Non-invasive measurement of skin thickness and glucose concentration with Raman spectroscopy and method of calibration thereof

• US 9,924,894 B2
• Filed: 06/03/2015
• Issued: 03/27/2018
• Est. Priority Date: 06/03/2015
• Status: Active Grant

• Current Assignee: Hong Kong Applied Science And Technology Research Institute Co., Ltd.
• Original Assignee: Hong Kong Applied Science And Technology Research Institute Co., Ltd.
• Inventors: Zhang, Chun, Lau, Tsz Ho, Chan, Yuk Cheung, Tsang, Wei Mong
• Primary Examiner(s): Winakur, Eric
• Assistant Examiner(s): Liu, Chu Chuan (JJ)
• Application Number:
  US14/729,075
• Publication Number:
  US 20160354015A1
• Time in Patent Office: 1,028 Days
• CPC Class Codes: A61B 2562/0238 Optical sensor arrangements for performing transmission measurements on body tissue
• 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 5/0507 using microwaves or terahertz waves
• CPC Class Codes: A61B 5/1075 for measuring dimensions by non-invasive methods, e.g. for determining thickness of tissue layer A61B8/0858 takes precedence
• CPC Class Codes: A61B 5/1076 for measuring dimensions inside body cavities, e.g. using catheters A61B3/1005 takes precedence
• CPC Class Codes: A61B 5/1079 using optical or photographic means
• 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 5/1495 Calibrating or testing of in-vivo probes
• 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 5/7275 Determining trends in physiological measurement data; Predicting development of a medical condition based on physiological measurements, e.g. determining a risk factor
• CPC Class Codes: A61B 5/7278 Artificial waveform generation or derivation, e.g. synthesising signals from measured signals A61B5/327 takes precedence

Abstract

The present invention discloses a non-invasive method of measuring skin thickness and blood glucose concentration of a subject by a Raman system. The advantage of the present invention is that a single Raman spectrum is used to measure both the skin thickness and glucose concentration. The skin thickness and Raman intensity retrieved from the same Raman spectrum are both utilized to yield a more accurate blood glucose concentration. The present invention also discloses a Raman system for measuring physiological data of a subject. It comprises a Raman spectroscopic unit and a signal processing unit.

Claims

1. A method of operating a physiological measurement system for measuring a concentration of glucose in the blood of a subject comprising the steps of:

a) receiving a Raman signal from a predetermined area of said subject'"'"'s skin;

said skin comprising at least a first layer and a second layer;

b) obtaining a first data relating to the thickness of said first layer;

c) acquiring a second data relating to the glucose concentration within said second layer based on said Raman signal; and

d) determining said concentration of glucose in the blood of said subject based on said first data and said second data,

wherein said step of obtaining said first data further comprises the steps of;

measuring a first intensity of said Raman signal at a first predetermined wavenumber and a second intensity of said Raman signal at a second predetermined wavenumber;

calculating a ratio of said first intensity and said second intensity; and

deriving said first data based on said ratio and a first prediction model.

2. The method of claim 1, wherein said first predetermined wavenumber is selected from a spectral range of 1300 cm⁻

1 to 1340 cm<sup>−

1</sup>; and

said second predetermined wavenumber is selected from a spectral range of 1440 cm<sup>−

1</sup> to 1460 cm<sup>−

1</sup>.

3. The method of claim 1, wherein said first prediction model satisfies the equation

4. The method of claim 3, wherein said first calibration comprises steps of:

a) collecting a plurality of said ratios at different body locations of at least one subject;

b) measuring skin thickness for each said different body location by a reference method;

c) generating a calibration curve correlating said ratio to said skin thickness for each said different body location; and

d) determining said first set of constants from said calibration curve.

5. The method of claim 4, wherein said reference method is selected from an invasive method and a non-invasive method;

said non-invasive method being selected from optical coherence tomography method, ultrasound imaging method, Terahertz imaging method, and near-infrared absorption.

6. The method of claim 4 wherein said determining step in claim 1 further comprises the step of computing said concentration of glucose in the blood of said subject based on said first data, said second data and a second prediction model.

7. The method of claim 6 wherein said second prediction model further comprising a second set of constants which are determined by a second calibration.

8. The method of claim 7, wherein said second data is at least one intensity of said Raman signal and said second prediction model satisfies the equation

9. The method of claim 7, wherein said second data is at least one intensity of said Raman signal and said second prediction model satisfies the equation

10. The method of claim 9, wherein said skin thickness mean value is obtained by averaging at least two values of skin thickness obtained from substantially same body location of same subject.

11. The method of claim 7, wherein said second data is at least one intensity of said Raman signal and said second prediction model satisfies the equation

12. The method of claim 7, wherein said second data is at least one intensity of said Raman signal and said second prediction model satisfies the equation

13. The method of claim 12, wherein said Raman ratio mean value is obtained by averaging at least two values of Raman ratio for the skin thickness obtained from substantially same body location of same subject.

