be disposed within the housing 32 or
within the straps 34 which hold the device in contact with a skin
or mucosal surface of a subject. In use, an electric potential
(either direct current or a more complex waveform) is applied
between the two iontophoretic electrodes 12 and 14 such that
current flows from the first iontophoretic electrode 12, through
the first conductive medium 8 into the skin or mucosal surface, and
then back out through the second conductive medium 10 to the second
iontophoretic electrode 14. The current flow is sufficient to
extract substances including an analyte of interest through the
skin into one or both of collection reservoirs 4 and 6. The
electric potential may be applied using any suitable technique, for
example, the applied current density may be in the range of about
0.01 to 0.5 mA/cm.sup.2. In a preferred embodiment, the device is
used for continual or continuous monitoring, and the polarity of
iontophoretic electrodes 12 and 14 is alternated at a rate of about
one switch every 10 seconds to about one switch every hour so that
each electrode is alternately a cathode or an anode. The housing 32
can further include an optional temperature sensing element (e.g.,
a thermistor, thermometer, or thermocouple device) which monitors
the temperature at the collection reservoirs to enable temperature
correction of sensor signals as described in detail below. The
housing can also include an optional conductance sensing element
(e.g., an integrated pair of electrodes) which monitors conductance
at the skin or mucosal surface to enable data screening correction
or invalidation of sensor signals as also described in detail
below.
[0086] After a suitable iontophoretic extraction period, one or
both of the sensor electrode sets can be activated in order to
detect extracted substances including the analyte of interest.
Operation of the iontophoretic sampling device 30 is controlled by
a controller 36 (e.g., a microprocessor), which interfaces with the
iontophoretic electrodes, the sensor electrodes, the power supply,
the optional temperature and/or conductance sensing elements, a
display and other electronics. For example, the controller 36 can
include a programmable a controlled circuit source/sink drive for
driving the iontophoretic electrodes. Power and reference voltage
are provided to the sensor electrodes, and signal amplifiers can be
used to process the signal from the working electrode or
electrodes. In general, the controller discontinues the
iontophoretic current drive during sensing periods. A sensor
confidence loop can be provided for continually monitoring the
sampling system to insure proper operations.
[0087] In a further aspect, the sampling device can operate in an
alternating polarity mode using first and second bimodal electrodes
(FIG. 4, 40 and 41) and two collection reservoirs (FIG. 4, 47 and
48). Each bi-modal electrode (FIG. 3, 30; FIG. 4, 40 and 41) serves
two functions depending on the phase of the operation: (1) an
electro-osmotic electrode (or iontophoretic electrode) used to
electrically draw analyte from a source into a collection reservoir
comprising water and an electrolyte, and to the area of the
electrode subassembly; and (2) as a counter electrode to the first
sensing electrode at which the chemical compound is catalytically
converted at the face of the sensing electrode to produce an
electrical signal.
[0088] The reference (FIG. 4, 44 and 45; FIG. 3, 32) and sensing
electrodes (FIG. 4, 42 and 43; FIG. 3, 31), as well as, the bimodal
electrode (FIG. 4, 40 and 41; FIG. 3, 30) are connected to a
standard potentiostat circuit during sensing. In general, practical
limitations of the system require that the bimodal electrode will
not act as both a counter and iontophoretic electrode
simultaneously.
[0089] The general operation of an iontophoretic sampling system is
the cyclical repetition of two phases: (1) a reverse-iontophoretic
phase, followed by a (2) sensing phase. During the reverse
iontophoretic phase, the first bimodal electrode (FIG. 4, 40) acts
as an iontophoretic cathode and the second bimodal electrode (FIG.
4, 41) acts as an iontophoretic anode to complete the circuit.
Analyte is collected in the reservoirs, for example, a hydrogel
(FIG. 4, 47 and 48). At the end of the reverse iontophoretic phase,
the iontophoretic current is turned off. During the sensing phase,
in the case of glucose, a potential is applied between the
reference electrode (FIG. 4, 44) and the sensing electrode (FIG. 4,
42). The chemical signal reacts catalytically on the catalytic face
of the first sensing electrode (FIG. 4. 42) producing an electrical
current, while the first bi-modal electrode (FIG. 4, 40) acts as a
counter electrode to complete the electrical circuit.
[0090] The electrode described is particularly adapted for use in
conjunction with a hydrogel collection reservoir system for
monitoring glucose levels in a subject through the reaction of
collected glucose with the enzyme glucose oxidase present in the
hydrogel matrix.
[0091] The bi-modal electrode is preferably comprised of Ag/AgCl.
The electrochemical reaction which occurs at the surface of this
electrode serves as a facile source or sink for electrical current.
This property is especially important for the iontophoresis
function of the electrode. Lacking this reaction, the iontophoresis
current could cause the hydrolysis of water to occur at the
iontophoresis electrodes causing pH changes and possible gas bubble
formation. The pH changes to acidic or basic pH could cause skin
irritation or burns. The ability of an Ag/AgCl electrode to easily
act as a source of sink current is also an advantage for its
counter electrode function. For a three electrode electrochemical
cell to function properly, the current generation capacity of the
counter electrode should not limit the speed of the reaction at the
sensing electrode. In the case of a large sensing electrode, the
counter electrode should be able to source proportionately larger
currents.
[0092] The design of the sampling system provides for a larger
sensing electrode (see for example, FIG. 3) than previously
designed. Consequently, the size of the bimodal electrode should be
sufficient so that when acting as a counter electrode with respect
to the sensing electrode the counter electrode does not become
limiting the rate of catalytic reaction at the sensing electrode
catalytic surface.
[0093] Two methods exist to ensure that the counter electrode does
not limit the current at the sensing electrode: (1) the bi-modal
electrode is made much larger than the sensing electrode, or (2) a
facile counter reaction is provided.
[0094] During the reverse iontophoretic phase, the power source
provides a current flow to the first bi-modal electrode to
facilitate the extraction of the chemical signal into the
reservoir. During the sensing phase, the power source is used to
provide voltage to the first sensing electrode to drive the
conversion of chemical signal retained in reservoir to electrical
signal at the catalytic face of the sensing electrode. The power
source also maintains a fixed potential at the electrode where, for
example hydrogen peroxide is converted to molecular oxygen,
hydrogen ions, and electrons, which is compared with the potential
of the reference electrode during the sensing phase. While one
sensing electrode is operating in the sensing mode it is
electrically connected to the adjacent bimodal electrode which acts
as a counter electrode at which electrons generated at the sensing
electrode are consumed.
[0095] The electrode sub-assembly can be operated by electrically
connecting the bimodal electrodes such that each electrode is
capable of functioning as both an iontophoretic electrode and
counter electrode along with appropriate sensing electrode(s) and
reference electrode(s), to create standard potentiostat
circuitry.
[0096] A potentiostat is an electrical circuit used in
electrochemical measurements in three electrode electrochemical
cells. A potential is applied between the reference electrode and
the sensing electrode. The current generated at the sensing
electrode flows through circuitry to the counter electrode (i.e.,
no current flows through the reference electrode to alter its
equilibrium potential). Two independent potentiostat circuits can
be used to operate the two biosensors. For the purpose of the
present sampling system, the electrical current measured at the
sensing electrode subassembly is the current that is correlated
with an amount of chemical signal.
[0097] With regard to continual operation for extended periods of
time, Ag/AgCl electrodes are provided herein which are capable of
repeatedly forming a reversible couple which operates without
unwanted electrochemical side reactions (which could give rise to
changes in pH, and liberation of hydrogen and oxygen due to water
hydrolysis). The Ag/AgCl electrodes of the present sampling system
are thus formulated to withstand repeated cycles of current passage
in the range of about 0.01 to 1.0 mA per cm.sup.2 of electrode
area. With regard to high electrochemical purity, the Ag/AgCl
components are dispersed within a suitable polymer binder to
provide an electrode composition which is not susceptible to attack
(e.g., plasticization) by components in the collection reservoir,
e.g., the hydrogel composition. The electrode compositions are also
formulated using analytical- or electronic-grade reagents and
solvents, and the polymer binder composition is selected to be free
of electrochemically active contaminants which could diffuse to the
biosensor to produce a background current.
