Systems and methods for synthesis of zyotons for use in collision computing for noninvasive blood glucose and other measurements

• US 9,442,065 B2
• Filed: 01/12/2016
• Issued: 09/13/2016
• Est. Priority Date: 09/29/2014
• Status: Active Grant

• Current Assignee: Zyomed Holdings, Inc.
• Original Assignee: Zyomed Corp.
• Inventors: Gulati, Sandeep, Ruchti, Timothy L.
• Primary Examiner(s): Kim, Kiho
• Application Number:
  US14/993,885
• Publication Number:
  US 20160139042A1
• Time in Patent Office: 245 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

A synthesizer synthesizes Zyotons, waveforms that without a collision can travel substantially unperturbed in a propagation medium over a specified distance, for extracting via collision computing properties of interest of signals, such as the occurrence/absence of events and presence or concentrations of substances such as blood glucose, toxic chemicals, etc., obtained from high noise/clutter environments. The Zyotons are synthesized using base waveform families/generator functions unrelated to the signal environment. The Zyotons and corresponding carrier kernels include component(s) adapted to correspond to a signal property of interest and other component(s) adapted to correspond to other properties, such as noise and clutter. The number of each type of component(s) may be determined using a representative signal obtained from the environment that is optionally transformed via derivitization, addition of noise and/or another representative signal, etc. A base waveform family/generator function can be selected according to the representative signal morphology.

Claims

1. A method for enabling extraction of a property of an element of a specified environment, the method comprising:

selecting by a processor using at least one of;

(i) at least one waveform family, and (ii) at least one waveform generator, a set of frequency components for an analyte-information-representing band and a set of frequency components for a non-analyte-information-representing band; and

synthesizing by the processor a Zyoton by combining the set of frequency components selected for the analyte-information-representing band and the set of frequency components selected for the non-analyte-information representing band, wherein;

at least one frequency component in the set of frequency components for the analyte-information-representing band corresponds to a property to be extracted;

at least one of the frequency components for the non-analyte-information representing band does not correspond significantly to the property to be extracted; and

the selected set of frequency components of the waveform family allow the Zyoton to propagate through a substantially constant propagation medium over a length of a collision grid without a change in morphology thereof of greater than 20%.

2. The method of claim 1, wherein:

after a collision of the Zyoton with a waveform constructed to perturb the Zyoton within specified limits, the selected set of frequency components allow;

generation of a modified Zyoton via the collision; and

subsequent propagation of the modified Zyoton through the substantially constant propagation medium over the length of the collision grid without a change in morphology of the modified Zyoton of greater than 20%.

3. The method of claim 2, wherein the at least one waveform family comprises at least one of:

optical solitons, autosolitons, similaritons, custom solitons generated using a sine-Gordon-based equation, self-compressing similaritons, vortex-solitons, multi-color solitons, parabolic-Similaritons, and Ricci solitons.

4. The method of claim 1, wherein the at least one waveform family comprises at least one of:

solitons;

wavelets;

curvelets;

ridgelets;

bions;

elliptic waves comprising at least one of Jacobi elliptic functions and Weierstrass elliptic functions; and

nonautonomous similinear wave equations.

5. The method of claim 1, wherein the at least one waveform generator comprises at least one of:

meromorphic functions;

Gamma functions;

Riemann Zeta functions;

regular instantons;

Frobenius manifolds;

harmonic oscillators;

Hermite polynomials;

polynomial sequences;

asymptotic Hankel functions;

Bessel functions;

fractals;

Neumann functions (spherical);

poweroid coupled with sinusoidal functions;

spatial random fields;

cyclostationary series;

random number generators;

spherical harmonics;

chaotic attractors;

exponential attractors;

multipoint Krylov-subspace projectors;

Lyapunov functions;

inertial manifolds of Navier-Stokes equation;

evolution equation for polynomial nonlinear reaction-diffusion equation;

evolution equation for Kuramoto-Savashinsky equation;

evolution equation of exponential attractors;

Fourier series; and

Ramanujan theta functions.

6. The method of claim 1, wherein:

the specified environment comprises a medium to be analyzed; and

the property to be extracted comprises at least one of;

presence of an analyte, absence of the analyte, a quantity of an analyte, and a rate of change of the quantity of the analyte, in the medium to be analyzed.

