COMBINATION NON-INVASIVE AND INVASIVE BIOPARAMETER MEASURING DEVICE

• US 20120191001A1
• Filed: 01/23/2011
• Published: 07/26/2012
• Est. Priority Date: 01/23/2011
• Status: Active Grant

• Current Assignee: Cnoga Ltd.
• Original Assignee: Cnoga Ltd.
• Inventors: Segman, Yosef
• Granted Patent: US 8,948,833 B2
• Time in Patent Office: Days
• US Class Current: 600/547
• CPC Class Codes: A61B 10/00 Other methods or instruments for diagnosis, e.g. instruments for taking a cell sample, for biopsy, for vaccination diagnosis vaccination prophylaxis, vaccination therapy A61B17/20; Sex determination; Ovulation-period determination menstruation tables G06C3/00; Throat striking implements
• CPC Class Codes: A61B 2010/0083 for taking gas samples
• CPC Class Codes: A61B 5/0022 Monitoring a patient using a global network, e.g. telephone networks, internet
• CPC Class Codes: A61B 5/01 Measuring temperature of body parts ; Diagnostic temperature sensing, e.g. for malignant or inflamed tissue clinical contact thermometers G01K13/20
• CPC Class Codes: A61B 5/053 Measuring electrical impedance or conductance of a portion of the body
• CPC Class Codes: A61B 5/14532 for measuring glucose, e.g. by tissue impedance measurement
• CPC Class Codes: A61B 5/1455 using optical sensors, e.g. spectral photometrical oximeters
• CPC Class Codes: A61B 5/1459 invasive, e.g. introduced into the body by a catheter A61B5/1464 takes precedence
• CPC Class Codes: A61B 5/1495 Calibrating or testing of in-vivo probes
• CPC Class Codes: A61B 5/486 Bio-feedback A61B5/375 takes precedence
• CPC Class Codes: A61B 5/7225 Details of analog processing, e.g. isolation amplifier, gain or sensitivity adjustment, filtering, baseline or drift compensation A61B5/30, A61B5/316 take precedence; transformers for specific applications H01F38/00; amplifiers H03F
• CPC Class Codes: A61B 5/7246 using correlation, e.g. template matching or determination of similarity
• CPC Class Codes: A61B 5/7267 involving training the classification device
• CPC Class Codes: A61B 5/7278 Artificial waveform generation or derivation, e.g. synthesising signals from measured signals A61B5/327 takes precedence
• CPC Class Codes: A61B 5/742 using visual displays A61B5/339 takes precedence

Abstract

In a combination invasive and non-invasive bioparameter monitoring device an invasive component measures the bioparameter and transmits the reading to the non-invasive component. The non-invasive component generates a bioparametric reading upon insertion by the patient of a body part. A digital processor processes a series over time of digital color images of the body part and represents the digital images as a signal over time that is converted to a learning vector using mathematical functions. A learning matrix is created. A coefficient of learning vector is deduced. From a new vector from non-invasive measurements, a new matrix of same size and structure is created. Using the coefficient of learning vector, a recognition matrix may be tested to measure the bioparameter non-invasively. The learning matrix may be expanded and kept regular. After a device is calibrated to the individual patient, universal calibration can be generated from sending data over the Internet.

Claims

1. A method of monitoring a bioparameter, comprising:

(a) invasively measuring the bioparameter of a patient using an invasive component of a bioparameter monitoring device and transmitting an invasive bioparameter reading to a non-invasive component of the bioparameter monitoring device, the invasive bioparameter reading to be entered in a column vector, Y;

(b) within a proximity time of step “

(a)”

, non-invasively measuring the bioparameter of the patient by using one or more color image sensors in the non-invasive component of the device to generate a series of color images of tissue of a body part of the patient and to sense a magnitude of each of three colors at pixels of each color image and by converting the magnitudes into a series of electric signals, to produce a signal over time reflecting a distribution of each of the three colors in the color images over time;

(c) a digital processor of the non-invasive component (i) using a mathematical function to convert the signal to a scalar learning number and (ii) repeating step “

