U.S. patent application number 11/266637 was filed with the patent office on 2006-03-09 for method and device for continuous monitoring of the concentration of an analyte.
Invention is credited to Ralph Gillen, Reinhard Kotulla, Arnulf Staib.
Application Number | 20060052679 11/266637 |
Document ID | / |
Family ID | 34177889 |
Filed Date | 2006-03-09 |
United States Patent
Application |
20060052679 |
Kind Code |
A1 |
Kotulla; Reinhard ; et
al. |
March 9, 2006 |
Method and device for continuous monitoring of the concentration of
an analyte
Abstract
The present invention generally relates to a method for
continuous monitoring of the concentration of an analyte by
determining its change over time in the living body of a human or
animal. A measurement variable value correlating with the desired
concentration of the analyte are measured as the measurement signal
(z.sub.t) and the change over time of the concentration is
determined from the measurement signal as the useful signal
(y.sub.t) using a calibration. A filter algorithm is used to reduce
errors of the useful signal, which result from noise contained in
the measurement signal. The filter algorithm includes an operation
in which the influence of an actual measurement value on the useful
signal is weighted using a weighting factor (V).
Inventors: |
Kotulla; Reinhard;
(Lambsheim, DE) ; Staib; Arnulf; (Rossdorf,
DE) ; Gillen; Ralph; (Papenburg, DE) |
Correspondence
Address: |
Roche Diagnostics Corporation
9115 Hague Road
PO Box 50457
Indianapolis
IN
46250-0457
US
|
Family ID: |
34177889 |
Appl. No.: |
11/266637 |
Filed: |
November 3, 2005 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
|
|
10945798 |
Sep 21, 2004 |
|
|
|
11266637 |
Nov 3, 2005 |
|
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Current U.S.
Class: |
600/309 ;
600/365 |
Current CPC
Class: |
A61B 2560/0223 20130101;
A61B 5/14503 20130101; A61B 5/7239 20130101; A61B 5/14532 20130101;
A61B 5/7207 20130101 |
Class at
Publication: |
600/309 ;
600/365 |
International
Class: |
A61B 5/00 20060101
A61B005/00 |
Foreign Application Data
Date |
Code |
Application Number |
Sep 23, 2003 |
DE |
DE 10343863.7 |
Claims
1. A method for continuous monitoring concentration of an analyte
by determining the analyte's change over time in the living body of
a human or animal, the method comprising: measuring at sequential
points in time, measurement values of a measurement variable
correlating with a desired concentration of the analyte; measuring
the measurement variable as a measurement signal (z.sub.t);
determining the change over time of the concentration of the
analyte from the measurement signal as a useful signal (y.sub.t) by
means of a calibration; providing a filter algorithm in the time
domain for determination of the useful signal (y.sub.t) from the
measurement signal (z.sub.t), wherein the filter algorithm reduces
errors of the useful signal resulting from noise contained in the
measurement signal, wherein the filter algorithm includes an
operation in which the influence of an actual measurement value on
the useful signal is weighted by means of a weighting factor (V);
determining a signal variation parameter (.sigma..sub.t) on the
basis of signal variations detected in close chronological relation
to the measurement of the actual measurement value; and adapting
dynamically the weighting factor as a function of the signal
variation parameter determined for the point in time of the actual
measurement.
2. The method according to claim 1, wherein measurement values,
which are measured less than 30 minutes before the measurement of
the actual measurement value, are used in the determination of the
signal variations.
3. The method according to claim 1, wherein measurement values,
which are measured less than 15 minutes before the measurement of
the actual measurement value, are used in the determination of the
signal variations.
4. The method according to claim 1, wherein measurement values,
which are measured less than 5 minutes before the measurement of
the actual measurement value, are used in the determination of the
signal variations.
5. The method according to claim 1, wherein the filter algorithm is
a recursive filter algorithm.
6. The method according to claim 5, wherein the filter algorithm is
a Kalman filter algorithm.
7. The method according to claim 6, characterized in that the
filter algorithm is a linear Kalman filter algorithm.
8. The method according to claim 1, wherein the variables of a
system model upon which the filter algorithm is based comprise a
check variable.
