Systems and methods for noninvasive blood glucose and other analyte detection and measurement using collision computing

• US 9,448,164 B2
• Filed: 01/12/2016
• Issued: 09/20/2016
• Est. Priority Date: 09/29/2014
• Status: Active Grant

• Current Assignee: Zyomed Holdings, Inc.
• Original Assignee: Zyomed Corp.
• Inventors: Gulati, Sandeep, Ruchti, Timothy L., Van Antwerp, William, Smith, John L.
• Primary Examiner(s): Kim, Kiho
• Application Number:
  US14/993,819
• Publication Number:
  US 20160139045A1
• Time in Patent Office: 252 Days
• US Class Current: 1/1
• CPC Class Codes: A61B 2560/0223 of calibration, e.g. protocols for calibrating sensors
• CPC Class Codes: A61B 5/0075 by spectroscopy, i.e. measuring spectra, e.g. Raman spectroscopy, infrared absorption spectroscopy A61B5/0071 takes precedence
• CPC Class Codes: A61B 5/024 Detecting, measuring or recording pulse rate or heart rate A61B5/0205, A61B5/021 take precedence
• CPC Class Codes: A61B 5/02416 using photoplethysmograph signals, e.g. generated by infrared radiation A61B5/14552 takes precedence
• CPC Class Codes: A61B 5/02433 for infrared radiation
• CPC Class Codes: A61B 5/14532 for measuring glucose, e.g. by tissue impedance measurement
• CPC Class Codes: A61B 5/14546 for measuring analytes not otherwise provided for, e.g. ions, cytochromes
• CPC Class Codes: A61B 5/1455 using optical sensors, e.g. spectral photometrical oximeters
• CPC Class Codes: A61B 5/1495 Calibrating or testing of in-vivo probes
• CPC Class Codes: A61B 5/7203 for noise prevention, reduction or removal
• CPC Class Codes: A61B 5/7235 Details of waveform analysis A61B5/349 takes precedence
• CPC Class Codes: A61B 5/7253 characterised by using transforms
• CPC Class Codes: A61B 5/7264 Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems neural networks per se G06N3/00; expert systems per se G06N5/00
• CPC Class Codes: A61B 5/7267 involving training the classification device
• CPC Class Codes: G01N 21/3577 for analysing liquids, e.g. polluted water
• CPC Class Codes: G01N 21/359 using near infrared light
• CPC Class Codes: G01N 21/4795 spatially resolved investigating of object in scattering medium in vivo A61B
• CPC Class Codes: G01N 21/49 within a body or fluid
• CPC Class Codes: G01N 2201/0612 Laser diodes
• CPC Class Codes: G01N 2201/062 LED's
• CPC Class Codes: G01N 2201/067 : Electro-optic, magneto-optic, acousto-optic elements
• CPC Class Codes: G01N 2201/12 : Circuits of general importance; Signal processing
• CPC Class Codes: G01N 2201/129 : Using chemometrical methods
• CPC Class Codes: G01N 33/4833 : of solid biological material, e.g. tissue samples, cell cultures tissue in vivo A61B5/00; cell suspensions G01N33/48735
• CPC Class Codes: G01N 33/49 : Blood chemical methods for determining blood cell populations G01N33/5094; chemical analysis of blood groups or blood types G01N33/80
• CPC Class Codes: G16H 50/20 : for computer-aided diagnosis, e.g. based on medical expert systems
• CPC Class Codes: G16H 50/70 : for mining of medical data, e.g. analysing previous cases of other patients

Abstract

In a noninvasive system for detection/measurement of glucose and other analytes in a medium such as tissue, spectra from the medium are deconstructed into features. Conditioned features, which contain frequency components specific to glucose or the other analytes, are derived from one or more features by modulating a carrier kernel with the feature. The conditioned features are computationally collided with one or more Zyotons that are co-dependent with the conditioned features. One or more collisions amplify a property of the analyte e.g., energy absorbed by glucose in tissue from radiation directed to the skin. A gradient of several values of the amplified property, each value corresponding to a particular radiation pattern according to a spectroscopic tomographic sequence, is used to select a suitable projector curve, with which a representative amplified value is projected to an accurate estimate of the concentration of glucose or the other analytes, without needing personalized calibration.

Claims

1. A method for determining concentration of glucose in a tissue sample, the method comprising the steps of:

(a) obtaining energy absorbed by glucose from energy directed to the tissue sample as a result of a collision between two co-dependent waveforms; and

(b) projecting, via a first processor, the energy absorbed by glucose to a glucose concentration.