(c)(i)”

, without necessarily using the same mathematical function, to form a learning vector that corresponds to a scalar invasive bioparameter reading entry of column vector Y;

(d) from a plurality of learning vectors, a digital processor forming an n by n learning matrix, D, that is a regular matrix, by repeating steps “

(a)”

through “

(c)”

enough times that a digital processor has sufficient correlations between non-invasive bioparametric readings and invasive bioparametric readings to be able to measure the bioparameter using a non-invasive bioparameter reading at a pre-defined level of threshold acceptability;

(e) a digital processor obtaining a coefficient of learning vector, C, by multiplying an inverse matrix D<sup>−

1</sup> of learning matrix, D by the column vector Y;

(f) a digital processor obtaining a new vector, V^new by (i) non-invasively measuring the bioparameter of the patient by using the one or more color image sensors in the non-invasive component of the device to generate a series of color images of tissue of a body part of the patient and to sense a magnitude of each of the three colors at pixels of each color image and by converting the magnitudes into a series of electric signals, to produce a signal over time reflecting a distribution of each of the three colors in the color images over time and by having a digital processor use a mathematical function to convert the signal to a scalar number and by (ii) repeating step “

(f)(i) n times to form V^new, without necessarily using the same mathematical functions;

(g) using the entries of V^new to form a regular matrix, D^new, of n by n size and whose structure of non-zero elements is identical to a structure of non-zero elements of learning matrix, D; and

(h) using a digital processor to perform a matrix vector multiplication of D^new by coefficient of learning vector, C, to create a column vector of non-invasive bioparameter measurement, R, and comparing entries of R with entries of Y to find one entry of R which represents a calibrated bioparameter value for the patient.

2. The method of claim 1, further comprising creating an expanded learning matrix D_exp of (n+1) by (n+1) size by (i) incorporating V_new into learning matrix D and by having a digital processor use a mathematical function to convert the signal of step “

(f)”

to a (n+1)th scalar number and adding the (n+1)th scalar number alongside V^new to maintain expanded learning matrix D_exp as a regular matrix and by (ii) testing an accuracy of the calibrated bioparameter value by taking a further invasive bioparameter measurement as in step “

(a)”

to expand column vector Y to (n+1) elements and transmitting the further invasive bioparameter measurement to the non-invasive component.

3. The method of claim 2, further comprising continuing to expand learning matrix D_exp to a size of (n+m) by (n+m) where m is greater than 1.

4. The method of claim 2, further comprising further testing an ability of the non-invasive component to measure the bioparameter by obtaining a new coefficient of learning vector, C^new by multiplying an inverse matrix D_exp⁻

1 of matrix, D_exp by the expanded column vector, Y, and by repeating steps “

(f)”

, “

(g)” and

“

(h)”

except substituting (n+1) for n in steps “

(f)”

, “

(g)” and

“

(h)”

to obtain an improved calibrated bioparameter value for the patient.

5. The method of claim 1, wherein the structure of the nonzero entries of learning matrix, D, is triangular such that a magnitude of entries in each succeeding row increases by an integer.

6. The method of claim 5, wherein the integer is one and wherein a first row of learning matrix, D, has one entry.

7. The method of claim 1, further comprising comparing entries of column vector R with entries of column vector Y to find a unique instance of an entry, i, such that an ith entry of R and an ith entry of Y are sufficiently close in magnitude using a pre-determined standard of mathematical closeness.

8. The method of claim 1, further comprising the medical device, after step “

(d)”

, displaying a message to a user of the medical device to the effect that further invasive measurements are not needed.

9. The method of claim 1, wherein the bioparameter is glucose and wherein the method further includes periodically taking non-invasive measurements in proximity to A1C results and using the A1C results as invasive measurements transmitted to the invasive component to expand the learning matrix D and calibrate the non-invasive component in a range between a last valid A1C result and a maximum or minimum calibrated bioparameter value previously obtained.