9. The method according to claim 8, wherein the check variable is a
time derivative, preferably the first time derivative of the
analyte concentration.
10. A device for continuous monitoring of a concentration of an
analyte by determining the analyte's change over time in the living
body of a human or animal, the device comprising: a measurement
unit, by which measurement values of a measurement variable
correlating with the desired concentration are measured as the
measurement signal (z.sub.t) at sequential points in time; an
analysis unit, by which the change over time of the concentration
is determined by means of a calibration as a useful signal
(y.sub.t) from the measurement signal, and a filter algorithm in
the time domain for determination of the useful signal (y.sub.t)
from the measurement signal (z.sub.t) to reduce errors of the
useful signal, which result from noise contained in the measurement
signal; wherein the filter algorithm includes operations, in which
the influence of an actual measurement value on the useful signal
is weighted using a weighting factor (V), such that a signal
variation parameter (.sigma..sub.t) is determined on the basis of
signal variations detected in close chronological relationship with
the measurement of the actual measurement value, wherein the
weighting factor is dynamically adapted as a function of the signal
variation parameter determined for the point in time of the actual
measurement
Description
REFERENCE TO RELATED APPLICATIONS
[0001] The present application is a Continuation of U.S. patent
application Ser. No. 10/945,798, filed Sep. 21, 2004 which claims
priority to German Patent Application No. 10343863.7, filed Sep.
23, 2003, which are hereby incorporated by reference in their
entirety.
TECHNICAL FIELD
[0002] The present invention generally relates to a method and a
device for continuous monitoring of the concentration of an
analyte. In particular, the invention relates to determining the
analyte's change over time in the living body of a human or animal.
The term "continuous monitoring (CM)" is used hereafter for this
purpose.
BACKGROUND
[0003] A CM method and device is described, for example, in U.S.
Pat. No. 5,507,288.
[0004] A main task is the continuous monitoring of the
concentration of glucose in the body of the patient, which is of
great medicinal significance. Studies have led to the result that
extremely grave long-term effects of diabetes mellitus (for
example, blinding because of retinopathy) can be avoided if the
change over time of the concentration of the glucose is
continuously monitored in vivo. Continuous monitoring allows to
dose the required medication (insulin) precisely at each point in
time and to keep the blood sugar level always within narrow limits,
similarly to a healthy person.
[0005] The present invention relates in particular to CM of
glucose. Further information can be taken from document (1) and the
literature cited therein. The content of this document is
incorporated herein by reference.
[0006] The present invention is, however, also suitable for other
applications in which the change over time of an analyte in the
living body (useful signal) is derived from a measurement signal,
which comprises measurement values, measured at sequential points
in time, of a measurement variable correlating with the
concentration desired. The measurement signal may be measured
invasively or non-invasively.
[0007] An invasive measurement method is described, for example,
in
[0008] U.S. Pat. No. 6,584,335.
[0009] Here a hollow needle carrying a thin optical fiber is stuck
into the skin, light is irradiated under the skin surface through
the optical fiber, and a modification of the light through
interaction with interstitial liquid which surrounds the optical
fiber is measured. In this case, the measurement signal comprises
measurement values obtained from light which is returned through
the optical fiber into a measurement device after the interaction.
For example, the measurement signal may comprise spectra of the
light which are measured at sequential points in time.
[0010] Another example of invasive measurement methods is the
monitoring of concentrations by means of an electrochemical sensor
which may be stuck into the skin. An electrical measurement
variable, typically a current, is thus determined as the
measurement variable which is correlated with the concentration of
the analyte.
[0011] Different non-invasive methods are discussed in Document
(1). These include spectroscopic methods in which light is
irradiated directly (i.e., without injuring the skin) through the
skin surface into the body and diffusely reflected light is
analyzed. Methods of this type have achieved some importance for
checking the change over time of oxygen saturation in the blood.
For the analysis of glucose alternative methods are preferred, in
which light is irradiated into the skin in a strongly localized
manner (typically punctually) and the useful signal (course of the
glucose concentration) is obtained from the spatial distribution of
the secondary light coming out of the skin in the surroundings of
the irradiation point. In this case the measurement signal is
formed by the intensity profile, measured at sequential points in
time, of the secondary light in the surroundings of the irradiation
point.