14. The method of claim 7, wherein said second calibration comprises steps of:

a) measuring a plurality of blood glucose concentrations over a time course at a predetermined body location from same subject by a second reference method;

b) concurrently, collecting a plurality of Raman spectra from said predetermined body location of said same subject;

c) deriving at least one group of variables from each said Raman spectrum; and

d) determining said second set of constants for said subject based on said second prediction model using said plurality of blood glucose concentration and said group of variables.

15. The method of claim 14, wherein said at least one group of variables is chosen from at least one intensity of said Raman signal, said ratio, a relative Raman ratio, said thickness of said first layer, a relative skin thickness and any combination therefrom.

16. The method of claim 14, wherein said second set of constants is obtained through partial least squares regression.

17. A physiological measurement system for measuring physiological data of a subject at a predetermined area of said subject'"'"'s skin comprising:

a) a Raman spectroscopic unit comprises

(i) an excitation light source that illuminates an optical signal into said predetermined area of said subject'"'"'s skin, said subject'"'"'s skin further comprising a first layer and a second layer; and

(ii) an optical detector that receives a Raman signal reflected from said predetermined area of said subject'"'"'s skin; and

b) a signal processing unit comprises

(i) a microprocessor coupled to said optical detector;

(ii) a non-transitory computer-readable storage medium coupled to said microprocessor, said computer-readable storage medium encoded with computer-readable codes to instruct said microprocessor to execute the following steps;

i. acquiring a first data relating to the skin thickness of said first layer from said Raman signal;

ii. acquiring a second data relating to the glucose concentration within said second layer from said Raman signal;

iii. computing said skin thickness of said first layer based on said first data; and

iv. computing said glucose concentration of said second layer based on said skin thickness of said first layer, said second data, and a second prediction model,

wherein said step of acquiring said first data further comprises the steps of;

measuring a first intensity of said Raman signal at a first predetermined wavenumber and a second intensity of said Raman signal at a second predetermined wavenumber;

calculating a ratio of said first intensity and said second intensity; and

deriving said first data based on said ratio and a first prediction model.

18. A method of operating a physiological measurement system for measuring the thickness of a predetermined area of a subject'"'"'s skin;

said method comprising the steps of;

a) receiving an Raman signal from said predetermined area;

said Raman signal comprising a plurality of signal components, each said signal component identified by a wavenumber thus forming a composite Raman spectrum;

b) identifying a first predetermined wavenumber and a second predetermined wavenumber from said Raman signal;

c) retrieving a first intensity of said Raman signal at said first predetermined wavenumber and a second intensity of said Raman signal at said second predetermined wavenumber;

d) calculating a ratio of said first intensity and said second intensity; and

e) determining the thickness of said predetermined area of said subject'"'"'s skin based on said ratio and a first prediction model,

wherein said first prediction model comprises a functional relationship among a plurality of attributes correlating the thickness of said predetermined area and said ratio.

19. The method of claim 18, wherein said functional relationship is:

20. The method of claim 18, wherein said first calibration comprises steps of:

a) collecting a plurality of said ratios at different locations of at least one subject;

b) measuring skin thickness for each said different location by a reference method; and

c) generating a calibration curve correlating said ratio to said skin thickness for each said different location.

21. The method of claim 18, wherein said first predetermined wavenumber and said second predetermined wavenumber are determined by:

a) measuring a plurality of skin thicknesses at a plurality of body locations from a plurality of subjects by said reference method;

b) obtaining the corresponding Raman spectrum of each said body location;

said Raman spectrum comprising a plurality of signal with wavenumber ranging from 300 cm<sup>−

1</sup> to 1800 cm<sup>−

1</sup>;

c) selecting a first set of wavenumbers sensitive to skin characteristic composition of said body locations and a second set of wavenumbers insensitive to skin characteristic composition of said body locations;

d) selecting a first wavenumber from said first set and a second wavenumber from said second set;

e) retrieving a first intensity from said Raman spectrum at said first wavenumber and a second intensity from said Raman spectrum at said second wavenumber;

f) calculating a Raman ratio of said first intensity and said second intensity for each Raman spectrum;

g) repeating step d-f for all combinations of said first wavenumber and said second wavenumber;

h) for each said first wavenumber and each said second wavenumber, identifying a set of candidate wavenumber pairs wherein in each said candidate wavenumber pair, said Raman ratios and said skin thicknesses of said plurality of subjects conform to a generic functional relationship as specified in said first prediction model;

i) determining an optimal wavenumber pair among said set of candidate wavenumber pairs that yields most compact representation of said generic functional relationship; and

j) assigning said first wavenumber and said second wavenumber in said optimal wavenumber pair as said first predetermined wavenumber and said second predetermined wavenumber respectively.

22. The method of claim 21 further comprises the steps of:

a) for each said body location, identifying a cluster of points;

each said point registering said Raman ratio and said skin thickness of a subject measured at said body location; and

each cluster having a cluster center;

b) retaining those candidate wavenumber pairs wherein said cluster centers at said body locations fit said generic functional relationship, and

c) determining said optimal wavenumber pair as an candidate wavenumber pair among said candidate wavenumber pairs that yields minimum aggregated standard deviations of said clusters.