10. The method of claim 1, wherein the mathematical function generates a scalar value representative of the signal.

11. The method of claim 1, further comprising obtaining the plurality of learning vectors in step “

(d)”

using different patients having common characteristics and collecting data of the non-invasive and invasive measurements through a telecommunications network, thereby creating a learning matrix, D^cluster representative of the bioparameter of a cluster of patients.

12. The method of claim 1, further comprising obtaining the plurality of learning vectors in step “

(d)”

using different patients having common characteristics and collecting data of the non-invasive and invasive measurements through a telecommunications network, thereby creating a learning matrix, D^universal representative of the bioparameter of an entire population.

13. A portable bioparameter-monitoring medical device usable by a patient, comprising:

a non-invasive component capable of generating non-invasive bioparametric readings of tissue of a body part of the patient upon insertion by the patient of a body part of the patient into the non-invasive component, the non-invasive component including at least one color image sensor to generate a series of color images of the tissue and to sense a magnitude of each of three colors at pixels of each color image, and including a first digital processor for processing the series of color images into a signal over time reflecting a distribution of each of the three colors over time;

an invasive component for obtaining an invasive bioparametric reading from blood of the patient, the invasive component also including a second digital processor for automatically transmitting the invasive bioparametric reading to the first digital processor of the non-invasive component, the invasive bioparametric readings forming entries in a column vector, Y,

the non-invasive component programmed to

(a) (i) use a mathematical function to convert the signal to a scalar learning number and (ii) repeat step “

(a)(i)”

, without necessarily using the same mathematical functions, to form a learning vector that corresponds to a scalar invasive bioparameter reading entry of column vector Y;

(b) form an n by n learning matrix, D, that is a regular matrix, by repeating step “

(a)”

to non-invasive readings and invasive readings enough times that the first digital processor has sufficient correlations between non-invasive bioparametric readings and invasive bioparametric readings to be able to measure the bioparameter based on a non-invasive bioparameter reading of the bioparameter at a pre-defined level of threshold acceptability;

(c) to obtain a coefficient of learning vector, C, by multiplying an inverse matrix of matrix, D by the column vector, Y;

(d) generate a new vector, V^new when a user non-invasively measures the bioparameter of the patient by using the one or more color image sensors in the non-invasive component of the device to generate a series of color images of tissue of the body part and by having the digital processor use a mathematical function to convert the signal to a scalar number and doing so n times to form V^new, without necessarily using the same mathematical functions;

(e) use the entries of V^new to form a regular matrix, D^new, of n by n size and whose structure of non-zero elements is identical to learning matrix, D, and

(f) use a digital processor to perform a matrix vector multiplication of D^new by coefficient of learning vector, C, to create a vector of non-invasive bioparameter measurement, R, and comparing entries of R with entries of Y to find one entry of R which represents a calibrated bioparameter value for the patient.

14. The medical device of claim 13, wherein the non-invasive component includes a light transmitting element that transmits light through a finger and generates the digital color images from light exiting the finger.

15. The medical device of claim 13, wherein the discrete digital signal incorporates a red matrix representing magnitudes of red light at pixels of a digital image of the finger, a green matrix representing magnitudes of green light at the pixels and a blue matrix representing magnitudes of blue light at the pixels.

16. The medical device of claim 13, further including a coupling element for maintaining the invasive component operatively engaged to the non-invasive component and allowing transmission of invasive bioparametric readings from the invasive component to the non-invasive component, wherein the coupling element also permits de-coupling of the invasive and non-invasive components from one another.

17. The medical device of claim 13, wherein the non-invasive component is further programmed to create an expanded learning matrix D_exp of (n+1) by (n+1) size by (i) incorporating V^new into learning matrix D and by having the first digital processor use a mathematical function to convert the signal of “

(f)”

to a (n+1)th scalar number and adding the (n+1)th scalar number alongside V^new to maintain expanded learning matrix D_exp as a regular matrix and by incorporating further invasive bioparameter measurements to expand column vector Y to (n+1) elements and transmit the further invasive bioparameter measurement to the non-invasive component.