[0012] A common feature of all methods of this type is that the
change of the concentration over time (useful signal) is determined
from the measurement values measured at sequential points in time
(measurement signal) using a microprocessor system and a suitable
algorithm. This analysis algorithm includes the following partial
algorithms: a filter algorithm, by which errors of the useful
signal resulting from signal noise contained in the measurement
signal are reduced and a conversion algorithm, in which a
functional relationship determined by calibration, which
relationship describes the correlation between measurement signal
and useful signal, is used.
[0013] Typically, these parts of the analysis algorithm are
performed in the described sequence, i.e., first a filtered
measurement signal is obtained from a raw measurement signal by
filtering and the filtered signal is then converted into the useful
signal. However, this sequence is not mandatory. The raw
measurement signal can also be first converted into a raw useful
signal and then filtered to obtain the final useful signal. The
analysis algorithm may also include further steps in which
intermediate variables are determined. It is only necessary in the
scope of the present invention that the two partial algorithms a)
and b) are performed as part of the analysis algorithm. The partial
algorithms a) and b) may be inserted anywhere into the analysis
algorithm and performed at any time.
[0014] The present invention relates to cases in which time domain
filter algorithms are used. Kalman filter algorithms are
particularly common for this purpose. More detailed information on
filter algorithms of this type is disclosed by the following
literature citations, some of which also describe chemical and
medical applications:
[0015] S. D. Brown: The Kalman filter in analytical chemistry,
Analytica Chimica Acta 181 (1986), 1-26.
[0016] K. Gordon: The multi-state Kalman filter in medical
monitoring, Computer Methods and Programs in Biomedicine 23 (1986),
147-154.
[0017] K. Gordon, A. F. M. Smith: Modeling and monitoring
biomedical time series, Journal of the American Statistical
Association 85 (1990), 328-337.
[0018] U.S. Pat. No. 5,921,937
[0019] EP 0 910 023 A2
[0020] WO 01/38948 A2
[0021] U.S. Pat. No. 6,317,662
[0022] U.S. Pat. No. 6,575,905 B2
[0023] As noted, the filter algorithm is used for the purpose of
removing noise signals which are contained in the raw measurement
signal and would corrupt the useful signal. The goal of every
filter algorithm is to eliminate this noise as completely as
possible, but simultaneously avoid to disturb the measurement
signal. This goal is especially difficult to achieve for in vivo
monitoring of analytes, because the measurement signals are
typically very weak and have strong noise components. Special
problems arise because the measurement signal typically contains
two types of noise, which differ significantly in regard to the
requirements for the filter algorithm: measurement noise: such
noise signal components follow a normal distribution having a
constant standard deviation around the correct (physiological)
measurement signal non-physiological signal changes, which are
caused, for example, by movements of the patient and changes of the
coupling of a measurement sensor to the skin to which it is
connected. They are typically neither distributed normally around
the physiological measurement signal, nor is the standard deviation
from the physiological measurement signal constant. For such noise
components of the raw signal the term NNNC (non-normal,
non-constant)-noise is used hereafter.
SUMMARY
[0024] The present invention is based on the technical problem to
achieve a better precision of CM methods by improving the filtering
of noise signals.
[0025] According to the present invention this is achieved by means
of a filter algorithm which includes an operation in which the
influence of an actual measurement value on the useful signal is
weighted using a weighting factor ("controllable filter
algorithm"), a signal variation parameter (related in each case to
the actual point in time, i.e. time-dependent) is determined on the
basis of signal variations detected during the continuous
monitoring in close chronological connection with the measurement
and the weighting factor is adapted dynamically as a function of
the signal variation parameter determined for the point in time of
the actual measurement.
[0026] The present invention, including preferred embodiments, will
be described in greater detail hereafter on the basis of the
figures. The details shown therein and described in the following
may be used individually or in combination to provide preferred
embodiments of the present invention.