[0098] Since the Ag/AgCl iontophoretic electrodes must be capable
of continual cycling over extended periods of time, the absolute
amounts of Ag and AgCl available in the electrodes, and the overall
Ag/AgCl availability ratio, can be adjusted to provide for the
passage of high amounts of charge. Although not limiting in the
sampling system described herein, the Ag/AgCl ratio can approach
unity. In order to operate within the preferred system which uses a
biosensor having a geometric area of 0.1 to 3 cm.sup.2, the
iontophoretic electrodes are configured to provide an approximate
electrode area of 0.3 to 1.0 cm.sup.2, preferably about 0.85
cm.sup.2. These electrodes provide for reproducible, repeated
cycles of charge passage at current densities ranging from about
0.01 to 1.0 mA/cm.sup.2 of electrode area. More particularly,
electrodes constructed according to the above formulation
parameters, and having an approximate electrode area of 0.85
cm.sup.2, are capable of a reproducible 5 total charge passage (in
both anodic and cathodic directions) of 270 mC, at a current of
about 0.3 mA (current density of 0.35 mA/cm.sup.2) for 48 cycles in
a 24 hour period.
[0099] Once formulated, the Ag/AgCl electrode composition is
affixed to a suitable rigid or flexible nonconductive surface as
described above with respect to the biosensor electrode
composition. A silver (Ag) underlayer is first applied to the
surface in order to provide uniform conduction. The Ag/AgCl
electrode composition is then applied over the Ag underlayer in any
suitable pattern or geometry using various thin film techniques,
such as sputtering, evaporation, vapor phase deposition, or the
like, or using various thick film techniques, such as film
laminating, electroplating, or the like. Alternatively, the Ag/AgCl
composition can be applied using screen printing, pad printing,
inkjet methods, transfer roll printing, or similar techniques.
Preferably, both the Ag underlayer and the Ag/AgCl electrode are
applied using a low temperature screen print onto a polymeric
substrate. This low temperature screen print can be carried out at
about 125 to 160.degree. C, and the screening can be carried out
using a suitable mesh, ranging from about 100-400 mesh.
[0100] User control can be carried out using push buttons located
on the housing 32, and an optional liquid crystal display (LCD) can
provide visual prompts, readouts and visual alarm indications. The
microprocessor generally uses a series of program sequences to
control the operations of the sampling device, which program
sequences can be stored in the microprocessor's read only memory
(ROM). Embedded software (firmware) controls activation of
measurement and display operations, calibration of analyte
readings, setting and display of high and low analyte value alarms,
display and setting of time and date functions, alarm time, and
display of stored readings. Sensor signals obtained from the sensor
electrodes are processed before storage and display by one or more
signal processing functions or algorithms which are described in
detail below. The microprocessor can also include an electronically
erasable, programmable, read only memory (EEPROM) for storing
calibration parameters (as described in detail below), user
settings and all downloadable sequences.
Step B: Data Screening Methodologies.
[0101] The raw signal obtained from the above-described glucose
monitoring device can be screened to detect deviations from
expected behavior which are indicative of poor or incorrect signals
that will not correlate with blood glucose. Signals that are
identified as poor or incorrect in this data screen may be
discarded or otherwise corrected for prior to any signal processing
and/or conversion in order to maintain data integrity. In the
method of the invention, an objective set of selection criteria is
established which can then be used to accept or discard signals
from the sensing device. These selection criteria are device- and
analyte-specific, and can be arrived at empirically by way of
testing various devices in particular applications.
[0102] In the particular context of transdermal blood glucose
monitoring using iontophoretic extraction and electrochemical
detection, the following data screens can be employed. As discussed
above, the iontophoretic extraction device can include two
collection reservoirs. Thus, in active/blank systems, wherein one
reservoir is active (contains the GOx enzyme) and one reservoir is
blank, each reservoir contains an iontophoretic electrode and a
sensing electrode. Signals from both the active and the blank
reservoirs are screened, and an error in either the active, or the
active and blank signal can be used to invalidate or correct the
measurement from the cycle. In multiple active systems (wherein two
or more reservoirs contain the GOx enzyme and iontophoretic and
sensing electrodes), signals from one or more of the active
reservoirs are screened, and an error can be used to invalidate or
correct the measurement from the cycle.
[0103] As with any chemical sensing method, transient changes in
temperature during or between measurement cycles, or between
measurements of blank and active signals, can alter background
signal, reaction constants and/or diffusion coefficients.
Accordingly, a temperature sensor is used to monitor changes in
temperature over time. A maximum temperature change over time
(d(temp)/d(time)) threshold value can then be used in a data screen
to invalidate a measurement. Such a threshold value can, of course,
be set at any objective level, which in turn can be empirically
determined depending upon the particular extraction/sensing device
used, how the temperature measurement is obtained, and the analyte
being detected. Absolute temperature threshold criteria can also be
employed, wherein detection of high and/or low temperature extremes
can be used in a data screen to invalidate a measurement.
Temperature monitoring can be carried out using a separate,
associated temperature sensing device, or, preferably using a
temperature sensor that is integral with the sensing device. A
large number of temperature sensing elements are known in the art
(e.g., thermometers, thermistors, thermocouples, and the like)
which can be used to monitor the temperature in the collection
reservoirs.
[0104] Another data screen entails monitoring physiological
conditions in the biological system, particularly monitoring for a
perspiration threshold. In this regard, perspiration contains
glucose, and perspiration occurring rapidly and in sufficient
quantities may affect the detected signal either before or during
biosensor measurement. Accordingly, a sensor can be used to monitor
perspiration levels for a given measurement cycle at time points
before, during, and/or after iontophoresis, and before, during,
and/or after glucose sensing. Detection of perspiration levels that
exceed an objective threshold is then used in a data screen to
invalidate poor measurements. Although a number of different
mechanisms can be used, skin conductance can be readily measured
with a device contacted with the skin. Skin conductivity is related
to perspiration. In one embodiment, if skin conductance as measured
by a conductivity detector is greater than a predetermined level,
then the corresponding measurement is invalidated.
[0105] Yet further data screens which are used in the practice of
the invention take into consideration the expected behavior of the
sampling/sensing device. In iontophoretic sampling, for example,
there is a skin equilibration period before which measurements will
generally be less accurate. During this equilibration period, the
system voltage can be assessed and compared against an objective
high voltage threshold. If this high voltage limit is exceeded, a
data screen is used to exclude the corresponding analyte
measurement, since the iontophoretic current was not at a target
value due to high skin resistance (as indicted by the high voltage
level).
[0106] In addition, the electrochemical signal during each sensing
cycle is expected to behave as a smooth, monotonically decreasing
signal which represents depletion of the hydrogen peroxide by the
sensor electrode. Significant departure from this expected behavior
is indicative of a poor or incorrect measurement (e.g., a
non-monotonically decreasing signal is indicative of excessive
noise in the biosensor signal), and thus monitoring signal behavior
during sensing operations provides yet a further data screen for
invalidating or correcting measurements.
[0107] Raw signal thresholds can also be used in the data screening
method of the present invention. For example, any sensor reading
that is less than some minimum threshold can indicate that the
sampling/sensing device is not operating correctly, for example,
where the biosensor electrode is disconnected. In addition, any
chemical sensor will have a maximum range in which the device can
operate reliably. A reading greater than some maximal value, then,
indicates that the measurement is off-scale, and thus possibly
invalid. Accordingly, minimum and maximum signal thresholds are
used herein as data screens to invalidate or correct measurements.
Such minimum and maximum thresholds can likewise be applied to
background measurements.
[0108] A general class of screens can be applied that detect
changes in signal, background, or voltage measurements. These
screens are useful to assess the consistency of measurements and
can detect problems or inconsistencies in the measurements. Error
messages can be relayed to a display screen on the monitoring
device, and/or, recorded to a log. Examples of such screens include
the following:
[0109] (i) signal--Peak Stability. A large change in the peak of a
sensor reading indicates a noisy signal. The peak of any given
cathodal half cycle is defined as the difference between the first
biosensor point and the temperature corrected average of the last
two points from the previous anodal half cycle. If the percentage
difference between successive peaks from the same sensor is greater
than a predetermined value, for example, 30%, then an error is
indicated.
