US20050272985A1 - Method and device for continuous monitoring of the concentration of an analyte - Google Patents
Method and device for continuous monitoring of the concentration of an analyte Download PDFInfo
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- US20050272985A1 US20050272985A1 US10/945,798 US94579804A US2005272985A1 US 20050272985 A1 US20050272985 A1 US 20050272985A1 US 94579804 A US94579804 A US 94579804A US 2005272985 A1 US2005272985 A1 US 2005272985A1
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/145—Measuring characteristics of blood in vivo, e.g. gas concentration, pH value; Measuring characteristics of body fluids or tissues, e.g. interstitial fluid, cerebral tissue
- A61B5/14503—Measuring characteristics of blood in vivo, e.g. gas concentration, pH value; Measuring characteristics of body fluids or tissues, e.g. interstitial fluid, cerebral tissue invasive, e.g. introduced into the body by a catheter or needle or using implanted sensors
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/145—Measuring characteristics of blood in vivo, e.g. gas concentration, pH value; Measuring characteristics of body fluids or tissues, e.g. interstitial fluid, cerebral tissue
- A61B5/14532—Measuring characteristics of blood in vivo, e.g. gas concentration, pH value; Measuring characteristics of body fluids or tissues, e.g. interstitial fluid, cerebral tissue for measuring glucose, e.g. by tissue impedance measurement
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B2560/00—Constructional details of operational features of apparatus; Accessories for medical measuring apparatus
- A61B2560/02—Operational features
- A61B2560/0223—Operational features of calibration, e.g. protocols for calibrating sensors
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
- A61B5/7203—Signal processing specially adapted for physiological signals or for diagnostic purposes for noise prevention, reduction or removal
- A61B5/7207—Signal processing specially adapted for physiological signals or for diagnostic purposes for noise prevention, reduction or removal of noise induced by motion artifacts
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
- A61B5/7235—Details of waveform analysis
- A61B5/7239—Details of waveform analysis using differentiation including higher order derivatives
Definitions
- the present invention generally relates to a method and a device for continuous monitoring of the concentration of an analyte.
- the invention relates to determining the analyte's change over time in the living body of a human or animal.
- continuous monitoring (CM) is used hereafter for this purpose.
- 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.
- 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.
- 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.
- the measurement signal comprises measurement values obtained from light which is returned through the optical fiber into a measurement device after the interaction.
- the measurement signal may comprise spectra of the light which are measured at sequential points in time.
- 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.
- Non-invasive methods 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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:
- 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.
- the present invention is based on the technical problem to achieve a better precision of CM methods by improving the filtering of noise signals.
- 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.
- FIG. 1 shows a block diagram of a device according to the present invention
- FIG. 2 shows a schematic diagram of a sensor suitable for the present invention
- FIG. 3 shows a measurement signal of a sensor as shown in FIG. 2 ;
- FIG. 4 shows a symbolic flowchart to explain the algorithm used in the scope of the present invention
- FIG. 5 shows a graphic illustration of typical signal curves to explain the problem solved by the present invention
- FIG. 6 shows a graphic illustration of experimentally obtained measurement results.
- FIG. 1 The components of a CM device according to the present invention are shown in FIG. 1 .
- 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.
- 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 .
- 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.
- 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”.
- 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.
- An example for such a useful signal is shown in FIG. 3 as thin line B.
- 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.
- y t and u t are vectors, which are referred to as state vectors and vectors of input variables, respectively.
- the state variable y 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.
- modulations may be described using their time-dependent frequency ⁇ t and the amplitude A t , which is also time-dependent. Therefore, four system variables result for the experiment described on the basis of FIGS. 2 and 3 : g t ,A t , ⁇ t ,g t ′.
- 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 t .
- 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 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.
- the mentioned variables of the state vector y t and the input vector u 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.
- 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”).
- ⁇ t f t ⁇ 1 ( y t ⁇ 1 ,y t ⁇ 2 , . . . ;u t ⁇ 1 ,u t ⁇ 2 , . . . )+W t ⁇ 1
- ⁇ 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 t identifies a system error vector.
- 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.
- ⁇ t A t ⁇ 1 y t ⁇ 1 +Bu t ⁇ 1 +w t ⁇ 1 (2a)
- a t is the system matrix and B is the input matrix.
- f t is to be preset or is to be calculated from data determined up to this point.
- ⁇ t is a variable which represents a measure of the deviation of an actual measurement value z t from the predicted value and is referred to as the “innovation”.
- ⁇ t z t ⁇ h ( ⁇ t )
- the noise of the measurement values is taken into consideration by v t .
- a current i is measured which is correlated with the glucose concentration g t .
- h t describes the correlation of the state variable g t with the measurement variable i (current), which is an element of the vector z t .
- the influence of the actual measurement value (contained in the innovation t ) on the filtered useful signal value y t is weighted by the factors ⁇ t and ⁇ t .
- the described algorithm is therefore a controllable filter algorithm.
- the Kalman gain is a measure of the weight given to additional measurement values.
- P t designates the Kalman error covariance matrix.
- V designates the measurement error covariance matrix in the conventional Kalman algorithm.