BRIEF DESCRIPTION OF THE DRAWINGS
[0027] The following detailed description of the embodiments of the
present invention can be best understood when read in conjunction
with the following drawings, where like structure is indicated with
like reference numerals and in which:
[0028] FIG. 1 shows a block diagram of a device according to the
present invention;
[0029] FIG. 2 shows a schematic diagram of a sensor suitable for
the present invention;
[0030] FIG. 3 shows a measurement signal of a sensor as shown in
FIG. 2;
[0031] FIG. 4 shows a symbolic flowchart to explain the algorithm
used in the scope of the present invention;
[0032] FIG. 5 shows a graphic illustration of typical signal curves
to explain the problem solved by the present invention;
[0033] FIG. 6 shows a graphic illustration of experimentally
obtained measurement results.
[0034] Skilled artisans appreciate that elements in the figures are
illustrated for simplicity and clarity and have not necessarily
been drawn to scale. For example, the dimensions of some of the
elements in the figures may be exaggerated relative to other
elements to help improve understanding of the embodiment(s) of the
present invention.
DETAILED DESCRIPTION
[0035] The following description of the preferred embodiment is
merely exemplary in nature and is in no way intended to limit the
invention or its application or uses.
[0036] The components of a CM device according to the present
invention are shown in FIG. 1. As shown, a sensor 1 measures
measurement values at sequential points in time. This measurement
signal is transmitted--wirelessly, in the case shown--to a receiver
2, from which the measurement signal is further transmitted to an
analysis unit 3, which contains a microprocessor 4 and a data
memory 5. Data and commands may also be transmitted to the analysis
unit 3 via an input unit 6. Results are output using an output unit
7, which may include a display and other typical output means. The
data processing is performed digitally in the analysis unit 3 and
corresponding converters for converting analog signals into digital
signals are provided. The present invention is suitable for a wide
range of measurement techniques in which--as explained at the
beginning--different measurement signals correlating to the desired
useful signal are obtained.
[0037] FIG. 2 shows a sensor 1 in the form of a schematic diagram,
in which an implantable catheter 10 is used in order to suction
interstitial liquid from the subcutaneous fatty tissue by means of
a pump 11. The tissue is then suctioned through a photometric
measurement unit 12 into a waste container 13. The line 14 by which
the interstitial liquid is transported contains a transparent
measurement cell 15 which is arranged in the photometric
measurement unit 12, into which primary light originating from a
light emitter 16 is irradiated. The secondary light resulting after
passing the measurement cell 15 is measured using a photodetector
17 and processed by means of a measurement electronics (not shown)
into a raw signal, which--as shown for exemplary purposes in FIG.
1--is transmitted to an analysis unit 3.
[0038] FIG. 3 shows the typical graph of a raw measurement signal
as curve A obtained using a sensor as shown in FIG. 2. The
intensity I of the secondary light is measured at a specific
wavelength and plotted against the time t in minutes. FIG. 3 is
based on a CM experiment in which the measurement values for curve
A were measured at intervals of one second each.
[0039] Variations of the flow of the interstitial liquid from the
body into the photometric measurement unit 12 lead to regular,
relatively small signal variations, which are referred to as
"fluidic modulation". After approximately three minutes, at the
point in time identified with the arrow 18, an inhibition of the
liquid flow occurred, which may be caused, for example, by movement
of the patient or by the entrance of a cell particle into the
catheter 10. This inhibition of the flow leads to a large drop of
the raw measurement signal A. This is an example of the fact that
not all noise signals are distributed normally, with essentially
constant standard deviation, around the signal corresponding to the
actual physiological measurement value. Rather also interfering
contributions of the type shown here exist, for which these
conditions do not apply (NNNC noise). Therefore, the signal
requires filtering even in such cases in such a manner that a
useful signal results which corresponds as closely as possible to
the actual physiological concentration of the analyte. An example
for such a useful signal is shown in FIG. 3 as thin line B.
[0040] The basis of a filter algorithm operating in the time
domain, which the present invention relates to, is a system model
that describes the change over time of the variables of interest
and their relationship to one another. The functional relationship
which describes the development of the system from time t to time
t+1 is as follows: y.sub.t+1=f.sub.t(y.sub.t,y.sub.t-1, . . .
,u.sub.t,u.sub.t-1, . . . ).
[0041] Therein, y.sub.t and u.sub.t are vectors, which are referred
to as state vectors and vectors of input variables, respectively.