[0110] (ii) background--Background Precision. Divergent readings at
the end of biosensing indicate an unstable biosensor signal.
Because these readings are used to assess background current for a
particular cycle, an unstable signal may lead to an erroneous data
point. If the difference between the last two anodal points (where
the last two anodal points are typically the last two biosensor
currents measured after anodal extraction) used to calculate the
baseline is greater than or equal to a predetermined value, for
example, 6 nA (or, e.g., a percentage of the first anodal point
relative to the second anodal point), then an error is
indicated.
[0111] (iii) background--Background Stability. This check is to
determine if the background current is changing too excessively,
which indicates a noisy signal and can result in inaccurate glucose
readings. If the percentage difference between successive
background measurements is greater than or equal to a predetermined
value, for example, 15%, then an error is indicated.
[0112] (iv) voltage--Voltage Stability. If the glucose monitoring
device is mechanically disturbed, there can be a larger change
(e.g., larger relative to when the monitor is functioning under
normal conditions) in iontophoresis voltage. This could lead to an
aberrant reading. If the percentage difference between successive
cathodal or anodal iontophoresis voltages is grater than a
predetermined value, for example, 15%, then an error is
indicated.
[0113] (v) voltage--Reference Electrode Check. When the electrode
assembly includes a reference electrode (as when, for example, a
bimodal electrode is employed) this check establishes the
connectivity of the reference electrode to the sampling device and
to the working electrode. The biosensor is activated such that a
current should flow from the working electrode to the reference
electrode. If the current measured is less than a threshold value,
then an error is indicated and the measurement sequence can be
terminated.
[0114] As will be appreciated by one of ordinary skill in the art
upon reading this specification, a large number of other data
screens can be employed without departing from the spirit of the
present invention.
Step C: The Conversion Step.
[0115] Continuing with the method of the invention, the
above-described iontophoretic sampling device is used to extract
the analyte from the biological system, and a raw amperometric
signal (e.g., nanoampere (nA) signal) is generated from the
associated electrochemical biosensor device. This raw signal can
optionally be subjected to a data screening step (Step B) to
eliminate poor or incorrect signals, or can be entered directly
into a conversion step to obtain an initial signal output which is
indicative of the amount of analyte extracted by the sampling
system.
[0116] I. Ways of Obtaining Integrated Signals
[0117] 1. Baseline Background.
[0118] In one embodiment, the raw or screened raw signal is
processed in the conversion step in order to remove or correct for
background information present in the signal. For example, many
sensor devices will have a signal whether or not an analyte of
interest is present, i.e., the background signal. One such
background signal is the "baseline background," which, in the
context of electrochemical detection, is a current (nA) generated
by the sensing device independent of the presence or absence of the
analyte of interest. This baseline background interferes with
measurement of analyte of interest, and the amount of baseline
background can vary with time, temperature and other variable
factors. In addition, electrochemically active interfering species
and/or residual analyte can be present in the device which will
further interfere with measurement of the analyte of interest.
[0119] This background can be transient background, which is a
current generated independent of the presence or absence of the
analyte of interest and which decreases over the time of sensor
activation on the time scale of a measurement, eventually
converging with the baseline background signal.
[0120] Accordingly, in one embodiment of the invention, a baseline
background subtraction method is used during the conversion step in
order to reduce or eliminate such background interferences from the
measured initial signal output. The subtraction method entails
activation of the electrochemical sensor for a sufficient period of
time to substantially reduce or eliminate residual analyte and/or
electrochemical signal that is not due to the analyte (glucose).
After the device has been activated for a suitable period of time,
and a stable signal is obtained, a measurement is taken from the
sensor which measurement can then be used to establish a baseline
background signal value. This background signal value is subtracted
from an actual signal measurement value (which includes both
analyte-specific and background components) to obtain a corrected
measurement value. This baseline background subtraction method can
be expressed using the following function:
i(.tau.)=i.sub.raw(.tau.)-i.sub.bkgnd(.tau.) wherein:
(i.sub.raw(.tau.)) is the current measured by the sensor (in nA) at
time .tau.; (.tau.) is the time after activation of the sensor;
(i.sub.bkgnd(.tau.)) is the background current (in nA); and
(i(.tau.)) is the corrected current (in nA). Measurement of the
baseline background signal value is taken close in time to the
actual signal measurement in order to account for temperature
fluctuations, background signal drift, and like variables in the
baseline background subtraction procedure. The baseline background
signal value can be integrated for use with coulometric signal
processing, or used as a discrete signal value in amperometric
signal processing. In particular embodiments of the invention,
continual measurement by the iontophoretic sampling device provides
a convenient source for the baseline background measurement, that
is, after an initial measurement cycle has be completed, the
baseline background measurement can be taken from a previous
measurement (sensing) cycle.
[0121] 2. Temperature Correcting Baseline Background.
[0122] In yet another embodiment of the invention, the conversion
step is used to correct for changing conditions in the biological
system and/or the biosensor system (e.g., temperature fluctuations
in the biological system, temperature fluctuations in the biosensor
element, or combinations thereof). Temperature can affect the
signal in a number of ways, such as by changing background,
reaction constants, and/or diffusion coefficients. Accordingly, a
number of optional temperature correction functions can be used in
order to reduce these temperature-related effects on the
signal.
[0123] In order to correct for the effect that temperature
fluctuations or differences may have on the baseline background
subtracted signal, the following temperature correction step can be
carried out. More particularly, to compensate for temperature
fluctuations, temperature measurements can be taken at each
measurement time point within the measurement cycle, and this
information can be used to base a temperature correction algorithm
which adjusts the background current at every time point depending
on the difference in temperature between that time point and the
temperature when the previous background current was measured. This
particular temperature correction algorithm is based on an
Arrhenius relationship between the background current and
temperature.
[0124] The temperature correction algorithm assumes an
Arrhenius-type temperature dependence on the background current,
such as: i bkgrid = A .times. .times. exp .function. [ - K .times.
.times. 1 T ] ##EQU1## wherein: (i.sub.bkgnd) is the background
current; (A) is a constant; (K1) is termed the "Arrhenius slope"
and is an indication of how sensitive the current is to changes in
temperature; and (T) is the temperature in .degree. K.
[0125] Plotting the natural log of the background current versus
the reciprocal of temperature provides a linear function having a
slope of (-K1). Using a known or derived value of K1 allows the
baseline current at any time (.tau.) to be corrected using the
following function (which is referred to herein as the "K1
temperature correction"): i bkgnd , corrected = i bkgnd , .tau. 0
.times. exp .function. [ - K .times. .times. 1 .times. ( 1 T .tau.
- 1 T .tau. 0 ) ] ##EQU2## wherein: (i.sub.bkgnd, corrected) is the
temperature corrected baseline current; (i.sub.bkgnd, .tau.0) is
the baseline current at some reference temperature T.sub..tau.0,
for example, the baseline background measurement temperature; (K1)
is the temperature correction constant; and (T.sub..tau.) is the
temperature at time .tau.. For the purposes of the invention,
(i.sub.bkgnd, .tau.0) is usually defined as the "previous" baseline
current. As can be seen, instead of making a time-independent
estimation of the baseline current, the K1 temperature correction
adjusts the baseline current in an Arrhenius fashion depending upon
whether the temperature increases or decreases during or between
biosensor cycles. Determination of the constant K1 can be obtained
by plotting the natural log of the background current versus the
reciprocal of the temperature for a learning set of data, and then
using a best fit analysis to fit this plot with a line having a
slope (-K1).
[0126] Raw or screened amperometric signals from Step A or Step B,
respectively (whether or not subjected to the above-described
baseline background subtraction and/or K1 temperature correction),
can optionally be refined in the conversion step to provide
integrated coulometric signals. In one particular embodiment of the
invention, any of the above amperometric signals (e.g., the current
generated by the sensor) can be converted to a coulometric signal
(nanocoulombs (nC)) , which represents the integration of the
current generated by the sensor over time to obtain the charge that
was produced by the electrochemical reaction.