7. The method of claim 1, further comprising:

selecting from the at least one waveform family a set of frequency components for a transition band; and

synthesizing the Zyoton by combining the set of frequency components for the analyte-information-representing band, the set of frequency components for the transition band, and the set of frequency components for the non-analyte-information-representing band.

8. A method for enabling extraction of a property of an element of a specified environment, the method comprising:

receiving in memory in communication with a processor a representative spectral signal from a data-collection domain, the representative spectral signal indicating a property of the element of the specified environment;

generating by the processor a Zyoton from at least one waveform family, the at least one waveform family being independent of the data-collection domain; and

generating by the processor a co-dependent carrier kernel for conditioning at least one feature corresponding to the representative spectral signal.

9. The method of claim 8, wherein:

the element of the specified environment comprises an analyte; and

the property of the element of the specified environment comprises concentration of the analyte.

10. The method of claim 9, wherein the specified environment further comprises at least one confounder.

11. The method of claim 10, wherein the property of the element of the specified environment that is represented by the representative spectral signal comprises energy loss via at least one of:

(i) absorption by the analyte, (ii) absorption by the at least one confounder, (iii) noise generated by variation in absorption by the at least one confounder, and (iv) noise generated by scattering.

12. The method of claim 11, wherein the at least one confounder comprises a dominant confounder.

13. The method of claim 9, wherein:

the specified environment comprises a portion of skin tissue; and

the analyte comprises glucose.

14. The method of claim 8, wherein the representative signal comprises a first model signal, and generating the Zyoton comprises:

obtaining a transformed signal from the first model signal representing energy loss via absorption by the analyte;

adding a noise signal to the transformed signal to obtain a second signal;

determining frequency components of the second signal;

using the frequency components of the second signal, determining a number of frequency components required to separate a high analyte absorption region of the transformed signal from a low analyte absorption region of the transformed signal;

selecting the at least one waveform family according to a morphology of one of the first model signal, the transformed signal, and the second signal;

obtaining a base Zyoton based on the at least one selected waveform family; and

selecting the determined required number of frequency components of the base Zyoton as frequency components of the Zyoton.

15. The method of claim 14, wherein:

the noise signal corresponds to an amount of noise that is a multiple of at least one of;

(i) a noise associated with the first model signal, and (ii) a noise associated with the transformed signal; and

the determined required number of frequency components represents a number of frequency components (k) required to separate the high analyte absorption region of the transformed signal from the low analyte absorption region of the transformed signal in presence of the specified amount of noise.

16. The method of claim 14, wherein obtaining the transformed signal comprises generating a second derivative of one of:

the first model signal and an interpolated first model signal.

17. The method of claim 14, wherein:

obtaining the transformed signal comprises generating a first composite signal by combining with the first model signal a second model signal representing energy loss via absorption by at least one non-dominant confounder; and

the determined required number of frequency components represents a number of frequency components (m) required to separate the high analyte absorption region of the transformed signal from the low analyte absorption region of the transformed signal in presence of the at least one non-dominant confounder.

18. The method of claim 14, wherein:

obtaining the transformed signal comprises generating a second composite signal by combining with the first model signal a third model signal representing energy loss via variations in absorption by a dominant confounder; and

the determined required number of frequency components represents a number of frequency components (j) required to separate the high analyte absorption region of the transformed signal from the low analyte absorption region of the transformed signal in presence of the dominant confounder and the specified amount of noise.

19. The method of claim 14, wherein the Zyoton comprises k analyte-information-representing components, k >

1, the method further comprising;

setting amplitudes of the k analyte-information-representing components within a first range of amplitudes according to an amplitude profile.

20. The method of claim 19, further comprising generating the amplitude profile by:

generating a plurality of wavelength-region pairs, each pair comprising a high absorption representing wavelength region corresponding to a respective wavelength range of the representative spectral signal and a low absorption representing wavelength region corresponding to a different respective wavelength range of the representative spectral signal;

for each pair in the plurality of wavelength-region pairs, computing a ratio of a magnitude of a region of the transformed signal corresponding to the high absorption representing wavelength region and a magnitude of a region of the transformed signal corresponding to the low absorption representing wavelength region;

determining a range of the magnitude ratios; and

setting the amplitude profile as a function bounded by the range of magnitude ratios, the function being one of linear, quadratic, and exponential.

