SYSTEM AND METHOD FOR CONTACTLESS BLOOD PRESSURE DETERMINATION

• US 20180199870A1
• Filed: 03/16/2018
• Published: 07/19/2018
• Est. Priority Date: 12/19/2016
• Status: Active Grant

• Current Assignee: NuraLogix Corporation
• Original Assignee: NuraLogix Corporation
• Inventors: LEE, Kang, KABAKOV, Evgueni, LEVY, Phil
• Granted Patent: US 10,376,192 B2
• Time in Patent Office: Days
• CPC Class Codes: A61B 5/0037 Performing a preliminary scan, e.g. a prescan for identifying a region of interest
• CPC Class Codes: A61B 5/0205 Simultaneously evaluating both cardiovascular conditions and different types of body conditions, e.g. heart and respiratory condition
• CPC Class Codes: A61B 5/021 Measuring pressure in heart or blood vessels A61B5/0205 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 5/026 Measuring blood flow A61B3/1233, A61B3/1241 take precedence
• CPC Class Codes: A61B 5/0261 using optical means, e.g. infrared light
• CPC Class Codes: A61B 5/0816 Measuring devices for examining respiratory frequency measuring frequency of electric signals G01R23/00
• CPC Class Codes: A61B 5/145 Measuring characteristics of blood in vivo, e.g. gas concentration, pH value; Measuring characteristics of body fluids or tissues, e.g. interstitial fluid, cerebral tissuemeasuring of blood pressure or blood flow A61B5/02; non-radiation detecting or locating of foreign bodies in blood A61B5/06
• 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 5/725 using specific filters therefor, e.g. Kalman or adaptive filters A61B5/316 take precedence
• CPC Class Codes: A61B 5/7267 involving training the classification device
• CPC Class Codes: A61B 5/7278 Artificial waveform generation or derivation, e.g. synthesising signals from measured signals A61B5/327 takes precedence
• CPC Class Codes: G16H 40/63 for local operation
• 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

Abstract

A system and method for contactless blood pressure determination. The method includes: receiving a captured image sequence; determining, using a trained hemoglobin concentration (HC) changes machine learning model, bit values from a set of bitplanes in the captured image sequence that represent the HC changes of the subject; determining a blood flow data signal; extracting one or more domain knowledge signals associated with the determination of blood pressure; building a trained blood pressure machine learning model with a blood pressure training set, the blood pressure training set including the blood flow data signal of the one or more predetermined ROIs and the one or more domain knowledge signals; determining, using the blood pressure machine learning model trained with a blood pressure training set, an estimation of blood pressure; and outputting the determination of blood pressure.

Claims

1. A method for contactless blood pressure determination of a human subject, the method executed on one or more processors, the method comprising:

receiving a captured image sequence of light re-emitted from the skin of one or more humans;

determining, using a trained hemoglobin concentration (HC) changes machine learning model trained with a HC changes training set, bit values from a set of bitplanes in the captured image sequence that represent the HC changes of the subject, the HC changes training set comprising the captured image sequence;

determining a blood flow data signal of one or more predetermined regions of interest (ROIs) of the subject captured on the images based on the bit values from the set of bitplanes that represent the HC changes;

extracting one or more domain knowledge signals associated with the determination of blood pressure from the blood flow data signal of each of the ROIs;

building a trained blood pressure machine learning model with a blood pressure training set, the blood pressure training set comprising the blood flow data signal of the one or more predetermined ROIs and the one or more domain knowledge signals;

determining, using the blood pressure machine learning model trained with the blood pressure training set, an estimation of blood pressure for the human subject; and

outputting the determination of blood pressure.

2. The method of claim 1, wherein determining the estimation of blood pressure comprises determining an estimation of systolic blood pressure (SBP) and diastolic blood pressure (DBP).

3. The method of claim 1, wherein the set of bitplanes in the captured image sequence that represent the HC changes of the subject are the bitplanes that are determined to significantly increase a signal-to-noise ratio (SNR).

4. The method of claim 1, further comprising preprocessing the blood flow data signal with a Butterworth filter or a Chebyshev filter.

5. The method of claim 1, wherein extracting the one or more domain knowledge signals comprises determining a magnitude profile of the blood flow data signal of each of the ROIs.

7. The method of claim 1, wherein extracting the one or more domain knowledge signals comprises determining a phase profile of the blood flow data signal of each of the ROIs.

8. The method of claim 7, wherein determining the phase profile comprises:

applying a multiplier junction to the phase profile to generate a multiplied phase profile; and

applying a low pass filter to the multiplied phase profile to generate a filtered phase profile.

9. The method of claim 7, wherein determining the phase profile comprises determining a beat profile, the beat profile comprising a plurality of beat signals based on a Doppler or an interference effect.

10. The method of claim 1, wherein extracting the one or more domain knowledge signals comprises determining at least one of systolic uptake, peak systolic pressure, systolic decline, dicrotic notch, pulse pressure, and diastolic runoff of the blood flow data signal of each of the ROIs.