[0127] In one embodiment, integration is carried out by operating
the biosensor in a coulometric (charge-measuring) mode. Measuring
the total amount of charge that passes through the biosensor
electrode during a measurement period is equivalent to
mathematically integrating the current over time. By operating in
the coulometric mode, changes in diffusion constants resulting from
temperature fluctuations, possible changes in the diffusion path
length caused by uneven or non-uniform reservoir thickness, and
changes in sensor sensitivity, have little effect on the integrated
signal, whereas these parameters may have a greater effect on
single point (current) measurements. Alternatively, a functionally
equivalent coulometric measurement can be mathematically obtained
in the method of the invention by taking discrete current
measurements at selected, preferably small, time intervals, and
then using any of a number of algorithms to approximate the
integral of the time-current curve. For example, integrated signal
can be obtained as follows: Y = .intg. .tau. 1 .tau. 2 .times. i
.function. ( .tau. ) .times. d .tau. ##EQU3## wherein: (Y) is the
integrated signal (in nC); and (i(.tau.)) is a current at time
.tau., and can be equal to i.sub.raw(.tau.) for an uncorrected raw
signal, or i.sub.raw(.tau.)-i.sub.bkgnd(.tau.) for a baseline
background subtracted signal, or i.sub.raw(.tau.).sub.-i.sub.bkgnd,
corrected(.tau.) for a baseline background subtracted and
temperature corrected signal.
[0128] 3. Temperature Correction of Active versus Blank
Integrals.
[0129] An additional temperature correction algorithm can be used
herein to compensate for temperature dependence of a transient
background (blank) signal. That is, in the active/blank sampling
system exemplified hereinabove, the analyte measurement (blood
glucose) is generated by integrating an active signal and
subtracting therefrom a blank signal (see the blank subtraction
method, infra). The blank integral may be "artifactually" high or
low depending upon whether blank signal was measured at a higher or
lower temperature than the active signal. In order to normalize the
blank integral to the temperature at which the active signal was
measured, the following function can be used (which is referred to
herein as the "K2 temperature correction"): Y blank , corrected = Y
blank .times. exp .function. [ - K .times. .times. 2 .times. ( 1 T
act n _ - 1 T blank n _ ) ] ##EQU4## wherein: (Y.sub.blank,
corrected) is the corrected blank integral; (Y.sub.blank) is the
uncorrected blank integral (in nC); (K2) is the "blank integral
correction constant"; and (T.sup.n.sub.act) and (T.sup.n.sub.blank)
are the average temperature of the active and blank signal,
respectively. The average temperature is obtained from averaging
the first n temperatures, such that (n) is also an adjustable
parameter. Determination of the constant K2 can be obtained from an
Arrhenius plot of the log of the blank integral against
1/T.sup.n.sub.blank, using the reciprocal of the average of the
first n temperature values, and then using a best fit analysis to
fit this plot with a line having a slope (-K2).
[0130] Alternative temperature corrections which can be performed
during the conversion step are as follows. In one embodiment, an
integral average temperature correction is used wherein, for each
measurement cycle, the integral average temperature is determined
by the function: < T >= 1 T f .times. .intg. 0 T f .times. T
.times. .times. d t ##EQU5## and then correcting for the
temperature at subsequent time points using the function: Y t ,
corrected = Y t .times. exp .function. [ - a .function. ( < T t
> - < T ref > < T ref > ) ] ##EQU6## wherein:
(Y.sub.t) is the uncorrected signal at time t; (Y.sub.t, corrected)
is the corrected signal at time t; (<T.sub.t>) is the
integral average temperature at time t; (<T.sub.ref>) is the
integral average temperature at the reference time (e.g., the
calibration time); (t) is the time after sensor measurement is
first initiated; and (a) is an adjustable parameter which is fit to
the data.
[0131] In other embodiments, temperature correction functions can
be used to correct for temperature differences between multiple
active signals, or between active and blank signals. For example,
in the active/blank sensing device exemplified herein, blank
subtraction is used to cancel out much of the
temperature-dependence in the active signal. However, temperature
transients during the monitoring period will result in varying
background currents, which can result in signal errors when the
current is multiplied by the total integration time in the instant
conversion step. This is particularly true where the active and
blank integrals are disjointed in time, and thus possibly comprised
of sets of background current values that occurred at different
temperatures.
[0132] 4. Anodal Subtraction.
[0133] In yet another alternative temperature correction,
temperature measurements taken in the active and blank reservoirs
at alternating anodal and cathodal phases during a measurement
cycle are used in a subtraction method in order to reduce the
impact of temperature fluctuations on the signals. In this regard,
the active/blank reservoir iontophoretic sampling system can be run
under conditions which alternate the active and blank reservoirs
between anodal and cathodal phases during a measurement cycle. This
allows the blank anodal signal to be measured at the same time as
the active, cathode signal, and temperature variations will likely
have similar impact on the two signals. The temperature correction
function thus subtracts an adjusted anodal signal (taken at the
same time as the cathodal signal) from the cathodal signal in order
to account for the effect of temperature on the background. More
particularly, a number of related temperature correction functions
which involve fractional subtraction of blank anode signals can be
summarized as follows: Y = Y act , cath - d * Y blank , an ##EQU7##
Y = Y act , cath - d * [ Y blank , an - ( Y act , an - Y blank ,
cath ) ] ##EQU7.2## Y = Y act , cath - d * [ Y blank , an - ( Y act
, an - Y blank , cath ) ] .times. ave .times. .times. t 1 , t 2
.times. .times. Y = Y act , cath - d * [ Y blank , an - ( Y blank ,
an - Y blank , cath ) ] .times. ave .times. .times. t 1 -- .times.
t 2 .times. .times. Y = Y act , cath - d * ( Y blank , an - AOS ) *
[ Y blank , cath Y act , an - AOS ] .times. ave .times. .times. t 1
, t 2 .times. .times. Y = Y act , cath - d * ( Y blank , an - AOS )
* [ Y blank , cath Y act , an - AOS ] .times. ave .times. .times. t
1 -- .times. t 1 ##EQU7.3## wherein: (Y.sub.act, catch) is the
active signal in the cathodal phase (in nC) ; (Y.sub.blank, an) is
the blank signal in the anodal phase (in nC); (Y.sub.act, an) is
the active signal in the anodal phase (in nC); (Y.sub.blank, catch)
is the blank signal in the cathodal phase (in nC); (Y) is the
"blank anode subtracted" signal; (ave t.sub.1, t.sub.2) is the
average of signals taken at two time points t.sub.1, and t.sub.2;
(ave t.sub.1-t.sub.2) is the average of signals taken over the time
period of t.sub.1-t.sub.2; (d) is a universal fractional weight and
is generally a function of time; and (AOS) is a universal anodal
offset which can be empirically obtained using standard
mathematical techniques, and optionally adjusted using data taken
from two previous time points, t.sub.1 and t.sub.2 (i.e., ave
t.sub.1, t.sub.2) or using the average of data taken over the time
period of t.sub.1-t.sub.2 (i.e., ave t.sub.1-t.sub.2).
[0134] In still further embodiments of the invention, the
conversion step can include a blank subtraction step, combined data
from two active reservoirs, and/or a smoothing step.
[0135] The blank subtraction step is used to subtract the blank
signal from the active signal in order to remove signal components
that are not related to the analyte, thus obtaining a cleaner
analyte signal. When raw signal is obtained from two active
reservoirs the two raw signals can be averaged or a summed value of
the two raw signals can be used. In the smoothing step,
mathematical transformations are carried out which individually
smooth signals obtained from the active and blank collection
reservoirs. These smoothing algorithms help improve the
signal-to-noise ratio in the biosensor, by allowing one to correct
the signal measurements obtained from the device to reduce unwanted
noise while maintaining the actual signal sought.