21. The method of claim 8, wherein the Zyoton comprises k analyte-information-representing components, k >

1, and j non-analyte-information representing components, j >

1, the k analyte-information-representing components having amplitudes within a first range, the method further comprising;

randomly setting amplitudes of the j non-analyte-information representing components within a second range of amplitudes, any amplitude in the second range being less than 1/100 of any amplitude in the first range.

22. The method of claim 21, wherein the Zyoton further comprises m transition components, m >

1, the method further comprising;

randomly setting amplitudes of the m transition components within a third range of amplitudes, any amplitude in the third range being at least two times any amplitude in the second range.

23. The method of claim 22, wherein generating the co-dependent carrier kernel comprises:

for each of the k analyte-information-representing major components of the Zyoton, including in the carrier kernel as a corresponding analyte-information-representing major component a frequency component having a frequency within a specified threshold σ

of frequency of the corresponding analyte-information-representing k component of the Zyoton; and

including in the carrier kernel as at least one non-major component, at least one of;

at least one harmonic of at least one of an analyte-information-representing component, a transition component, and non-analyte-information-representing component included in the carrier kernel; and

for at least one non-major component of the Zyoton, including in the carrier kernel a frequency component having a frequency within the specified threshold σ

of frequency of the corresponding non-major component of the Zyoton.

24. The method of claim 23, wherein one of:

the non-major component of the Zyoton is a transition component, and the corresponding non-major component of the carrier kernel is a transition component; and

the non-major component of the Zyoton is a non-analyte-information-representing j component, and the corresponding non-major component of the carrier kernel is a non-analyte-information-representing j component.

25. The method of claim 21, further comprising:

setting amplitudes of the information-representing, transition, and non-information representing frequency components of the carrier kernel according to the amplitudes of the respective analyte-information-representing, transition, and non-analyte-information-representing Zyoton components scaled by a pre-set scaling factor.

26. The method of claim 21, further comprising:

selecting an amplitude scaling coefficient such that;

(i) a difference between a scaled velocity of the Zyoton, scaled by the amplitude scaling factor, and a velocity of the carrier kernel does not exceed a specified velocity difference threshold κ

^syn.; and

setting amplitudes of the analyte-information-representing, transition, and non-analyte-information-representing frequency components of the carrier kernel according to the amplitudes of the respective analyte-information-representing, transition, and non-analyte-information-representing Zyoton components scaled by the selected scaling factor.

27. A system for enabling extraction of a property of an element of a specified environment, 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;

select using at least one of;

(i) at least one waveform family, and (ii) at least one waveform generator, a set of frequency components for an analyte-information-representing band and a set of frequency components for a non-analyte-information representing band; and

synthesize a Zyoton by combining the set of frequency components selected for the analyte-information-representing band and the set of frequency components selected for the non-analyte-information-representing band, wherein;

at least one frequency component in the set of frequency components for the analyte-information-representing band corresponds to a property to be extracted;

at least one of the frequency components for the non-analyte-information-representing band does not correspond significantly to the property to be extracted; and

the selected set of frequency components of the waveform family allow the Zyoton to propagate through a substantially constant propagation medium over a length of a collision grid without a change in morphology thereof of greater than 20%.

28. The system of claim 27, wherein:

after a collision of the Zyoton with a waveform constructed to perturb the Zyoton within specified limits, the selected set of frequency components allow;

generation of a modified Zyoton via the collision; and

subsequent propagation of the modified Zyoton through the substantially constant propagation medium over the length of the collision grid without a change in morphology of the modified Zyoton of greater than 20%.

29. The system of claim 28, wherein the at least one waveform family comprises at least one of:

optical solitons, autosolitons, similaritons, custom solitons generated using a sine-Gordon-based equation, self-compressing similaritons, vortex-solitons, multi-color solitons, parabolic-Similaritons, and Ricci solitons.

