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

• US 9,459,201 B2
• Filed: 09/29/2015
• Issued: 10/04/2016
• Est. Priority Date: 09/29/2014
• Status: Active Grant

• Current Assignee: Zyomed Holdings, Inc.
• Original Assignee: Zyomed Corp.
• Inventors: Gulati, Sandeep, Ruchti, Timothy L., Antwerp, William V., Smith, John L.
• Primary Examiner(s): Kim, Kiho
• Application Number:
  US14/869,550
• Publication Number:
  US 20160097716A1
• Time in Patent Office: 371 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 of quantitating an analyte, the method comprising:

receiving from an uncharacterized sample an energy change value to be mapped, the energy change value corresponding to an uncharacterized sample;

mapping, via a first processor, the energy change value to be mapped to a quantity of an analyte in the uncharacterized sample, via an individual projector curve associating;

(i) energy change values obtained from a synthetic reference system to analyte quantities of the reference system, and (ii) energy change values obtained from a non-synthetic reference system to the analyte quantities of the reference system.

2. The method of claim 1, further comprising:

computing a representative absorption gradient (AG) using a first set of energy change values corresponding to a first feature pair, each energy change value in the first set corresponding to a respective path of radiation through a medium to be analyzed;

selecting the individual projector curve from a plurality of projector curves using the representative gradient value associated with the first feature pair; and

determining a quantity of an analyte using the selected individual projector curve, the energy change value to be mapped being a representative energy change value associated with the first feature pair.

3. The method of claim 2, wherein the representative gradient value comprises a normalized AG (NAG), the method further comprising:

weighing the representative gradient associated with the first feature pair by a weight associated with the first feature pair, to obtain the NAG.

4. The method of claim 2, wherein computing the representative energy change value comprises weighting one energy change value from the first set of energy change values by a weight associated with the first feature pair.

5. The method of claim 2, wherein a plurality of feature pairs comprises the first feature pair, the method further comprising:

generating a set of acceptable feature pairs from the plurality of feature pairs;

for each feature pair in the set of acceptable feature pairs computing a respective absorption gradient (AG) using a respective set of energy change values corresponding to the feature pair;

computing the representative AG value as an average of the respective AGs associated with each of the acceptable feature pairs.

6. The method of claim 5, wherein generating the set of acceptable feature pairs comprises testing for each feature pair in the plurality of feature pairs monotonicity of the corresponding set of energy change values across a plurality of illumination states.

7. The method of claim 5, wherein computing the AG for each acceptable feature pair comprises:

determining a slope of a regression of the corresponding set of energy change 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.

8. The method of claim 5, further comprising, prior to computing the average:

normalizing each of the AGs corresponding to the acceptable feature pairs using a weight associated with the corresponding acceptable feature pair.

9. The method of claim 5 wherein computing the representative energy change value comprises:

for each acceptable feature pair, selecting one energy change value from the respective set of energy change values;

weighting for each acceptable feature pair, the selected energy change value by a weight associated with the acceptable feature pair; and

setting an average of the weighted energy change values as the representative energy change value.

10. The method of claim 1, wherein:

the uncharacterized sample comprises a portion of tissue; and

the analyte comprise glucose.

11. The method of claim 1, wherein the energy change comprises one of energy gain and energy loss.

12. A system of quantitating an analyte, 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;

receive an energy change value to be mapped, the energy change value corresponding to an uncharacterized sample; and

map the energy change value to be mapped to a quantity of an analyte in the uncharacterized sample, via an individual projector curve associating;

(i) energy change values obtained from a synthetic reference system to analyte quantities of the reference system, and (ii) energy change values obtained from a non-synthetic reference system to the analyte quantities of the reference system.

13. The system of claim 12, wherein the instructions further program the processing unit to:

compute a representative absorption gradient (AG) using a first set of energy change values corresponding to a first feature pair, each energy change value in the first set corresponding to a respective path of radiation through a medium to be analyzed;

select the individual projector curve from a plurality of projector curves using the representative gradient value associated with the first feature pair; and

determine a quantity of an analyte using the selected individual projector curve, the energy change value to be mapped being a representative energy change value associated with the first feature pair.

14. The system of claim 13, wherein the representative gradient value comprises a normalized AG (NAG), wherein the instructions further program the processing unit to:

weigh the representative gradient associated with the first feature pair by a weight

associate with the first feature pair, to obtain the NAG.

15. The system of claim 13, wherein to compute the representative energy change value, the instructions further program the processing unit to weight one energy change value from the first set of energy change values by a weight associated with the first feature pair.

16. The system of claim 13, wherein a plurality of feature pairs comprises the first feature pair, wherein the instructions further program the processing unit to:

generate a set of acceptable feature pairs from the plurality of feature pairs;

for each feature pair in the set of acceptable feature pairs compute a respective absorption gradient (AG) using a respective set of energy change values corresponding to the feature pair;

compute the representative AG value as an average of the respective AGs associated with each of the acceptable feature pairs.

17. The system of claim 16, wherein to generate the set of acceptable feature pairs, the instructions further program the processing unit to test for each feature pair in the plurality of feature pairs monotonicity of the corresponding set of energy change values across a plurality of illumination states.

18. The system of claim 16, wherein to compute the AG for each acceptable feature pair, the instructions further program the processing unit to:

determine a slope of a regression of the corresponding set of energy change 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.

19. The system of claim 16, wherein the instructions further program the processing unit to:

prior to computing the average, normalize each of the AGs corresponding to the acceptable feature pairs using a weight associated with the corresponding acceptable feature pair.

20. The system of claim 16, wherein to compute the representative energy change value, the instructions further program the processing unit to:

for each acceptable feature pair, select one energy change value from the respective set of energy change values;

weight for each acceptable feature pair, the selected energy change value by a weight associated with the acceptable feature pair; and

set an average of the weighted energy change values as the representative energy change value.

21. The system of claim 12, wherein:

the uncharacterized sample comprises a portion of tissue; and

the analyte comprises glucose.

22. The system of claim 12, wherein the energy change comprises on of energy gain and energy loss.