[0136] More particularly, a blank subtraction step is used in the
active-blank iontophoretic sampling system of the invention as
follows. Signals from the blank (second) reservoir, taken at, or
about the same time as signals from the active (first) reservoir,
are used to substantially eliminate signal components from the
active signal that are not specifically related to the analyte. In
this regard, the blank reservoir contains all of the same
components as the active reservoir except for the GOx enzyme, and
the blank signal should thus exhibit similar electrochemical
current to the active signal, except for the signal associated with
the analyte. Accordingly, the following function can be used to
subtract the blank signal from the active signal: Y.sub.t=Y.sub.t,
act-d*Y.sub.t, blank wherein: (Y.sub.t,act) is the active signal
(in nC) at time t; (Y.sub.t,blank) is the blank signal (in nC) at
time t; (Y.sub.t) is the "blank subtracted" signal at time t; and
(d) is the time-dependent fractional weight for the blank signal,
and d preferably =1. In relation to the equation shown above that
is used to subtract the blank signal from the active signal, when
two active reservoirs are used d preferably=-1, or, more generally,
as shown in the equation below, the summed signal can be "weighted"
to account for different contributions of signal from each
reservoir.
[0137] In the case of two active reservoirs, each reservoir is
capable of generating raw signal and each contains all of the same
components. For example, where two collection reservoirs are used
for detecting glucose both reservoirs contain glucose oxidase.
Accordingly, the following function can be used:
Y.sub.t,.epsilon.=aY.sub.t, act1+bY.sub.t, act2 wherein: "a" is the
time-dependent fractional weight for the first active signal;
(Y.sub.t, act1) is the first active signal (in nC) at time t; "b"
is the time-dependent fractional weight for the second active
signal; (Y.sub.t,act2) is the second active signal (in nC) at time
t; (Y.sub.t,.epsilon.) is the summed signal at time t.
[0138] II. General Procedures for Smoothing Integrated Signals.
[0139] In the smoothing step, the active signal obtained from the
first (active) reservoir can be smoothed using a smoothing
function. In multiple active systems, the same smoothing can be
applied to each signal before summing. In one embodiment, the
function can be expressed as a recursive function as follows:
E.sub.t,act=w.sub.actY.sub.t,act+(1-w.sub.act)(E.sub.t-1,act)
wherein: (Y.sub.t, act) is the measurement of the active signal (in
nC) at time t; (E.sub.t, act) is the estimate of the active signal
(in nC) at time t for t>1 (at t=1, E.sub.t, act=Y.sub.t, act)
and (w.sub.act) is the "estimate weight" for the active biosensor,
wherein 0.ltoreq.w.sub.act.ltoreq.1.
[0140] The reference (blank) signal obtained from the second
reservoir can also be smoothed using a similar recursive smoothing
function. This function can be expressed as follows:
E.sub.t,blank=w.sub.t,blankY.sub.t,blank+(1-w.sub.blank)(E.sub.t-1,blank)
wherein: (Y.sub.t, blank) is the measurement of the blank signal
(in nC) at time t; (E.sub.t, blank) is the estimate of the blank
signal (in nC) at time t for t>1 (at t=1, E.sub.t,
blank=Y.sub.t,blank) ; and (W.sub.blank) is the "estimate weight"
for the blank biosensor, wherein 0.ltoreq.w.sub.blank.ltoreq.1.
[0141] Once the active and blank signals have been individually
smoothed, the blank signal can be subtracted from the active signal
in order to obtain a signal that is indicative of the glucose
reaction only. As discussed above, the blank signal should exhibit
a similar electrochemical current to the active signal, except for
the signal associated with the glucose analyte. In the practice of
the invention, this blank subtraction step can subtract the value
of the smoothed blank signal per se, or a weighted blank signal can
be subtracted from the active signal, using the following function
to obtain a fractional subtraction: E.sub.t=E.sub.t,
act-d*E.sub.t,blank wherein: (E.sub.t,act) is the estimate of the
active signal (in nC) at time t; (E.sub.t, blank) is the estimate
of the blank signal (in nC) at time t; (E.sub.t) is the "blank
subtracted" smoothed sensor signal at time t; and (d) is the
time-dependent fractional weight for the blank signal.
[0142] The same recursive function can be used wherein the order of
the smoothing and blank subtraction steps are reversed such that:
(Y.sub.t, act) is the integral of the active signal (in nC) at time
t; (Y.sub.t, blank) is the integral of the blank signal (in nC) at
time t; (Y.sub.t) is the "blank subtracted" sensor signal (in nC)
at time t; (d) is the time-dependent fractional weight for the
blank signal; and Y.sub.t=Y.sub.t,act-d*E.sub.t,blank
E.sub.t=wY.sub.t+(1-w)(E.sub.t-1)
[0143] This smoothing can alternatively be carried out on discrete
(nA) sensor signals, with or without temperature and/or background
subtraction corrections. Smoothing can also be carried out on
active signals or on averages of two or more active signals.
Further modifications to these functions will occur to those of
ordinary skill in the art, in light of the present enabling
disclosure.
Step D: The Calibration Step.
[0144] Continuing with the method of the invention, any of the raw
signals obtained from Step A, the screened raw signal obtained from
Step B, or the initial output signal obtained from Step C (or from
Steps B and C), can be converted into an analyte-specific value
using a calibration step which correlates the signal obtained from
the sensing device with the concentration of the analyte present in
the biological system. A wide variety of calibration techniques can
be used to interpret such signals. These calibration techniques
apply mathematical, statistical and/or pattern recognition
techniques to the problem of signal processing in chemical
analyses, for example, using neural networks, genetic algorithm
signal processing, linear regression, multiple-linear regression,
partial linear regression, deconvolution, or principal components
analysis of statistical (test) measurements.
[0145] One method of calibration involves estimation techniques. To
calibrate an instrument using estimation techniques, it is
necessary to have a set of exemplary measurements with known
concentrations referred to as the calibration set (e.g., reference
set). This set consists of m samples, each with n instrument
variables contained in an m by n matrix (X), and an m by 1 vector
(y), containing the concentrations. If a priori information
indicates the relationship between the measurement and
concentration is linear, the calibration will attempt to determine
an n by 1 transformation or mapping (b), such that y=Xb is an
optimal estimate of y according to a predefined criteria. Numerous
suitable estimation techniques useful in the practice of the
invention are known in the art. These techniques can be used to
provide constant parameters, which can then be used in a
mathematical transformation to obtain a measurement value
indicative of the concentration of analyte present in the
biological system at the times of measurement.
[0146] In one particular embodiment, the calibration step may be
carried out using artificial neural networks or genetic algorithms.
The structure of a particular neural network algorithm used in the
practice of the invention can vary widely; however, the network
should contain an input layer, one or more hidden layers, and one
output layer. Such networks can be optimized on training data set,
and then applied to a population. There are an infinite number of
suitable network types, transfer functions, training criteria,
testing and application methods, which will occur to the ordinarily
skilled artisan upon reading the instant specification.
[0147] In the context of the iontophoretic glucose sampling device
described hereinabove (which can contain an active collection
reservoir--with the GOx enzyme, and a blank collection reservoir;
or alternately, two active reservoirs with the GOx enzyme), a
preferred neural network algorithm would use, for example, inputs
selected from the following to provide a blood glucose measurement:
elapsed time since calibration; signal from the active reservoir;
signal from the blank reservoir; signal from two active reservoirs
(either averaged or summed); calibration time; measured
temperature; applied iontophoretic voltage; skin conductance; blood
glucose concentration, determined by an independent means, at a
defined calibration point; background; background referenced to
calibration; and, when operating in the training mode, measured
glucose.
[0148] Whether or not the calibration step is carried out using
conventional statistical techniques or neural network algorithms,
the calibration step can include a universal calibration process, a
single-point calibration process, or a multi-point calibration
process. In one embodiment of the invention, a universal
calibration process is used, wherein the above mathematical
techniques are used to derive a correlation factor (or correlation
algorithm) that allows for accurate, dependable quantification of
analyte concentration by accounting for varying backgrounds and
signal interferences irrespective of the particular biological
system being monitored. In this regard, the universal calibrant is
selected to provide a close correlation (i.e., quantitative
association) between a particular instrument response and a
particular analyte concentration, wherein the two variables are
correlated.
