APPARATUS AND METHOD FOR ESTIMATING BIOLOGICAL COMPONENT

• US 20200129095A1
• Filed: 05/21/2019
• Published: 04/30/2020
• Est. Priority Date: 10/29/2018
• Status: Active Application

• Current Assignee: Samsung Electronics Co. Ltd.
• Original Assignee: Samsung Electronics Co. Ltd.
• Inventors: LEE, So Young, BAE, Sang Kon
• Application Number:
  US16/418,455
• Publication Number:
  US 20200129095A1
• Time in Patent Office: Days
• 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 5/7267 involving training the classification device
• 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: G16H 10/40 for data related to laboratory analysis, e.g. patient specimen analysis
• CPC Class Codes: G16H 30/40 for processing medical images, e.g. editing
• CPC Class Codes: G16H 50/20 for computer-aided diagnosis, e.g. based on medical expert systems

Abstract

An apparatus for estimating a biological component may include: a spectrometer configured to measure a background spectrum from an object; and a processor configured to generate a virtual spectrum as training data by combining the background spectrum with a virtual biological component signal including noise, and generate a prediction model for estimating the biological component based on the training data.

Claims

1. An apparatus for estimating a biological component, the apparatus comprising:

a spectrometer configured to measure a background spectrum from an object; and

a processor configured to generate a virtual spectrum as training data by combining the background spectrum with a virtual biological component signal including noise, and generate a prediction model for estimating the biological component based on the training data.

2. The apparatus of claim 1, wherein the spectrometer comprises:

a light source configured to emit light onto the object; and

a detector configured to detect light scattered or reflected from the object.

3. The apparatus of claim 1, wherein the processor is further configured to generate the virtual biological component signal based on a unit biological component spectrum and a light travel path of a light that emitted from the spectrometer and collected by the spectrometer after passing through the object.

4. The apparatus of claim 3, wherein the processor is further configured to generate the virtual biological component signal by adding a random noise to the virtual biological component signal for each wavelength of the virtual biological component signal.

5. The apparatus of claim 4, wherein the processor is further configured to perform smoothing on the virtual biological component signal.

6. The apparatus of claim 4, wherein the processor is further configured to perform a first smoothing on the virtual biological component signal for each wavelength, and then performs a second smoothing on the virtual biological component signal for each of a plurality of calibration times

7. The apparatus of claim 1, wherein the processor is further configured to obtain a virtual biological component value of the virtual biological component signal as the training data, and train the prediction model based on the training data.

8. The apparatus of claim 7, wherein the processor is further configured to obtain the virtual biological component value by combining a reference biological component value that is measured by an external device at a time when the background spectrum is measured, with a virtual biological component variation used for generating the virtual spectrum.

9. The apparatus of claim 1, wherein the processor is further configured to obtain a plurality of virtual biological component variations based on a plurality of biological component change patterns, and generate the virtual spectrum based on the plurality of virtual biological component variations.

10. The apparatus of claim 9, wherein the plurality of biological component change patterns are pre-defined for each user based on at least one of a user'"'"'s gender, age, and medical history.

11. The apparatus of claim 1, wherein the processor is further configured to combine the background spectrum with the virtual biological component signal by using Lambert-Beer'"'"'s law.

12. The apparatus of claim 1, wherein the spectrometer is further configured to measure a spectrum for estimating the biological component, the processor estimates the biological component by applying the prediction model to the spectrum that is measured for estimating the biological component.

13. The apparatus of claim 1, wherein the biological component comprises at least one of blood glucose, cholesterol, triglycerides, proteins, and uric acid.

14. A method of estimating a biological component, the method comprising:

measuring a background spectrum from an object;

obtaining a virtual spectrum as training data by combining the background spectrum with a virtual biological component signal including noise; and

generating a prediction model for estimating the biological component based on the training data.

15. The method of claim 14, wherein the obtaining the virtual spectrum as the training data comprises generating the virtual biological component signal based on a unit biological component spectrum and a light travel path of a light that is emitted to the object and then reflected or scattered from the object after passing though the object.

16. The method of claim 15, wherein the obtaining the virtual spectrum as the training data further comprises generating the virtual biological component signal by adding a random noise to the virtual biological component signal for each wavelength of the virtual biological component signal.

17. The method of claim 16, wherein the obtaining the virtual spectrum as the training data further comprises performing smoothing on the virtual biological component signal.

18. The method of claim 14, wherein the obtaining the virtual spectrum as the training data comprises performing a first smoothing on the virtual biological component signal for each wavelength, and then performing a second smoothing on the virtual biological component signal for each of a plurality of calibration times.

19. The method of claim 14, wherein:

the obtaining the virtual spectrum as the training data comprises obtaining a virtual biological component value of the virtual biological component signal as the training data; and

the generating the prediction model comprises training the prediction model based on the training data.

20. The method of claim 14, further comprising:

measuring a spectrum for estimating the biological component; and

estimating the biological component by applying the prediction model to the spectrum measured for estimating the biological component.