2. The method of claim 1, further comprising:

(c) receiving a feature corresponding to a specified wavelength range of a spectral signal and representing at least one of;

(i) absorption of energy, within the specified wavelength range, by at least one of;

(A) glucose in the tissue sample, and (B) at least one confounder in the tissue sample, and

(ii) loss of energy within the specified wavelength range due to scattering; and

(d) generating a conditioned feature by modulating a carrier kernel using the received feature.

3. The method of claim 2, wherein receiving the feature comprises:

directing near-infrared radiation to the tissue sample;

collecting radiation from the tissue sample;

generating an absorption spectrum from the collected radiation; and

selecting a region of the absorption spectrum bounded by the wavelength range as the feature.

4. The method of claim 3, wherein collecting the radiation comprises at least one of receiving diffusely reflected radiation from the tissue sample and receiving radiation transmitted through the tissue sample.

5. The method of claim 2, wherein:

one of the two co-dependent waveforms comprises an original Zyoton constructed to amplify energy absorbed by the feature; and

the carrier kernel is constructed such that the original Zyoton and the conditioned feature obtained by modulating the carrier kernel using the feature are co-dependent.

6. The method of claim 5, wherein colliding the original Zyoton with the conditioned feature comprises, in a first iteration:

colliding the original Zyoton with the conditioned feature to obtain a modified Zyoton; and

renormalizing the modified Zyoton to obtain a renormalized Zyoton.

7. The method of claim 6, further comprising:

in each of −

1 additional iterations, >

1;

colliding the original Zyoton with a renormalized Zyoton from an immediately preceding iteration, to obtain a remodified Zyoton; and

renormalizing the remodified Zyoton to obtain a renormalized modified Zyoton; and

computing energy gain of the modified Zyoton obtained after N iterations relative to energy of the original Zyoton to determine the energy absorbed by glucose.

8. The method of claim 7, wherein the number of iterations is selected such that accuracy of the glucose concentration measurement, as determined by Absolute Relative Deviation (ARD) relative to a glucose concentration measurement obtained using a reference method, is less than 15%.

9. The method of claim 7, wherein the number of iterations is selected such that accuracy of a plurality of glucose concentration measurements, as determined by Mean Absolute Relative Deviation (MARD) relative to a plurality of glucose concentration measurements obtained using a reference method, is less than 15%.

10. The method of claim 7, wherein the conditioned feature is derived from a glucose feature, the glucose feature representing at least absorption of energy by glucose within a first specified wavelength range.

11. The method of claim 10, further comprising:

repeating the steps (a) through (c) with respect to another conditioned feature derived from a non-glucose feature paired with the glucose feature, forming a first feature pair, the non-glucose feature representing at least one of;

(i) absorption of energy, within a second specified wavelength range, by at least one confounder in the tissue sample, and (ii) loss of energy due to scattering within the second specified wavelength range;

computing energy gain of the modified Zyoton corresponding to the non-glucose feature relative to energy of the original Zyoton; and

computing net renormalized spectral energy gain (NRSEG) for the first feature pair based on energy gains corresponding to the glucose feature and the non-glucose feature of the first feature pair.

12. The method of claim 11, further comprising normalizing the NRSEG for the first feature pair according to a weight designated to the first feature pair, to obtain a normalized NRSEG.

13. The method of claim 11, wherein:

receiving the glucose feature and the non-glucose feature comprises directing via a source near infra-red radiation to the tissue sample and collecting via a detector radiation from the tissue sample; and

the projecting step comprises;

(i) computing a plurality of NRSEG values for the first feature pair, each NRSEG value corresponding to a respective illumination state of the near infra-red radiation from an illumination sequence;

(ii) computing a normalized absorption gradient (NAG) based on the plurality of NRSEG values;

(iii) selecting from a plurality of individual projector curves an individual projector curve such that the computed NAG is within lower bound and upper bound NAG values associated with that individual projector curve; and

(iv) determining glucose concentration using the selected individual projector curve and a representative energy absorption value based on the plurality of energy absorption values.

14. The method of claim 13, further comprising repeating step (i) for each additional feature pair in a set of feature pairs comprising the first feature pair, and computing a plurality of NRSEG values for each additional feature pair in the set, each NRSEG value in a respective plurality of NRSEG values corresponding to a respective illumination state of the near infra-red radiation from an illumination sequence, wherein:

computing the NAG comprises;

for each feature pair in the set designated as acceptable;

(A) computing an absorption gradient (AG), and (B) weighting the AG according to a weight associated with the corresponding acceptable feature pair; and

averaging the weighted AGs to obtain the NAG; and

the representative energy absorption value is computed by;

selecting a particular illumination state;

for each feature pair in the set designated as acceptable, weighting an NRSEG value corresponding to the particular illumination state according to a weight associated with the corresponding acceptable feature pair; and

averaging the weighted NRSEG values, the representative energy absorption value being set to the average of the weighted NRSEG values.