[0149] In another embodiment, a single-point calibration is used.
More particularly, the single-point calibration process can be used
to calibrate measurements obtained by iontophoretic sampling
methodologies using a reference measurement obtained by
conventional (invasive) methods. Single-point calibration allows
one to account for variables that are unique to the particular
biological system being monitored, and the particular sensing
device that is being used. In this regard, the transdermal sampling
device is generally contacted with the biological system (placed on
the surface of a subject's skin) upon waking. After the device is
put in place, it is preferable to wait a period of time in order
allow the device to begin normal operations.
[0150] Further, the sampling system can be pre-programmed to begin
execution of its signal measurements (or other functions) at a
designated time. One application of this feature is to have the
sampling system in contact with a subject and to program the
sampling system to begin sequence execution during the night so
that it is available for calibration immediately upon waking. One
advantage of this feature is that it removes any need to wait for
the sampling system to warm-up before calibrating it.
[0151] In the context of glucose monitoring, a blood sample can be
extracted when the device has attained normal operations, such that
the invasive blood sample extraction is taken in a corresponding
time period with a measurement cycle. Actual blood glucose levels
can then be determined using any conventional method (e.g.,
calorimetric, electrochemical, spectrophotometric, or the like) to
analyze the extracted sample. This actual value is then used as a
reference value in the single-point calibration process, wherein
the actual value is compared against the corresponding measured
value obtained with the transdermal sampling device. In yet another
embodiment, a multi-point calibration process is used, wherein the
above-described single-point calibration process is repeated at
least once to provide a plurality of point calibrations. For
example, the multi-point calibration process can be carried out at
various time intervals over the course of a continual or continuous
measuring period.
[0152] Continuing with the calibration step, the signals obtained
from Step B and/or Step C, supra, can be subjected to further
signal processing prior to calibration as follows. Referring
particularly to the baseline background subtraction method of the
conversion step (Step C), the corrected signal should theoretically
be directly proportional to the amount of analyte (glucose) present
in the iontophoretically extracted sample. However, sometimes a
non-zero intercept is obtained in the correlation between signal
and reference glucose value. Accordingly, a constant offset term
(which can be positive or negative) is obtained which can be added
to the converted signal to account for a non-zero signal at an
estimated zero blood glucose concentration. The offset can be added
to the active sensor signal; or, in the case of an iontophoretic
sampling system that obtains both active and blank signals, the
offset can be added to the blank-subtracted active signal.
[0153] The calibration step can be carried out using, for example,
the single-point calibration method described hereinabove. The
reference blood glucose concentration thus obtained can then be
used in the following conversion factor: b gain = BG c .times.
.times. a .times. .times. l + .rho. E c .times. .times. a .times.
.times. l + OS ##EQU8## wherein: (E.sub.cal) is the
blank-subtracted smoothed sensor signal (in nC) at calibration;
(BG.sub.cal) is the reference blood glucose concentration (in
mg/dL) at calibration; (b.sub.gain) is the conversion factor
[(mg/dL)/nC]; (OS) is the offset calibration factor constant (in
nC) which can be calculated using standard regression analysis; and
(.rho.) is the calibration offset (in mg/dL). Post calibration data
can then be converted using the following function:
EG.sub.t=b.sub.gain[E.sub.t+OS]-.rho. wherein (EG.sub.t) is the
estimated blood glucose concentration (in mg/dL). Other signal
values, such as Y.sub.t, can be substituted for E.sub.t, and
E.sub.cal depending upon the amount of prior signal processing
performed (see, e.g., Step C, supra).
[0154] Further signal processing can also be used to correct for
time-dependent behavior related to the particular sensor element
that is used in the sensing operation. In this regard, signal
measurements of certain types (such as the electrochemical signal
measurements described herein) exhibit change over time for reasons
which are not fully understood. The present invention is not
premised on any particular theory with respect to why such
time-dependent change occurs. Rather, the invention recognizes that
time-dependent behavior can occur, and corrects for this behavior
using one or more mathematical functions.
[0155] Thus, in one embodiment, a corrected measurement can be
calculated using a mathematical function which compensates for
time-dependent decline in the biosensor signal between measurements
during the period of continual or continuous measuring of the
analyte concentration. The correction function uses one or more
additive decay parameters (.alpha..sub.i) and one or more
multiplicative decay parameters (.epsilon..sub.i), (both of which
are empirically determined for the biosensor),,and can be expressed
as follows: EG t = b gain .function. [ E t .function. ( 1 +
.epsilon. i .times. t ) + OS ] + .alpha. i .times. t - .rho.
.times. .times. wherein : .times. b gain = BG c .times. .times. a
.times. .times. l + .rho. - .alpha. i .times. t c .times. .times. a
.times. .times. l E c .times. .times. a .times. .times. l
.function. ( 1 + .epsilon. i .times. t c .times. .times. a .times.
.times. l ) + OS ##EQU9## and (t.sub.cal) is the calibration point;
(EG.sub.t) is the estimated blood glucose concentration at time t;
(E.sub.t) is the analyte signal at time t; (OS) is the constant
offset term which accounts for a non-zero signal at an estimated
zero blood glucose concentration (as described above); (.epsilon.)
is a gain term for time-dependent signal decline and can have
multiple time segments (e.g., i=1, 2, or 3); (.alpha.) is a
correction term for a linear time-dependent signal decline in the
time segments and can have multiple time segments (e.g., i=1, 2, or
3) ; (t) is the elapsed time, and (.rho.) is the calibration offset
(in mg/dl).
[0156] In an alternative embodiment, a corrected measurement can be
calculated using a mathematical function which compensates for
time-dependent decline in the biosensor signal between
measurements, during the period of continual or continuous
measuring of the analyte concentration, by correlating signal at
the beginning of the measurement series to a unit of decay. The
correction function uses an additive decay parameter (.alpha.) and
a decay correction factor (.gamma.). This equation allows a
time-dependent multiplicative correction to be applied to the
integrated signal in a manner that amplifies, to a greater extent,
those signals that have been observed to decay at a greater rate
(e.g., empirically, signals that give lower BGain tend to decay
faster). Use of the BGAIN factor, as described herein, can insure
that a reasonable calibration factor is obtained.
[0157] In this embodiment, EG.sub.t, the calculated value of blood
glucose at the measurement time, is computed as follows: EG t = ( [
BG c .times. .times. a .times. .times. l - .alpha. .times. .times.
t c .times. .times. a .times. .times. l E c .times. .times. a
.times. .times. l + OS - .gamma. .times. .times. t c .times.
.times. a .times. .times. l ] + .gamma. .times. .times. t ) * ( E t
+ OS ) + .alpha. .times. .times. t ##EQU10## where .times. .times.
BGAIN = [ BG c .times. .times. a .times. .times. l - .alpha.
.times. .times. t c .times. .times. a .times. .times. l E c .times.
.times. a .times. .times. l + OS - .gamma. .times. .times. t c
.times. .times. a .times. .times. l ] ##EQU10.2## wherein:
BG.sub.cal is the true blood glucose at the calibration point;
E.sub.cal, is the analyte signal at calibration; (t.sub.cal) is the
elapsed time of the calibration point; (EG.sub.t) is the estimated
blood glucose concentration at time t; (E.sub.t) is the analyte
signal at time t; (OS) is the constant offset term which accounts
for a non-zero signal at an estimated zero blood glucose
concentration (as described above); (.gamma.) is a time-dependent
correction term for signal decline; (.alpha.) is a time-dependent
correction term for signal decline; and (t) is the elapsed
time.