30. The system of claim 27, wherein the at least one waveform family comprises at least one of:

optical solitons, autosolitons, similaritons, custom solitons generated using a sine-Gordon-based equation, self-compressing similaritons, vortex-solitons, multi-color solitons, parabolic-Similaritons, and Ricci solitons.

31. The system of claim 27, wherein the at least one waveform generator comprises at least one of:

meromorphic functions;

Gamma functions;

Riemann Zeta functions;

regular instantons;

Frobenius manifolds;

harmonic oscillators;

Hermite polynomials;

polynomial sequences;

asymptotic Hankel functions;

Bessel functions;

fractals;

Neumann functions (spherical);

poweroid coupled with sinusoidal functions;

spatial random fields;

cyclostationary series;

random number generators;

spherical harmonics;

chaotic attractors;

exponential attractors;

multipoint Krylov-subspace projectors;

Lyapunov functions;

inertial manifolds of Navier-Stokes equation;

evolution equation for polynomial nonlinear reaction-diffusion equation;

evolution equation for Kuramoto-Savashinsky equation;

evolution equation of exponential attractors;

Fourier series; and

Ramanujan theta functions.

32. The system of claim 27, wherein:

the specified environment comprises a medium to be analyzed; and

the property to be extracted comprises at least one of;

presence of an analyte, absence of the analyte, a quantity of an analyte, and a rate of change of the quantity of the analyte, in the medium to be analyzed.

33. The system of claim 27, wherein the instructions further program the processing unit to:

select from the at least one waveform family a set of frequency components for a transition band; and

synthesize the Zyoton by combining the set of frequency components for the analyte-information-representing band, the set of frequency components for the transition band, and the set of frequency components for the non-analyte-information-representing band.

34. A system for enabling extraction of a property of an element of a specified environment, 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 a representative spectral signal from a data-collection domain, the representative spectral signal indicating a property of the element of the specified environment;

generate a Zyoton from at least one waveform family, the at least one waveform family being independent of the data-collection domain; and

generate a co-dependent carrier kernel for conditioning at least one feature corresponding to the representative spectral signal.

35. The system of claim 34, wherein:

the element of the specified environment comprises an analyte; and

the property of the element of the specified environment comprises concentration of the analyte.

36. The system of claim 35, wherein the specified environment further comprises at least one confounder.

37. The system of claim 36, wherein the property of the element of the specified environment that is represented by the representative spectral signal comprises energy loss via at least one of:

(i) absorption by the analyte, (ii) absorption by the at least one confounder, (iii) noise generated by variation in absorption by the at least one confounder, and (iv) noise generated by scattering.

38. The system of claim 37, wherein the at least one confounder comprises a dominant confounder.

39. The system of claim 35, wherein:

the specified environment comprises a portion of skin tissue; and

the analyte comprises glucose.

40. The system of claim 34, wherein the representative signal comprises a first model signal and, to generate the Zyoton, the instructions further program the processing unit to:

obtain a transformed signal from the first model signal representing energy loss via absorption by the analyte;

add a noise signal to the transformed signal to obtain a second signal;

determine frequency components of the second signal;

using the frequency components of the second signal, determine a number of frequency components required to separate a high analyte absorption region of the transformed signal from a low analyte absorption region of the transformed signal;

select the at least one waveform family according to a morphology of one of the first model signal, the transformed signal, and the second signal;

obtain a base Zyoton based on the at least one selected waveform family; and

select the determined required number of frequency components of the base Zyoton as frequency components of the Zyoton.

41. The system of claim 40, wherein:

the noise signal corresponds to an amount of noise that is a multiple of at least one of;

(i) a noise associated with the first model signal, and (ii) a noise associated with the transformed signal; and

the determined required number of frequency components represents a number of frequency components (k) required to separate the high analyte absorption region of the transformed signal from the low analyte absorption region of the transformed signal in presence of the specified amount of noise.

42. The system of claim 40, wherein, to obtain the transformed signal, the instructions further program the processing unit to generate a second derivative of one of:

the first model signal and an interpolated first model signal.