The state vector y.sub.t contains the variables of physiological
interest and optionally check variables, which allow to check the
measurement, as will be described in greater detail below. In the
CM method, these include the desired analyte concentration, for
example, the glucose concentration g.sub.t in the blood. The speed
of change of the analyte concentration g.sub.t'=dg.sub.t/dt is
suitable as a check variable. The state variable y.sub.t may also
contain model variables related to the measurement method. For
example, in the case of a measurement result of the type shown in
FIG. 3, it is advantageous to incorporate fluidic modulations into
the system model. These modulations may be described using their
time-dependent frequency .omega..sub.t and the amplitude A.sub.t,
which is also time-dependent. Therefore, four system variables
result for the experiment described on the basis of FIGS. 2 and 3:
g.sub.t, A.sub.t, .omega..sub.t, g.sub.t'.
[0042] Input variables which, in the field of automatic control,
correspond to control variables and are therefore not measured
themselves are entered into the vector u.sub.t. In the case of
glucose monitoring, for example, the administered insulin quantity
given and the bread exchange units supplied are suitable input
variables, because they both influence the glucose concentration in
the blood. If these input variables are used, the vector u.sub.t
has two elements: insulin dose and bread exchange units. A
characteristic feature of input variables is that no prediction of
their future values is necessary in the scope of the filter
algorithm.
[0043] The mentioned variables of the state vector y.sub.t and the
input vector u.sub.t are, of course, only to be understood as
examples. The present invention relates to greatly varying systems
which require different system models. It is not necessary to use
the models in a discrete form. The continuous form with the
corresponding differential equations may also be used.
[0044] A feature of filter algorithms in the time domain, is that
they include an alternating sequence of predictions and
corrections. A prediction of the system state ("predictor step") is
followed by a subsequent correction of this prediction on the basis
of a further measurement value ("corrector step").
[0045] In a predictor step, the actual value of the state variable
y.sub.t at the point in time t is predicted using the system
equation (1): y.sub.t=f.sub.t-1(y.sub.t-1,y.sub.t-2, . . .
;u.sub.t-1,u.sub.t-2, . . . )+W.sub.t-1
[0046] In this equation, y.sub.t identifies the value of the state
vector at the point in time t which is estimated (predicted) using
the data of the previous point in time (t-1); W.sub.t identifies a
system error vector.
[0047] In the case of a recursive filter algorithm, the calculation
of each predictor step is not performed by taking all preceding
points in time (t-1, t-2, t-3 . . . ) into consideration, but
rather by using a weighted sum of smoothed signal values. In the
example of a linear Kalman algorithm, the corresponding equation
may be written as follows:
y.sub.t=A.sub.t-1y.sub.t-1+Bu.sub.t-1+w.sub.t-1 (2a)
[0048] In this equation, A.sub.t is the system matrix and B is the
input matrix. In the general (non-linear) case, f.sub.t is to be
preset or is to be calculated from data determined up to this
point.
[0049] In the corrector step, the prediction is corrected on the
basis of an actual measurement value according to
y.sub.t=.alpha..sub.ty.sub.t+.beta..sub.t.DELTA..sub.t
[0050] In this equation, .DELTA..sub.t is a variable which
represents a measure of the deviation of an actual measurement
value z.sub.t from the predicted value and is referred to as the
"innovation". .DELTA..sub.t=z.sub.t-h(y.sub.t)
[0051] Further it is taken into consideration that typically the
system variables cannot be observed directly. The linkage between
the measurement values and the state variables is provided by means
of a measurement model (measurement function h.sub.t) according to:
z.sub.t=h.sub.t(y.sub.t)+v.sub.t
[0052] The noise of the measurement values is taken into
consideration by v.sub.t.
[0053] In the case of a linear Kalman algorithm (cf. equation 2a),
the measurement equation is z.sub.t=H.sub.ty.sub.t+V.sub.t,
(5a)
[0054] H.sub.t referring to the measurement matrix.
[0055] For example, in the continuous monitoring of glucose using
an electrochemical sensor, a current i is measured which is
correlated with the glucose concentration g.sub.t. In that example
h.sub.t describes the correlation of the state variable g.sub.t
with the measurement variable i (current), which is an element of
the vector z.sub.t.