- Equation (6) shows that the elements of K 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 t , K 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 t (multiplied by H t ), a strong correction occurs due to the actual measurement value.
- FIG. 4 shows in graphic form the iteration loop 20 which is the basis of the filter procedure.
- a corrector step which takes an actual measurement value z t into consideration, and, after a time step dt, a predictor step for a new point in time are performed.
- 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 .
- a signal variation parameter designated here as at
- the weighting of the influence of the actual measurement value z t is dynamically adapted in the context of the corrector step as a function of ⁇ t .
- box 23 symbolizes the calculation of the variation parameter at as a function of the measurement signal in a preceding period of time (measurement values z t ⁇ n . . . z 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 ⁇ t .
- the weighting factor is a time-dependent (dynamically adapted) variable (in this case V t ).
- the present invention does not have the goal of weighting different filter types—like a filter bank—by applying weighting factors.
- 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.
- the standard deviation which may be calculated as follows, is suitable as the signal variation parameter, for example.
- ⁇ 3 ⁇ ( ⁇ ⁇ ⁇ z 1 - ⁇ ⁇ ⁇ z 4 ) + ⁇ ⁇ ⁇ z 2 - ⁇ ⁇ ⁇ z 3 10
- ⁇ t [ 1 3 ⁇ ( ⁇ ⁇ ⁇ z 1 - ( ⁇ + 1.5 ⁇ ⁇ ) ) 2 + 1 3 ⁇ ( ⁇ ⁇ ⁇ z 2 - ( ⁇ + 0.5 ⁇ ⁇ ) ) 2 + 1 3 ⁇ ( ⁇ ⁇ ⁇ z 3 - ( ⁇ - 0.5 ⁇ ⁇ ) ) 2 + 1 3 ⁇ ( ⁇ ⁇ ⁇ z 4 - ( ⁇ - 1.5 ⁇ ) ) 2 ] 1 / 2 ( 9 )
- V t ( ⁇ o + ⁇ t ) ⁇
- ⁇ o and ⁇ 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.
- the weighting factors ⁇ t , ⁇ t from equation (3) are a function of the signal variation parameter in such a manner that with increasing ⁇ t , factor ⁇ t becomes larger and factor ⁇ ⁇ t becomes smaller.
- 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.
- 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 .
- 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.
- the patient begins to move which interferes with the signal curve.
- the noise caused by the movement cannot be filtered out by the conventional filter.
- using the filtering according to the present invention a useful signal is obtained which approximates the physiological glucose curve very closely.
- the filtering extends not only to the desired analyte concentration, but rather additionally to at least one further variable, which is designated “check variable”.
- check variable This may be a variable derived from the analyte concentration, in particular its first, second, or higher derivative versus time.
- an additional measurement variable such as the flow of the interstitial liquid at the sensor shown in FIG. 2 , can be used.
- This check variable may, as explained above (for g t ′, A t , and ⁇ 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.
- threshold values in order to perform plausibility checks, for example.
- the query 30 shown in FIG. 4 compares the value of y t ′ to a minimum value and a maximum value. The value y t is only accepted as correct if y 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.
- 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.
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US11/266,637 US20060052679A1 (en) | 2003-09-23 | 2005-11-03 | Method and device for continuous monitoring of the concentration of an analyte |
US11/870,606 US7389133B1 (en) | 2003-09-23 | 2007-10-11 | Method and device for continuous monitoring of the concentration of an analyte |
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DE10343863A DE10343863A1 (de) | 2003-09-23 | 2003-09-23 | Verfahren und Gerät zur laufenden Überwachung der Konzentration eines Analyten |
DEDE10343863.7 | 2003-09-23 |
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US11/266,637 Continuation US20060052679A1 (en) | 2003-09-23 | 2005-11-03 | Method and device for continuous monitoring of the concentration of an analyte |
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US10/945,798 Abandoned US20050272985A1 (en) | 2003-09-23 | 2004-09-21 | Method and device for continuous monitoring of the concentration of an analyte |
US11/266,637 Abandoned US20060052679A1 (en) | 2003-09-23 | 2005-11-03 | Method and device for continuous monitoring of the concentration of an analyte |
US11/870,606 Active US7389133B1 (en) | 2003-09-23 | 2007-10-11 | Method and device for continuous monitoring of the concentration of an analyte |
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US11/870,606 Active US7389133B1 (en) | 2003-09-23 | 2007-10-11 | Method and device for continuous monitoring of the concentration of an analyte |
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JP (1) | JP4040614B2 (de) |
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DE502004010963D1 (de) | 2010-05-12 |
DE10343863A1 (de) | 2005-04-14 |
JP2005131370A (ja) | 2005-05-26 |
JP4040614B2 (ja) | 2008-01-30 |
US20080139902A1 (en) | 2008-06-12 |
ATE462350T1 (de) | 2010-04-15 |
US20060052679A1 (en) | 2006-03-09 |
CA2481627C (en) | 2009-12-29 |
EP1518495A1 (de) | 2005-03-30 |
CA2481627A1 (en) | 2005-03-23 |
US7389133B1 (en) | 2008-06-17 |
EP1518495B1 (de) | 2010-03-31 |
ES2340383T3 (es) | 2010-06-02 |
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