SYSTEM AND METHOD FOR OBTAINING HEALTH DATA USING A NEURAL NETWORK

• US 20180214088A1
• Filed: 02/17/2018
• Published: 08/02/2018
• Est. Priority Date: 09/24/2016
• Status: Active Grant

• Current Assignee: Trilinear Bioventures LLC
• Original Assignee: Sanmina-SCI Corporation
• Inventors: Newberry, Robert Steven
• Granted Patent: US 10,888,280 B2
• Time in Patent Office: Days
• CPC Class Codes: A61B 2560/0223 of calibration, e.g. protocols for calibrating sensors
• CPC Class Codes: A61B 5/0002 Remote monitoring of patients using telemetry, e.g. transmission of vital signals via a communication network A61B5/07 takes precedence; transmission systems for measured values G08C; transmission H04B; transmission of digital information H04L; wireless communication networks H04W
• CPC Class Codes: A61B 5/0022 Monitoring a patient using a global network, e.g. telephone networks, internet
• CPC Class Codes: A61B 5/0077 Devices for viewing the surface of the body, e.g. camera, magnifying lens
• 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 5/0816 Measuring devices for examining respiratory frequency measuring frequency of electric signals G01R23/00
• 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 5/14551 for measuring blood gases
• CPC Class Codes: A61B 5/6817 Ear canal
• CPC Class Codes: A61B 5/6826 Finger
• 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 5/7264 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 5/7275 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 5/743 Displaying an image simultaneously with additional graphical information, e.g. symbols, charts, function plots
• CPC Class Codes: G16H 50/20 for computer-aided diagnosis, e.g. based on medical expert systems
• CPC Class Codes: G16H 50/30 for calculating health indices; for individual health risk assessment
• CPC Class Codes: G16H 50/50 for simulation or modelling of medical disorders
• CPC Class Codes: Y02A 90/10 Information and communication technologies [ICT] supporting adaptation to climate change, e.g. for weather forecasting or climate simulation

Abstract

A photoplethysmography (PPG) circuit or non-contact camera obtains PPG signals at a plurality of wavelengths. A signal processing module obtains at least a first spectral response around a first wavelength and a second spectral response around a second wavelength. The signal processing device generates PPG input data using the PPG signals, wherein the PPG input data includes one or more parameters obtained from each of the first spectral response and the second spectral response. A neural network processing device generates an input vector including the PPG input data and determines an output vector including health data, wherein the health data includes for example, an oxygen saturation level, a glucose level or a blood alcohol level.

Claims

1. A device, comprising:

a signal processing module configured to;

receive photoplethysmography (PPG) signals, wherein the PPG signals include a first spectral response obtained from light reflected around a first wavelength from skin tissue of a patient and a second spectral response obtained from light reflected around a second wavelength from skin tissue of a patient, and wherein the first wavelength has a high absorption coefficient for nitric oxide (NO) and wherein the second wavelength has a lower absorption coefficient for NO;

generate PPG input data using the PPG signals, wherein the PPG input data includes at least one of;

the first spectral response and the second spectral response;

or

one or more parameters obtained from the first spectral response and the second spectral response; and

a neural network processing device configured to;

generate an input vector including the PPG input data; and

determine an output vector including health data, wherein the health data includes one or more of;

an oxygen saturation level, an NO concentration level or a glucose concentration level.

2. The device of claim 1, wherein the first wavelength is in a UV range and the second wavelength is in at least one of:

a visible range or an infrared (IR) range.

3. The device of claim 1, wherein the first wavelength is in a range of 370 nm to 410 nm.

4. The device of claim 1, wherein the health data further includes a risk of sepsis.

5. The device of claim 1, wherein the health data further includes a level of vasodilation.

6. The device of claim 1, wherein the health data further includes a diabetic risk factor.

7. The device of claim 1, wherein the health data further includes blood pressure, heart rate, and respiration rate.

8. The device of claim 1, wherein the input vector further includes patient data on pre-existing conditions, wherein at least one pre-existing condition includes data on a potential head injury and wherein the health data further includes a risk of concussion.

9. The device of claim 1, wherein the input vector further includes patient data on pre-existing conditions, wherein at least one pre-existing condition is a traumatic event and wherein the health data further includes a diagnosis of post traumatic stress disorder (PTSD).

10. The device of claim 1, wherein the PPG input data includes:

a value L<sub>λ

1</sub> generated using the first spectral response, wherein the value L<sub>λ

1</sub> isolates the first spectral response due to pulsating blood flow; and

a value L<sub>λ

2</sub> generated using the second spectral response, wherein the value L<sub>λ

2</sub> isolates the second spectral response due to pulsating blood flow.

11. The device of claim 10, wherein the PPG input data further includes:

a value R<sub>λ

1,λ

2</sub> obtained from a ratio of the value L<sub>λ

1</sub> and the value L<sub>λ

2</sub>.

12. The device of claim 1, wherein the PPG input data includes:

a first AC component signal I_AC generated using the first spectral response;

or

a second AC component signal I_AC generated using the second spectral response.