[0056] In the given example of photometric glucose detection using
filter-assisted compensation of the fluidic modulation, a
non-linear measurement model is used which links the photometric
measurement signal z.sub.t to the system variables of glucose
concentration g.sub.t, amplitude A.sub.t, and frequency
.omega..sub.t of the fluidic modulation:
z.sub.t=g.sub.t+A.sub.tsin(.omega..sub.tt).
[0057] According to equation (3), the influence of the actual
measurement value (contained in the innovation .DELTA..sub.t) on
the filtered useful signal value y.sub.t is weighted by the factors
.alpha..sub.t and .beta..sub.t. The described algorithm is
therefore a controllable filter algorithm.
[0058] In the case of a Kalman filter, .alpha..sub.t=1 for every
point in time and .beta..sub.t=K.sub.t. K.sub.t refers to the
Kalman gain. Accordingly, the corrector equation is as follows:
y.sub.t=y.sub.t+K.sub.t.DELTA..sub.t (3a)
[0059] Further details regarding the Kalman gain K.sub.t and more
detailed information on the algorithm may be taken from the
relevant literature, as cited above. Expressed descriptively, the
Kalman gain is a measure of the weight given to additional
measurement values. The Kalman gain is calculated anew in every
iteration step of the filter algorithm according to an equation
which may be written in simplified form (for the linear case) as
follows: K.sub.t=P.sub.tH.sub.t(P.sub.tH.sub.t+V).sup.-1
[0060] Here, P.sub.t designates the Kalman error covariance matrix.
V designates the measurement error covariance matrix in the
conventional Kalman algorithm.
[0061] Equation (6) shows that the elements of K.sub.t may assume
only values between 0 and 1. If the assumed measurement error V is
relatively large in relation to the Kalman error covariance
P.sub.t, K.sub.t is small, i.e., the particular actual measurement
value is given relatively little weight. In contrast, if V is small
in relation to P.sub.t (multiplied by H.sub.t), a strong correction
occurs due to the actual measurement value.
[0062] FIG. 4 shows in graphic form the iteration loop 20 which is
the basis of the filter procedure. Alternately a corrector step
which takes an actual measurement value z.sub.t into consideration,
and, after a time step dt, a predictor step for a new point in time
are performed. For example, the corrector step may be calculated
according to equation (3) or (3a) and the predictor step according
to equation (2) or (2a). This part of the algorithm is referred to
as the filter core 22. As explained, it may be implemented in
different ways, as long as it is an algorithm operating in the time
domain and it includes an operation in which the influence of an
actual measurement value z.sub.t on the filter useful signal
y.sub.t is weighted using a weighting factor .alpha..sub.t,
.beta..sub.t, or K.sub.t, respectively.
[0063] An important improvement of the filtering is achieved in the
scope of the present invention in that, on the basis of signal
variations detected in close chronological relationship with the
measurement of the actual measurement value z.sub.t, a signal
variation parameter, designated here as .sigma..sub.t, is
determined and the weighting of the influence of the actual
measurement value z.sub.t is dynamically adapted in the context of
the corrector step as a function of .sigma..sub.t. This is shown in
graphic form in FIG. 4: box 23 symbolizes the calculation of the
variation parameter .sigma..sub.t as a function of the measurement
signal in a preceding period of time (measurement values z.sub.t-n
. . . z.sub.t). Box 24 symbolizes the calculation of the weighting
factor taken into consideration in the corrector step (here, for
example, the measurement error covariance V, which influences the
Kalman gain), as a function of the signal variation parameter
.sigma..sub.t. The weighting factor is a time-dependent
(dynamically adapted) variable (in this case V.sub.t).
[0064] The present invention does not have the goal of weighting
different filter types--like a filter bank--by applying weighting
factors. For this purpose, a series of system models analogous to
equation (2) would have to be defined, one model for each filter of
the filter bank. This is not necessary in the present invention,
whereby the method is less complex.