43. The system of claim 40, wherein:

to obtain the transformed signal, the instructions further program the processing unit to generate a first composite signal by combining with the first model signal a second model signal representing energy loss via absorption by at least one non-dominant confounder; and

the determined required number of frequency components represents a number of frequency components (m) required to separate the high analyte absorption region of the transformed signal from the low analyte absorption region of the transformed signal in presence of the at least one non-dominant confounder.

44. The system of claim 40, wherein:

to obtain the transformed signal, the instructions further program the processing unit to generate a second composite signal by combining with the first model signal a third model signal representing energy loss via variations in absorption by a dominant confounder; and

the determined required number of frequency components represents a number of frequency components (j) required to separate the high analyte absorption region of the transformed signal from the low analyte absorption region of the transformed signal in presence of the dominant confounder and the specified amount of noise.

45. The system of claim 40, wherein the Zyoton comprises k analyte-information-representing components, k>

1, wherein the instructions further program the processing unit to;

set amplitudes of the k analyte-information-representing components within a first range of amplitudes according to an amplitude profile.

46. The system of claim 45, wherein to generate the amplitude profile, the instructions further program the processing unit to:

generate a plurality of wavelength-region pairs, each pair comprising a high absorption representing wavelength region corresponding to a respective wavelength range of the representative spectral signal and a low absorption representing wavelength region corresponding to a different respective wavelength range of the representative spectral signal;

for each pair in the plurality of wavelength-region pairs, compute a ratio of a magnitude of a region of the transformed signal corresponding to the high absorption representing wavelength region and a magnitude of a region of the transformed signal corresponding to the low absorption representing wavelength region;

determine a range of the magnitude ratios; and

set the amplitude profile as a function bounded by the range of magnitude ratios, the function being one of linear, quadratic, and exponential.

47. The system of claim 34, wherein the Zyoton comprises k analyte-information- representing components, k>

1, and j non-analyte-information-representing components, j>

1, the k analyte-information-representing components having amplitudes within a first range, wherein the instructions further program the processing unit to;

randomly set amplitudes of the j non-analyte-information-representing components within a second range of amplitudes, any amplitude in the second range being less than 1/100 of any amplitude in the first range.

48. The system of claim 47, wherein the Zyoton further comprises m transition components, m >

1, wherein the instructions further program the processing unit to;

randomly set amplitudes of the m transition components within a third range of amplitudes, any amplitude in the third range being at least two times any amplitude in the second range.

49. The system of claim 48, wherein, to generate the co-dependent carrier kernel, the instructions further program the processing unit to:

for each of the k analyte-information-representing major components of the Zyoton, include in the carrier kernel as a corresponding analyte-information-representing major component a frequency component having a frequency within a specified threshold σ

of frequency of the corresponding analyte-information-representing k component of the Zyoton; and

include in the carrier kernel as at least one non-major component, at least one of;

at least one harmonic of at least one of an analyte-information-representing component, a transition component, and non-analyte-information-representing component included in the carrier kernel; and

for at least one non-major component of the Zyoton, include in the carrier kernel a frequency component having a frequency within the specified threshold σ

of frequency of the corresponding non-major component of the Zyoton.

50. The system of claim 49, wherein one of:

the non-major component of the Zyoton is a transition component, and the corresponding non-major component of the carrier kernel is a transition component; and

the non-major component of the Zyoton is a non-analyte-information-representing j component, and the corresponding non-major component of the carrier kernel is a non-analyte-information-representing j component.

51. The system of claim 47, wherein the instructions further program the processing unit to:

set amplitudes of the analyte-information-representing, transition, and non-analyte-information-representing frequency components of the carrier kernel according to the amplitudes of the respective analyte-information-representing, transition, and non-analyte-information representing Zyoton components scaled by a pre-set scaling factor.

52. The system of claim 47, wherein the instructions further program the processing unit to:

select an amplitude scaling coefficient such that;

(i) a difference between a scaled velocity of the Zyoton, scaled by the amplitude scaling factor, and a velocity of the carrier kernel does not exceed a specified velocity difference threshold κ

^syn.; and

set amplitudes of the analyte-information-representing, transition, and non-analyte-information representing frequency components of the carrier kernel according to the amplitudes of the respective analyte-information-representing, transition, and non-analyte-information-representing Zyoton components scaled by the selected scaling factor.