18. A method of producing a portable bioparameter-monitoring medical device custom-tailored to a patient, the method comprising:

(a) providing directly or indirectly to a patient a medical device having (i) a non-invasive component capable of generating a non-invasive bioparametric reading of the patient'"'"'s bioparameter upon insertion by the patient of a body part into the non-invasive component, the non-invasive component including a first digital processor for processing digital color images of part of the body part and representing the digital images as a discrete signal over time, and having (ii) an invasive component for measuring the bioparameter from blood of the patient and obtaining an invasive bioparametric reading for the patient, the invasive component also including a second digital processor for transmitting the invasive bioparametric reading to the first digital processor of the non-invasive component, and (iii) a coupling element for maintaining the invasive component operatively engaged to the non-invasive component and allowing transmission of invasive bioparametric readings from the invasive component to the non-invasive component, the first digital processor also for calibrating the non-invasive component so that the non-invasive bioparametric readings for the patient approximate the invasive bioparametric readings for the patient for a given bioparameter under a predefined standard of approximation; and

(b) calibrating the non-invasive component to the patient by (i) invasively measuring the bioparameter of the patient using the invasive component, (ii) transmitting the invasive bioparameter readings to the non-invasive component, and (iii) non-invasively measuring the bioparameter of the patient within a proximity time of the invasive measuring using mathematical functions, and performing substeps (i), (ii) and (iii) enough times that the first digital processor has sufficient correlations between non-invasive bioparametric readings and invasive bioparametric readings to be able to measure the bioparameter using a non-invasive bioparameter reading at a pre-defined level of threshold acceptability.

19. A method of monitoring a bioparameter, comprising:

(a) invasively measuring the bioparameter of a patient using an invasive component of a bioparameter monitoring device and transmitting an invasive bioparameter reading to a non-invasive component of the bioparameter monitoring device, the invasive bioparameter reading to be entered in a column vector, Y;

(b) within a proximity time of step “

(a)”

, non-invasively measuring the bioparameter of the patient by using one or more variable sensors in the non-invasive component of the device to generate a series of data representing a magnitude of one or more variables of tissue of a body part of the patient and by converting the magnitudes into a series of electric signals, to produce a signal over time reflecting a distribution of each of the one or more variables over time;

(c) a digital processor of the non-invasive component (i) using a mathematical function to convert the signal to a scalar learning number and (ii) repeating step “

(c)(i)”

, without necessarily using the same mathematical function, to form a learning vector that corresponds to a scalar invasive bioparameter reading entry of column vector Y;

(d) from a plurality of learning vectors, a digital processor forming an n by n learning matrix, D, that is a regular matrix, by repeating steps “

(a)”

through “

(c)”

enough times that a digital processor has sufficient correlations between non-invasive bioparametric readings and invasive bioparametric readings to be able to measure the bioparameter using a non-invasive bioparameter reading at a pre-defined level of threshold acceptability;

(e) a digital processor obtaining a coefficient of learning vector, C, by multiplying an inverse matrix of learning matrix, D by the column vector Y;

(f) a digital processor obtaining a new vector, V^new by (i) non-invasively measuring the bioparameter of the patient by using the one or more variable sensors of the non-invasive component of the device to generate a series of data representing a magnitude of one or more variables of tissue of a body part of the patient and by converting the magnitudes into a series of electric signals, to produce a signal over time reflecting a distribution of each of the variables over time and by having a digital processor use a mathematical function to convert the signal to a scalar number and by (ii) repeating step “

(f)(i) n times to form V^new, without necessarily using the same mathematical functions;

(g) using the entries of V^new to forth a regular matrix, D^new, of n by n size and whose structure of non-zero elements is identical to a structure of non-zero elements of learning matrix, D; and

(h) using a digital processor to perform a matrix vector multiplication of D^new by coefficient of learning vector, C, to create a column vector of non-invasive bioparameter measurement, R, and comparing entries of R with entries of Y to find one entry of R which represents a calibrated bioparameter value for the patient.

20. The method of claim 19, wherein the variable sensed by the variable sensor is selected from the group consisting of temperature, conductivity and smell.