[0065] No precise mathematical rules may be specified for the
functional relationships used in steps 23 and 24, because they must
be tailored to each individual case. However, the following general
rules apply: [0066] The signal variation parameter is determined as
a function of measurement values which have a close chronological
relationship to the particular actual measurement value. In this
way, the speed of adaption of the filter is sufficient. The
determination of the signal variation parameter is preferably based
on measurement values which were measured less than 30 minutes,
preferably less than 15 minutes, and especially preferably less
than 5 minutes before the measurement of the actual measurement
value. At the least, measurement values from the periods of time
should be included in the algorithm for determining the signal
variation parameter. [0067] Independently of the equations used in
a particular case, the principle applies that with decreasing
signal quality (i.e., for example, increase of the standard
deviation of the measurement signal), the signal variation
parameter and therefore the weighting factor (or possibly the
weighting factors) are changed in such a direction that the
influence of the currently actual measurement value is reduced.
[0068] The standard deviation, which may be calculated as follows,
is suitable as the signal variation parameter, for example.
[0069] If one assumes that the determination of the standard
deviation is based on the actual measurement values z and four
preceding measurement values z.sub.1 to z.sub.4, and if the
difference between z and the preceding values is referred to as
.delta.z (.delta.z.sub.n=z-z.sub.n), the average value .epsilon. is
calculated as = 1 4 .times. ( .delta. .times. .times. z 1 + .delta.
.times. .times. z 2 + .delta. .times. .times. z 3 + .delta. .times.
.times. z 4 ) ( 7 ) ##EQU1## [0070] and the slope .phi. of a linear
smoothing function is calculated as .phi. = 3 .times. ( .delta.
.times. .times. z 1 - .delta. .times. .times. z 4 ) + .delta.
.times. .times. z 2 - .delta. .times. .times. z 3 10 ##EQU2##
[0071] The standard deviation of the four values of the difference
.delta.1, .delta.2, .delta.3, .delta.4 in relation to the linear
smoothing function is .sigma. t = .times. [ 1 3 .times. ( .delta.
.times. .times. z 1 - ( + 1.5 .times. .phi. ) ) 2 + 1 3 .times. (
.delta. .times. .times. z 2 - ( + 0.5 .times. .phi. ) ) 2 + .times.
1 3 .times. ( .delta. .times. .times. z 3 - ( - 0.5 .times. .phi. )
) 2 + 1 3 .times. ( .delta. .times. .times. z 4 - ( - 1.5 .times.
.phi. ) ) 2 ] 1 2 ( 9 ) ##EQU3##
[0072] On the basis of this standard deviation at, a dynamic
(time-dependent) measurement error covariance V.sub.t, which is
included in a filter core with the Kalman algorithm, may be
calculated, for example, according to
V.sub.t=(.sigma..sub.o+.sigma..sub.t).sup..gamma.
[0073] In this case, .sigma..sub.o and .gamma. are constant
parameters which characterize the filter, and which may be set to
tailor the chronological behavior of the filter, in particular its
adaptivity, to a particular application.
[0074] In the example of a controllable recursive filter, the
weighting factors .alpha..sub.t, .beta..sub.t from equation (3) are
a function of the signal variation parameter in such a manner that
with increasing .sigma..sub.t, factor .alpha..sub.t becomes larger
and factor .beta..sub.t becomes smaller.
[0075] As already explained, equations (7) through (10) only
represent one of numerous possibilities for calculating a signal
variation parameter and, based thereon, a weighting factor for a
controllable filter algorithm in the time domain. The standard
deviation, which may, of course, be calculated using a varying
number of measurement values, can be replaced by variables which
represent a measure for the signal variations in a period of time
preceding an actual measurement value. The term "signal variation
parameter" is used generally to identify a mathematical variable
which fulfills these requirements.
[0076] Three typical graphs of a signal S are plotted against time
t in FIG. 5, specifically: [0077] as a solid line, a raw signal
with strong non-physiological variations in the time period
enclosed by circle 25 and oscillates significantly less in the time
period enclosed by rectangle 26, these variations being essentially
physiological. [0078] as a dashed line, a useful signal, which was
obtained from the raw signal a) using a Kalman filter, whose
measurement error covariance was set corresponding to the variation
of the raw signal in the circle 25. [0079] as a dotted line, a
useful signal which was obtained from the raw signal a) using a
Kalman filter, whose measurement error covariance was set
corresponding to the graph of the raw signal in the rectangle
26.