[0158] Employing these equations a "time segmentation" can be
performed as follows: BGAIN 1 = [ BG cal - .alpha. 1 .times. t cal
E cal + OS - .gamma. 1 .times. t cal ] ##EQU11## if .times. .times.
t < t 12 ##EQU11.2## BGAIN 2 = [ BG cal - .alpha. 1 .times. t 12
- .alpha. 2 .function. ( t cal - t 12 ) E cal + OS - .gamma. 1
.times. t 12 - .gamma. 2 .function. ( t cal - t 12 ) ] ##EQU11.3##
if .times. .times. t 12 < t cal < t 23 ##EQU11.4## BGAIN 3 =
[ BG cal - .alpha. 1 .times. t 12 - .alpha. .times. .times. 2
.times. ( t cal - t 12 ) - .alpha. .times. .times. 3 .times. ( t
cal - t 23 ) E cal + OS - .gamma. 1 .times. t 12 - .gamma. 2
.function. ( t cal - t 12 ) - .gamma. 3 .function. ( t cal - t 23 )
] .times. .times. if .times. .times. t 23 < t cal .times.
.times. EG t = ( BGAIN 1 + .gamma. 1 .times. t ) * ( E t + OS ) +
.alpha. 1 .times. t .times. .times. if .times. .times. t < t 12
.times. .times. EG t = ( BGAIN 2 + .gamma. 1 .times. t 12 + .gamma.
2 .function. ( t - t 12 ) ) * ( E t + OS ) + .alpha. 1 .times. t 12
+ .alpha. 2 .function. ( t - t 12 ) .times. .times. if .times.
.times. t 12 < t < t 23 .times. .times. EG t = ( BGAIN 3 +
.gamma. 1 .times. t 12 + .gamma. 2 .function. ( t 23 - t 12 ) +
.gamma. 3 .function. ( t - t 23 ) ) * ( E t + OS ) + .alpha. 1
.times. t 12 + .alpha. 2 .function. ( t 23 - t 12 ) + .alpha. 3
.function. ( t - t 23 ) .times. .times. if .times. .times. t 23
< t .times. ##EQU11.5## wherein: EG.sub.t is the calculated
value of blood glucose at the measurement time; BG.sub.cal is the
true blood glucose at the calibration point, t is the elapsed time
(hence t.sub.cal is the elapsed time at the calibration point), OS
is the offset parameter, .alpha..sub.i and .gamma..sub.i are the
time dependent correction terms to account for the declining signal
with time. To avoid a dominant time correction term as the elapsed
time increases, the time correction parameters .alpha..sub.i and
.gamma..sub.i are distinct for three different time intervals
("i"): 0 to 6 hours (e.g., i=1), 6 to 10 hours (e.g., i=2), and 10
to 14 hours (e.g., i=3), as shown above. Therefore, t.sub.12=6
hours and t.sub.23=10 hours.
[0159] The time segmentation allows for greater flexibility in
predicting non-linear signal decay terms.
[0160] The signal processing methods and techniques described in
Steps A through D can be combined in a variety of ways to provide
for improved signal processing during analyte measurement. In one
embodiment, an active/blank sampling system is used to obtain the
raw signal in Step A. These raw signals are then screened in Step B
to obtain screened data. These screened data are then subjected to
a temperature correction using the K1 temperature correction, and
then converted using the baseline subtraction and integration
methods of Step C. The converted data are also smoothed (both
active and blank) using the smoothing functions of Step C, the
smoothed data are temperature corrected using the K2 temperature
correction, and a blank subtraction is carried out. The smoothed
and corrected data are then converted to the analyte concentration
in the biological system using the calibration methods of Step D to
perform a single-point calibration, wherein the data is also
refined using the offset and time-dependent behavior corrections to
obtain a highly accurate analyte concentration value.
[0161] In another embodiment, if two active reservoirs
(A.sub.1/A.sub.2) are used, a "sensor consistency check" can be
employed that detects whether the signals from the reservoirs are
changing in concert with one another. This check compares the
percentage change from the calibration signal for each reservoir,
then calculates the difference in percentage change in signal
between the two reservoirs. If this difference is greater than some
threshold, then the signals are not "tracking" one another and this
data point can be screened as in Step B. This check verifies
consistency between the two sensors. A large difference can
indicate noise in the signals.
[0162] In yet another embodiment of the present invention a
"Calibration Factor Check" may be employed. This check provides
control over unreasonable finger prick measurements or incorrect
entries and provides additional assurance that a reasonable
calibration slope has been generated. Typically, there are two
calibration factors that are calculated at calibration: BGAIN and
CAL RATIO. If BGAIN is less than or equal to a predetermined
threshold value, or if the CAL RATIO is greater than or equal to a
predetermined threshold value, then a calibration error is
indicated. Such an error can be displayed to the user, for example,
a calibration window can appear on the monitor's display appear.
Such an error indicates to the users that the user must perform the
calibration again. For the Calibration Factor Check, CAL RATIO can
be calculated as follows: CALRATIO = [ BG cal E cal + OS ]
##EQU12## wherein, BG.sub.cal is the true blood glucose at the
calibration point; E.sub.cal is the analyte signal at calibration;
and (OS) is the constant offset term which accounts for a non-zero
signal at an estimated zero blood glucose concentration. Step E:
Time Forecasting Measurements.
[0163] The corrected analyte value obtained using the above
techniques can be used to predict future (e.g., time forecasting)
or past (e.g., calibration) target analyte concentrations in the
biological system. In one embodiment, a series of analyte values
are obtained by performing any combination of Steps A, B, C, and/or
D, supra, in an iterative manner. These measurements are then used
to predict unmeasured analyte values at different points in time,
future or past.
[0164] More particularly, the above-described iontophoretic
sampling process is carried out in order to obtain three or more
measurements of the target analyte. Using these measurements, an
additional measurement can be calculated. The additional
measurement is preferably calculated using a series function.
[0165] In the context of blood glucose monitoring, it has been
found that the actual (real-time) glucose level in a subject
differs from the measured glucose level obtained using a sampling
device that extracts glucose from the subject using iontophoresis.
The difference is due, in part, to a lag time between extracting
the glucose analyte and obtaining a measurement from the extracted
glucose. This lag time can vary depending on factors such as the
particular subject using the device, the particular area of skin
from which glucose is extracted, the type of collection reservoir
used, and the amount of current applied. In order to compensate for
this inherent lag time, the method of the present invention can
utilize data obtained from previous measurements and a mathematical
function in order to predict what a future analyte concentration
will be. In this case, the predicted future reading can be used as
a "real-time value" of the analyte level.
[0166] In another embodiment, mathematical methods can be used to
predict past measurements, such as in the context of making a
calibration. More particularly, measurements obtained using the
above-described transdermal sampling device can be calibrated
against one or more reference measurements obtained by conventional
(blood extraction) methods. In such calibration processes, actual
blood glucose levels are determined using conventional analytical
methods (e.g., calorimetric, electrochemical, spectrophotometric,
or the like) to analyze an extracted blood sample. These actual
measurements are then compared with corresponding measurements
obtained with the transdermal sampling device, and a conversion
factor is then determined. In normal operations, the transdermal
sampling device is generally first contacted with the biological
system (placed on the surface of a subject's skin) upon waking.
After the device is put in place, it is preferable to wait a period
of time in order allow the device to attain normal operating
parameters, after which time the device can be calibrated. However,
if a blood sample is extracted at the time when the device is first
applied (as would normally be most convenient), there may not be a
corresponding signal from the transdermal sampling system which can
be compared with the reference value obtained from the extracted
blood sample This problem can be overcome using prediction methods
which allow one to perform a conventional blood glucose test (via a
blood sample extraction) when the device is first applied, and then
calibrate the device at a later time against the results of the
conventional glucose test.
[0167] A number of mathematical methods for predicting future or
past measurements can be used in the practice of the invention. For
example, linear or nonlinear regression analyses, time series
analyses, or neural networks can be used to predict such
measurements. However, it is preferred that a novel combination of
exponential smoothing and a Taylor series analysis be used herein
to predict the future or past measurement.