15. The method of claim 14, further comprising, prior to computing the NAG, for the plurality of NRSEG values corresponding to at least one feature pair in the set:

computing a mean NRSEG value; and

excluding an NRSEG value that is different from the mean NRSEG value by a specified threshold.

16. The method of claim 14, further comprising:

determining monotonicity of the plurality of NRSEG values corresponding to each feature pair in the set; and

designating each feature pair having monotonicity as acceptable.

17. The method of claim 13, wherein determining the glucose concentration using the selected individual projector curve comprises interpolation, comprising:

adding to a product of the representative energy absorption value and a slope of the selected individual projector curve, an intercept of the selected individual projector curve.

18. The method of claim 13, wherein the selected individual projector curve is one of a linear curve, a semi-linear curve, and a non-linear curve.

19. The method of claim 13, wherein the plurality of individual projector curves comprises at least nine individual projector curves.

20. The method of claim 13, wherein determining the glucose concentration using the selected individual projector curve comprises mapping the representative energy absorption value to glucose concentration using the selected individual projector curve.

21. The method of claim 13, wherein determining the normalized absorption gradient comprises:

determining a slope of a regression of the plurality of NRSEG values with respect to the illumination states, the illumination states being ordered such that successive illumination states represent monotonically changing distance between the source and at least one detector.

22. The method of claim 11, wherein projecting the energy absorbed by glucose to glucose concentration comprises mapping the computed NRSEG value to glucose concentration using a single projector curve.

23. The method of claim 22, further comprising designating the determined glucose concentration to one of a plurality of concentration bands.

24. The method of claim 11, wherein obtaining energy absorbed by glucose as a result of collision comprises a single collision iteration.

25. A system for determining concentration of glucose in a tissue sample, the system comprising:

a first processor; and

a first memory in electrical communication with the first processor, the first memory comprising instructions which, when executed by a processing unit comprising at least one of the first processor and a second processor, and in electronic communication with a memory module comprising at least one of the first memory and a second memory, program the processing unit to;

(a) obtain energy absorbed by glucose from energy directed to the tissue sample as a result of a collision between two co-dependent waveforms; and

(b) project the energy absorbed by glucose to a glucose concentration.

26. The system of claim 25, wherein the instructions further program the processing unit to:

(c) receive a feature corresponding to a specified wavelength range of a spectral signal and representing at least one of;

(i) absorption of energy, within the specified wavelength range, by at least one of;

(A) glucose in the tissue sample, and (B) at least one confounder in the tissue sample, and

(ii) loss of energy within the specified wavelength range due to scattering; and

(d) generate a conditioned feature by modulating a carrier kernel using the received feature.

27. The system of claim 26, wherein to receive a feature, the instructions further program the processing unit to:

direct near-infrared radiation to the tissue sample;

collect radiation from the tissue sample;

generate an absorption spectrum from the collected radiation; and

select a region of the absorption spectrum bounded by the wavelength range as the feature.

28. The system of claim 27, wherein to collect the radiation, the instructions further program the processing apparatus to:

at least one of receive diffusely reflected radiation from the tissue sample and receive radiation transmitted through the tissue sample.

29. The system of claim 26, wherein:

one of the two co-dependent waveforms comprises an original Zyoton constructed to amplify energy absorbed by the feature; and

the carrier kernel is constructed such that the original Zyoton and the conditioned feature obtained by modulating the carrier kernel using the feature are co-dependent.

30. The system of claim 29, wherein to collide the original Zyoton with the conditioned feature, the instructions further program the processing unit to, in a first iteration:

collide the original Zyoton with the conditioned feature to obtain a modified Zyoton; and

renormalize the modified Zyoton to obtain a renormalized Zyoton.

31. The system of claim 30, wherein the instructions further program the processing unit to:

in each of N—

1 additional iterations, N>

1;

collide the original Zyoton with a renormalized Zyoton from an immediately preceding iteration, to obtain a remodified Zyoton; and

renormalize the remodified Zyoton to obtain a renormalized modified Zyoton; and

compute energy gain of the modified Zyoton obtained after N iterations relative to energy of the original Zyoton to determine the energy absorbed by glucose.

32. The system of claim 31, wherein the number of iterations N is selected such that accuracy of the glucose concentration measurement, as determined by Absolute Relative Deviation (ARD) relative to a glucose concentration measurement obtained using a reference method, is less than 15%.

33. The system of claim 31, wherein the number of iterations N is selected such that accuracy of a plurality of glucose concentration measurements, as determined by Mean Absolute Relative Deviation (MARD) relative to a plurality of glucose concentration measurements obtained using a reference method, is less than 15%.