[0080] Evidently, in the case of curve b the strong variations are
filtered well within the circle 25, but in the rectangle 26, the
signal b reflects the physiological variations of the raw signal
insufficiently. The useful signal c, in contrast, follows the
physiological variations in the region 26 well, while the filtering
of the non-physiological variations in the region 25 is
insufficient. The conventional Kalman filter algorithm therefore
allows no setting which leads to optimal filtering for the
different conditions shown. In contrast, the present invention does
not even require knowledge of the maximum variations of measurement
values. The filter algorithm adapts itself automatically to the
changes in the signal course and provides a filtered signal which
corresponds to the curve b in the circle 25 and to the curve c in
the rectangle 26.
[0081] FIG. 6 shows corresponding experimental results from a CM
experiment for glucose monitoring. A useful signal resulting from
conventional filtering is shown as the solid curve A (glucose
concentration in mg/dl) over the time in hours. The dashed curve B
is the useful signal filtered according to the present invention.
At the point in time marked with the arrow 28, the patient begins
to move which interferes with the signal curve. Although there is
very little variation of the free analyte concentration, the noise
caused by the movement (NNNC noise) cannot be filtered out by the
conventional filter. In contrast, using the filtering according to
the present invention, a useful signal is obtained which
approximates the physiological glucose curve very closely.
[0082] Significant additional reliability may be achieved if the
filtering extends not only to the desired analyte concentration,
but rather additionally to at least one further variable, which is
designated "check variable". This may be a variable derived from
the analyte concentration, in particular its first, second, or
higher derivative versus time. Alternatively, an additional
measurement variable, such as the flow of the interstitial liquid
at the sensor shown in FIG. 2, can be used.
[0083] This check variable may, as explained above (for g.sub.t',
A.sub.t, and .omega..sub.t), be included in the filter algorithm as
a system variable. The filtering then also extends to the check
variable, for which corresponding reliable smoothed useful signal
values are available as the result of the filtering. These may then
be compared to threshold values, in order to perform plausibility
checks, for example. In the case of the glucose concentration, for
example, it is known that the glucose concentration physiologically
does not change by more than 3 mg/dl/min under normal conditions. A
higher filtered value of the time derivative g.sub.t' is a sign of
a malfunction. Therefore the query 30 shown in FIG. 4 compares the
value of y.sub.t' to a minimum value and a maximum value. The value
y.sub.t is only accepted as correct if y.sub.t' lies within these
limits. Such a comparison would not be possible using the useful
signal A in FIG. 6, because the insufficiently filtered
non-physiological variations would lead to false alarms.
[0084] In order that the invention may be more readily understood,
reference is made to the following examples, which are intended to
illustrate the invention, but not limit the scope thereof.
[0085] It is noted that terms like "preferably", "commonly", and
"typically" are not utiliized herein to limit the cope of the
claimed invention or to imply that certain features are critical,
essential, or even important to the structure or function of the
claimed invention. Rather, these terms are merely intended to
highlight alternative or additional features that may or may not be
utilized in a particular embodiment of the present invention.
[0086] For the purposes of describing and defining the present
invention it is noted that the term "substantially" is utilized
herein to represent the inherent degree of incertainty that may be
attributed to any quantitative comparison, value, measurement, or
other representation. The term "substantially" is also utilized
herein to represent the degree by which a quantitative
representation may vary from a stated reference without resulting
in a change in the basic function of the subject matter at
issue.
[0087] Having described the invention in detail and by reference to
specific embodiments thereof, it will be apparent that
modifications and variations are possible without departing from
the scope of the invention defined in the appended claims. More
specifically, although some aspects of the present invention are
identified herein as preferred or particularly advantageous, it is
contemplated that the present invention is not necessarily limited
to these preferred aspects of the invention.
[0088] As any person skilled in the art will recognize from the
previous description and from the figures and claims, modifications
and changes can be made to the preferred embodiment of the
invention without departing from the scope of the invention as
defined in the following claims.
* * * * *