[0168] A number of other physiological variables may be predicted
using the above techniques. For example, these prediction methods
can be used to time forecast those physiological variables that
cannot be measured in real-time, or that demonstrate frequent
fluctuations in their data. Examples of physiological functions and
the variables that characterize them include, but are not limited
to, cerebral blood flow (in the treatment of stroke patients) which
is related to blood viscosity and the concentrations of plasma
proteins and clotting factors in the blood stream (Hachinski, V.
and Norris, J. W., "The Acute Stroke," Philadelphia, F A Davis,
1985); pulmonary function (in asthma patients) as measured by lung
volumes in the different phases of respiration (Thurlbeck, W. M.
(1990) Clin. Chest Med. 11:389); and heart activity (in recurrent
cardiac arrest) as measured by electrical activity of the heart
(Marriott, HJL, "Practical Electrocardiography", 8th Ed.,
Baltimore, Williams & Wilkins, 1983). Other examples of
physiological variables that can be predicted, include renal
dialysis, where blood concentrations of urea and blood gases are
followed. (Warnock, D. G. (1988) Kidney Int. 34:278); and
anesthesia treatment, where various parameters (e.g., heart rate,
blood pressure, blood concentration of the anesthesia) are
monitored to determine when the anesthesia will stop functioning
(Vender, J. S., and Gilbert, H. C., "Monitoring the Anesthetized
Patient," in Clinical Anesthesia, 3rd Ed., by Barash et al.,
Lippincott-Raven Publishers, Philadelphia, 1996).
Step F: Controlling a Physiological Effect.
[0169] The analyte value obtained using the above techniques can
also be used to control an aspect of the biological system. e.g., a
physiological effect. In one embodiment, an analyte value obtained
as described above is used to determine when, and at what level, a
constituent should be added to the biological system in order to
control the concentration of the target analyte.
[0170] More particularly, in the context of blood glucose
monitoring, use of prediction techniques (Step E, supra) allows for
accurate predictions of either real-time or future blood glucose
values. This is of particular value in predicting hypoglycemic
episodes which can lead to diabetic shock, or even coma. Having a
series of measurements obtained from the continual iontophoretic
sampling device, and the capability to predict future values,
allows a subject to detect blood glucose swings or trends
indicative of hypoglycemic or hyperglycemic episodes prior to their
reaching a critical level, and to compensate therefor by way of
exercise, diet or insulin administration.
[0171] A feedback control application of the present invention
entails using a function to predict real-time blood glucose levels,
or measurement values of blood glucose levels at a different time,
and then the same to control a pump for insulin delivery to treat
hyperglycemia.
EXAMPLES
[0172] The following examples are put forth so as to provide those
of ordinary skill in the art with a complete disclosure and
description of how to make and use the devices, methods, and
formulae of the present invention, and are not intended to limit
the scope of what the inventors regard as their invention. Efforts
have been made to ensure accuracy with respect to numbers used
(e.g., amounts, temperature, etc.) but some experimental errors and
deviations should be accounted for. Unless indicated otherwise,
parts are parts by weight, molecular weight is weight average
molecular weight, temperature is in degrees Centigrade, and
pressure is at or near atmospheric.
Example 1
Signal Processing for Measurement of Blood Glucose
[0173] In order to assess the signal processing methods of the
present invention, an iontophoretic sampling device was used to
extract a series of 525 blood glucose samples from an experimental
population of human subjects, and non-processed measurement values
were compared against measurement values obtained using the data
screening and correction algorithm of the present invention.
[0174] More particularly, iontophoretic sampling was performed on
subjects using a Glucowatch.TM. (Cygnus, Inc., Redwood City,
Calif.) iontophoretic sampling system. This transdermal sampling
device, which is designed to be worn like a wrist watch, uses
iontophoresis (electroosmosis) to extract glucose analyte into a
collection pad worn beneath the watch. Glucose collected into the
GlucoWatch.TM. sampling system triggers an electrochemical reaction
with a reagent in the pad, giving rise to a current which is
sensed, measured, and converted to a blood glucose concentration.
Measurements are taken on a continual basis, wherein combined
extraction and sensing (measurement cycles) were set at 30 minutes.
Iontophoresis was carried out using two collection pads contacted
with Ag/AgCl iontophoretic electrodes, an iontophoretic current
density of 0.3 mA/cm.sup.2, and the electrical polarity of the
electrodes was switched halfway through the 30 minute measurement
cycle. Sensing was carried out using platinum-based biosensor
electrodes which were contacted with the collection pads. A
description of the Glucowatch.TM. sampling system can be found in
publication to Conn, T. E. (Jan. 15, 1997) "Evaluation of a
Non-Invasive Glucose Monitoring System for People with Diabetes,"
given at the Institute of Electrical and Electronics Engineers
(IEEE) meeting entitled "Engineering in Medicine & Biology,"
Stanford, Calif., which publication is incorporated herein by
reference.
[0175] Concurrent with obtaining the calculated blood glucose
values (from the GlucoWatch.TM. sampling system), blood samples
(finger sticks) were obtained and analyzed for use as reference
measurements. As a result, 525 sets of paired measurements
(reference and calculated measurements) were obtained. A comparison
was then made between the reference measurements and the calculated
measurements (either raw, or signal processed using the methods of
the invention). Two different sets of data screens were used as
follows: (a) maximum temperature change over time
(d(temp)/d(time)), perspiration threshold, and a threshold
departure from monotonicity (this set of temperature screens is
indicated as (+) in Table 1 below); or (b) maximum temperature
change over time (d(temp)/d(time)), perspiration threshold, a
threshold departure from monotonicity, and a threshold baseline
background change over time (this set of temperature screens is
indicated as (++) in Table 1 below). The correction algorithm that
was used is as follows: EG t = b gain .function. [ E t .function. (
1 + .epsilon. i .times. t ) + OS ] + .alpha. i .times. t - .rho.
.times. .times. wherein .times. : .times. .times. b gain = BG cal +
.rho. - .alpha. cal .times. t E cal .function. ( 1 + .epsilon. i
.times. t cal ) + OS ##EQU13## and (t.sub.cal) is the calibration
point; (EG.sub.t) is the estimated blood glucose concentration at
time t; (E.sub.t) is the analyte signal at time t; (OS) is the
constant offset term which accounts for a non-zero signal at an
estimated zero blood glucose concentration (as described above);
(.epsilon.) is a gain term for time-dependent signal decline and
can have multiple time segments (e.g., i=1, 2, or 3); (.alpha.) is
a correction term for a linear time-dependent signal decline in the
time segments and can have multiple time segments (e.g., i=1, 2, or
3); (t) is the elapsed time, and (.rho.) is the calibration offset
(in mg/dl).
[0176] In the comparison, an Error Grid Analysis (Clarke et al.
(1987) Diabetes Care 10:622-628) was used to assess device
effectiveness, wherein calculated measurements were plotted against
the corresponding reference measurements. An effective blood
glucose monitoring device should have greater than approximately
85-90% of the data in the A and B regions of the Error Grid
Analysis, with a majority of the data in the A region (Clark et
al., supra). The results of the Error Grid Analysis are presented
below in Table 1 as (A+B%). As can be seen, the combination of data
screening methods and the correction algorithm of the present
invention met this effective criteria.
[0177] Another measure of device accuracy is the mean absolute %
error (MPE(%)) which is determined from the mean of individual %
error (PE) given by the following function: PE = EG t - BG t BG t
##EQU14## wherein BG.sub.t is the reference glucose measurement and
EG.sub.t is the calculated glucose measurement. Effective
measurements should have a MPE(%) of about 25% or less. The results
of the MPE(%) are also depicted in Table 1 As can be seen, the
combination of data screening methods and the correction algorithm
of the present invention met this effective criteria.
[0178] The correlation between calculated and measured blood
glucose values was also assessed. The correlation coefficient
values (R) are also presented in Table 1 below. Effective
measurements should have R values of greater than about 0.85. As
can be seen, the combination of data screening methods and the
correction algorithm of the present invention provide for increased
correlation between actual and measured values. TABLE-US-00001
TABLE 1 525 Total Paired Data Algorithm Screen No. pts. MPE(%) A +
B(%) Other(%) R 0 0 525 54 73 27 0.54 + + 467 24 90 10 0.87 + ++
308 20 91 9 0.90
* * * * *