34. The system of claim 31, wherein the conditioned feature is derived from a glucose feature, the glucose feature representing at least absorption of energy by glucose within a first specified wavelength range.

35. The system of claim 34, wherein the instructions further program the processing unit to:

repeat the steps (a) through (c) with respect to another conditioned feature derived from a non-glucose feature paired with the glucose feature, forming a first feature pair, the non-glucose feature representing at least one of;

(i) absorption of energy, within a second specified wavelength range, by at least one confounder in the tissue sample, and (ii) loss of energy due to scattering within the second specified wavelength range;

compute energy gain of the modified Zyoton corresponding to the non-glucose feature relative to energy of the original Zyoton; and

compute net renormalized spectral energy gain (NRSEG) for the first feature pair based on energy gains corresponding to the glucose feature and the non-glucose feature of the first feature pair.

36. The system of claim 35, wherein the instructions further program the processing unit to normalize the NRSEG for the first feature pair according to a weight designated to the first feature pair, to obtain a normalized NRSEG.

37. The system of claim 35, wherein:

to receive the glucose feature and the non-glucose feature, the instructions further program the processing unit to direct via a source near infra-red radiation to the tissue sample and collect via a detector radiation from the tissue sample; and

to project the energy absorbed by glucose to the glucose concentration, the instructions further program the processing unit to;

(i) compute a plurality of NRSEG values for the first feature pair, each NRSEG value corresponding to a respective illumination state of the near infra-red radiation from an illumination sequence;

(ii) compute a normalized absorption gradient (NAG) based on the plurality of NRSEG values;

(iii) select from a plurality of individual projector curves an individual projector curve such that the computed NAG is within lower bound and upper bound NAG values associated with that individual projector curve; and

(iv) determine glucose concentration using the selected individual projector curve and a representative energy absorption value based on the plurality of energy absorption values.

38. The system of claim 37, wherein the instructions further program the processing unit to repeat step (i) for each additional feature pair in a set of feature pairs comprising the first feature pair, and compute a plurality of NRSEG values for each additional feature pair in the set, each NRSEG value in a respective plurality of NRSEG values corresponding to a respective illumination state of the near infra-red radiation from an illumination sequence, wherein:

to compute the NAG, the instructions further program the processing unit to;

for each feature pair in the set designated as acceptable;

(A) compute an absorption gradient (AG), and (B) weight the AG according to a weight associated with the corresponding acceptable feature pair; and

average the weighted AGs to obtain the NAG; and

to compute the representative energy absorption value, the instructions further program the processing unit to;

select a particular illumination state;

for each feature pair in the set designated as acceptable, weight an NRSEG value corresponding to the particular illumination state according to a weight associated with the corresponding acceptable feature pair; and

average the weighted NRSEG values, the representative energy absorption value being set to the average of the weighted NRSEG values.

39. The system of claim 38, wherein the instructions further program the processing unit to, prior to computing the NAG, for the plurality of NRSEG values corresponding to at least one feature pair in the set:

compute a mean NRSEG value; and

exclude a NRSEG value that is different from the mean NRSEG value by a specified threshold.

40. The system of claim 38, wherein the instructions further program the processing unit to:

determine monotonicity of the plurality of NRSEG values corresponding to each feature pair in the set; and

designate each feature pair having monotonicity as acceptable.

41. The system of claim 37, wherein to determine the glucose concentration using the selected individual projector curve, the instructions further program the processing unit to perform an interpolation, comprising adding to a product of the representative energy absorption value and a slope of the selected individual projector curve, an intercept of the selected individual projector curve.

42. The system of claim 37, wherein the selected individual projector curve is one of a linear curve, a semi-linear curve, and a non-linear curve.

43. The system of claim 37, wherein the plurality of individual projector curves comprises at least nine individual projector curves.

44. The system of claim 37, wherein to determine the glucose concentration using the selected individual projector curve, the instructions further program the processing unit to map the representative energy absorption value to glucose concentration using the selected individual projector curve.

45. The system of claim 37, wherein to determine the normalized absorption gradient, the instructions further program the processing unit to:

determine a slope of a regression of the plurality of NRSEG values with respect to the illumination states, the illumination states being ordered such that successive illumination states represent monotonically changing distance between the source and at least one detector.

46. The system of claim 35, wherein to project the energy absorbed by glucose to glucose concentration, the instructions further program the processing unit to map the computed NRSEG value to glucose concentration using a single projector curve.

47. The system of claim 46, wherein the instructions further program the processing unit to designate the determined glucose concentration to one of a plurality of concentration bands.

48. The system of claim 35, wherein to obtain energy absorbed by glucose as a result of collision, the instructions further program the processing unit to perform a single collision iteration.