11. The method of claim 1, wherein extracting the one or more domain knowledge signals comprises determining waveform morphology features of the blood flow data signal of each of the ROIs.

12. The method of claim 1, wherein extracting the one or more domain knowledge signals comprises determining one or more biosignals, the biosignals comprising at least one of heart rate measured from the human subject, Mayer waves measured from the human subject, and breathing rates measured from the human subject.

13. The method of claim 1, further comprising receiving ground truth blood pressure data, and wherein the blood pressure training set further comprises the ground truth blood pressure data.

14. The method of claim 13, wherein the ground truth blood pressure data comprises at least one of an intra-arterial blood pressure measurement of the human subject, an auscultatory measurement of the human subject, or an oscillometric measurement of the human subject.

15. The method of claim 1, further comprising applying a plurality of band-pass filters, each having a separate passband, to each of the blood flow data signals to produce a bandpass filter (BPF) signal set for each ROI, and wherein the blood pressure training set comprising the BPF signal set for each ROI.

6. The method of claim 6, wherein determining the magnitude profile comprises using digital filters to create a plurality of frequency filtered signals of the blood flow data signal in the time-domain for each image in the captured image sequence.

16. A system for contactless blood pressure determination of a human subject, the system comprising one or more processors and a data storage device, the one or more processors configured to execute:

a transdermal optical imaging (TOI) module to receive a captured image sequence of light re-emitted from the skin of one or more humans, the TOI module determines, using a trained hemoglobin concentration (HC) changes machine learning model trained with a HC changes training set, bit values from a set of bitplanes in the captured image sequence that represent the HC changes of the subject, the HC changes training set comprising the captured image sequence, the TOI module determines a blood flow data signal of one or more predetermined regions of interest (ROIs) of the subject captured on the images based on the bit values from the set of bitplanes that represent the HC changes;

a profile module to extract one or more domain knowledge signals associated with the determination of blood pressure from the blood flow data signal of each of the ROIs;

a machine learning module to build a trained blood pressure machine learning model with a blood pressure training set, the blood pressure training set comprising the blood flow data signal of the one or more predetermined ROIs and the one or more domain knowledge signals, the machine learning module determines, using the blood pressure machine learning model trained with the blood pressure training set, an estimation of blood pressure of the human subject; and

an output module to output the determination of blood pressure.

17. The system of claim 16, wherein determination of the estimation of blood pressure by the machine learning module comprises determining an estimation of systolic blood pressure (SBP) and diastolic blood pressure (DBP).

18. The system of claim 16, wherein the set of bitplanes in the captured image sequence that represent the HC changes of the subject are the bitplanes that are determined to significantly increase a signal-to-noise ratio (SNR).

19. The system of claim 16, further comprising a filter module to preprocess the blood flow data signal with a Butterworth filter or a Chebyshev filter.

20. The system of claim 16, wherein extracting the one or more domain knowledge signals by the profile module comprises determining a magnitude profile of the blood flow data signal of each of the ROIs.

21. The system of claim 20, wherein determining the magnitude profile by the profile module comprises using digital filters to create a plurality of frequency filtered signals of the blood flow data signal in the time-domain for each image in the captured image sequence.

22. The system of claim 16, wherein extracting the one or more domain knowledge signals by the profile module comprises determining a phase profile of the blood flow data signal of each of the ROIs.

25. The system of claim 16, wherein extracting the one or more domain knowledge signals by the profile module comprises determining at least one of systolic uptake, peak systolic pressure, systolic decline, dicrotic notch, pulse pressure, and diastolic runoff of the blood flow data signal of each of the ROIs.

26. The system of claim 16, wherein extracting the one or more domain knowledge signals by the profile module comprises determining waveform morphology features of the blood flow data signal of each of the ROIs.

27. The system of claim 16, wherein extracting the one or more domain knowledge signals by the profile module comprises determining one or more biosignals, the biosignals comprising at least one of heart rate measured from the human subject, Mayer waves measured from the human subject, and breathing rates measured from the human subject.

28. The system of claim 16, wherein the profile module receives ground truth blood pressure data, and wherein the blood pressure training set further comprises the ground truth blood pressure data.

29. The system of claim 28, wherein the ground truth blood pressure data comprises at least one of an intra-arterial blood pressure measurement of the human subject, an auscultatory measurement of the human subject, or an oscillometric measurement of the human subject.

30. The system of claim 16, further comprising a filter module to apply a plurality of band-pass filters, each having a separate passband, to each of the blood flow data signals to produce a bandpass filter (BPF) signal set for each ROI, and wherein the blood pressure training set comprising the BPF signal set for each ROI.

23. The system of claim 23, wherein determining the phase profile by the profile module comprises:

applying a multiplier junction to the phase profile to generate a multiplied phase profile; and

applying a low pass filter to the multiplied phase profile to generate a filtered phase profile.

24. The system of claim 23, wherein determining the phase profile by the profile module comprises determining a beat profile, the beat profile comprising a plurality of beat signals based on a Doppler or an interference effect.