13. The device of claim 1, wherein the PPG input data includes:

a plurality of systolic points and diastolic points generated using the first spectral response; and

a plurality of systolic points and diastolic points generated using the second spectral response.

14. The device of claim 1, wherein the PPG signals are generated by at least one of:

a PPG circuit or a non-contact camera.

15. The device of claim 1, wherein the neural network processing device is further configured to:

periodically receive an updated learning vector, wherein the updated learning vector is generated from an updated training set; and

reconfigure one or more parameters for generating the output vector using the updated learning vector.

16. The device of claim 1, wherein the device is implemented within a user device or is configured to communicate with a user device.

17. A device, comprising:

a signal processing module configured to;

receive photoplethysmography (PPG) signals, wherein the PPG signals include a first spectral response obtained from light reflected around a first wavelength from skin tissue of a patient and a second spectral response obtained from light reflected around a second wavelength from skin tissue of a patient and a third spectral response obtained from light reflected around a third wavelength from skin tissue of a patient;

generate PPG input data using the PPG signals, wherein the PPG input data includes at least one of;

the first spectral response, the second spectral response and the third spectral response;

or

one or more parameters obtained from each of the first spectral response, the second spectral response, and the third spectral response; and

a neural network processing device configured to;

generate an input vector including the PPG input data; and

determine an output vector including health data, wherein the health data includes an oxygen saturation level and at least one additional substance in blood flow.

18. The device of claim 17, wherein the first wavelength, second wavelength and third wavelength include at least three of:

390 nm, 468 nm, 530 nm, 592 nm, 660 nm, 880 nm, 940 nm or 1050 nm in a range of +/−

20 nm from these wavelengths.

19. The device of claim 17, wherein the PPG input data includes characteristic features related to the shape of the PPG waveform;

and

wherein the health data includes a concentration level of one or more of;

LDL-Cholesterol, HDL-Cholesterol, and Triglycerides.

20. The device of claim 19, wherein the first wavelength is in a range of 20 nm around 440 nm, the second wavelength is in a range of 20 nm around 550 nm and wherein the third wavelength has a low absorption coefficient for cholesterol in blood flow;

and

wherein the health data includes a concentration level of one or more of;

LDL-Cholesterol, HDL-Cholesterol, Triglycerides.

21. The device of claim 17, wherein a first wavelength has a high absorption coefficient for nitric oxide (NO) in blood flow and wherein a second wavelength has a lower absorption coefficient for NO in blood flow;

and wherein the health data includes at least one of;

an NO concentration level or a glucose concentration level.

22. The device of claim 17, wherein the first wavelength has a high absorption coefficient for a liver enzyme and is in a range of 448 nm to 488 nm, wherein the liver enzyme includes an alcohol dehydrogenase (ADH) related enzyme;

and

wherein the second wavelength has a lower absorption coefficient for the ADH related enzyme in blood flow; and

wherein the health data includes a blood alcohol level.

23. The device of claim 17, wherein the first wavelength has a high absorption coefficient for ethanol and is in a range of approximately 1030 nm to 1070 nm;

and

wherein the second wavelength has a lower absorption coefficient for the ethanol in blood flow; and

wherein the health data includes a blood alcohol level.

24. The device of claim 17, wherein the first wavelength has a high absorption coefficient for a first type of antigen on a surface of red blood cells;

and

wherein the second wavelength has a high absorption coefficient for a second type of antigen on a surface of red blood cells;

wherein the third wavelength has a low absorption coefficient for red blood cells; and

wherein the health data includes a blood type.

25. The device of claim 17, wherein the first wavelength has a high absorption coefficient for creatinine;

and

wherein the second wavelength has a low absorption coefficient for creatinine in blood flow; and

wherein the health data includes a concentration level of creatinine in blood flow.

26. The device of claim 17, wherein the first wavelength has a high absorption coefficient for an electrolyte in blood flow;

and

wherein the second wavelength has a low absorption coefficient for the electrolyte in blood flow; and

wherein the health data includes a level of the electrolyte in the blood flow.

27. A neural network processing device, comprising:

a signal processing module configured to;

receive photoplethysmography (PPG) signals, wherein the PPG signals include a first spectral response obtained from light reflected around a first wavelength from skin tissue of a patient and a second spectral response obtained from light reflected around a second wavelength from skin tissue of a patient;

generate PPG input data using the PPG signals, wherein the PPG input data includes at least one of;

the first spectral response and the second spectral response;

or

one or more parameters obtained from the first spectral response and the second spectral response; and

a processing device configured to;

generate an input vector including the PPG input data; and

determine an output vector including health data, wherein the health data includes one or more of;

a blood alcohol level, a liver enzyme level, or an ethanol level.

28. The neural network processing device of claim 27, wherein the first wavelength includes a high absorption coefficient for P450 liver enzyme and is in a range of approximately 468 nm.

29. The neural network processing device of claim 27, wherein the first wavelength includes a high absorption coefficient for ethanol and is in a range of approximately 1050 nm.