IP Library Granted Patent US 9,351,649
Granted Patent B2
US 9,351,649 · App. 14/026,739 · Granted May 31, 2016

System and method for determining video-based pulse transit time with time-series signals

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Quick Facts
Patent No.
US 9,351,649
App. No.
14/026,739
Granted
May 31, 2016
Kind
B2
Abstract

What is disclosed is a system and method for determining a subject of interest's arterial pulse transit time from time-varying source signals generated from video images. In one embodiment, a video imaging system is used to capture a time-varying source signal of a proximal and distal region of a subject of interest. The image frames are processed to isolate localized areas of a proximal and distal region of exposed skin of the subject. A time-series signal for each of the proximal and distal regions is extracted from the source video images. A phase angle is computed with respect to frequency for each of the time-series signals to produce respective phase v/s frequency curves for each region. Slopes within a selected cardiac frequency range are extracted from each of the phase curves and a difference is computed between the two slopes to obtain an arterial pulse transit time for the subject.

Claims (56)

1. A method for determining an arterial pulse transit time between a proximal and distal region of a subject of interest from source video images acquired using a video imaging system, the method comprising:

receiving time-varying source images acquired over at least one channel of a video imaging system, said source images comprising video images captured of a proximal and distal region of an area of exposed skin of a subject of interest in which plethysmographic signals can be registered and at least one background region in which plethysmographic signals cannot be registered, and said proximal and distal regions and said background region being exposed to the same environmental factors;

extracting, from said source images a first time-series signal for said proximal region and a second times series signal for said distal region and at least one background region time-series signal;

subtracting said first time-series signal and said background region times-series signal to obtain environmentally compensated time-series signal for said proximal region;

subtracting said second time-series signal and said background region times-series signal to obtain environmentally compensated time-series signal for said distal region;

computing a first phase angle φ 1 (ω) with respect to frequency ω of said environmentally compensated time-series signal for said proximal region;

computing a second phase angle φ 2 (ω) with respect to frequency ω of said environmentally compensated time-series signal for said distal region;

plotting said computed phases with respect to frequency to obtain respective phase vs frequency curves for each of said proximal and distal regions;

computing respective slopes T 1 =∂φ 1 /∂ω and T 2 =∂φ 2 /∂ω within a defined cardiac frequency range between 0.75 to 4.0 Hz from each of said respective phase vs frequency curves;

computing a difference between said slopes T 1 and T 2 , said difference comprising an arterial pulse transit time for said subject between said proximal and distal regions; and

storing, using a processor, said arterial pulse transit time to a storage device.

2. The method of claim 1 , wherein said time-varying source signal comprises any combination of: NIR images, RGB images, RGB with NIR images, multispectral images, thermal images, and hyperspectral images.

3. The method of claim 1 , further comprising determining, from said arterial pulse transit time, any of: a blood pressure in said subject's vascular network, a blood vessel dilation over time, a blood vessel blockage, a blood flow velocity, and the existence of a peripheral neuropathy.

4. The method of claim 1 , further comprising:

processing said video images to determine a region of skin; and

isolating said proximal and distal regions from said skin regions.

5. The method of claim 1 , wherein said video images are acquired by a single NIR channel, further comprising:

partitioning frames from said channel for each region into a non-skin region and a skin region; and

performing source separation to obtain both a heart rate signal and said phase vs frequency curve.

6. The method of claim 1 , wherein, in advance of extracting said time-series signals, further comprising compensating for any of: a motion induced blur, an imaging blur, harmonics which are not related to components of said subject's cardiac pulse, and slow illuminant variation.

7. The method of claim 1 , wherein said video imaging system used to acquire said time-varying source images of said proximal and distal regions comprises two video cameras, a first video camera acquiring a first video of said proximal region and a second video camera acquiring a second video of said distal region.

8. The method of claim 7 , wherein, in advance of extracting said time-series signals, further comprising synchronizing said first and second videos with respect to time.

9. The method of claim 1 , wherein the video imaging system is used in a non-contact, remote sensing environment.

10. A video based system for determining an arterial pulse transit time between a proximal and distal region of a subject of interest from source video images acquired using a video camera, the system comprising:

a video imaging system for capturing a time-varying source images over at least one channel, said source images comprising video images captured of a proximal and distal region of an exposed area of skin of a subject of interest where an arterial pulse transit time is desired to be determined; and

a processor in communication with said video camera and a memory, said processor executing machine readable instructions for performing:

receiving time-varying source images acquired over at least one channel of a video imaging system, said source images comprising video images captured of a proximal and distal region of an area of exposed skin of a subject of interest in which plethysmographic signals can be registered and at least one background region in which plethysmographic signals cannot be registered, and said proximal and distal regions and said background region being exposed to the same environmental factors;

extracting, from said source images a first time-series signal for said proximal region and a second times series signal for said distal region and at least one background region time-series signal;

subtracting said first time-series signal and said background region times-series signal to obtain environmentally compensated time-series signal for said proximal region;

subtracting said second time-series signal and said background region times-series signal to obtain environmentally compensated time-series signal for said distal region;

computing a first phase angle φ 1 (ω) with respect to frequency ω of said environmentally compensated time-series signal for said proximal region;

computing a second phase angle φ 2 (ω) with respect to frequency ω of said environmentally compensated time-series signal for said distal region;

plotting said computed phases with respect to frequency to obtain respective phase vs frequency curves for each of said proximal and distal regions;

computing respective slopes T 1 =∂φ 1 /∂ω and T 2 =∂φ 2 /∂ω within a defined cardiac frequency range between 0.75 to 4.0 Hz from each of said respective phase vs frequency curves;

computing a difference between said slopes T 1 and T 2 , said difference comprising an arterial pulse transit time for said subject between said proximal and distal regions; and

storing, using a processor, said arterial pulse transit time to a storage device.

11. The system of claim 10 , wherein said time-varying source signal comprises any combination of: NIR images, RGB images, RGB with NIR images, multispectral images, thermal images, and hyperspectral images.

12. The system of claim 10 , further comprising determining, from said arterial pulse transit time, any of: a blood pressure in said subject's vascular network, a blood vessel dilation over time, a blood vessel blockage, a blood flow velocity, and the existence of a peripheral neuropathy.

13. The system of claim 10 , further comprising:

processing said video images to determine a region of skin; and

isolating said proximal and distal regions from said skin regions.

14. The system of claim 10 , wherein said video images are acquired by a single NIR channel, further comprising:

partitioning frames from said channel for each region into a non-skin region and a skin region; and

performing source separation to obtain both a heart rate signal and said phase vs frequency curve.

15. The system of claim 10 , wherein, in advance of extracting said time-series signals, further comprising compensating for any of: a motion induced blur, an imaging blur, harmonics which are not related to components of said subject's cardiac pulse, and slow illuminant variation.

16. The system of claim 10 , wherein said video imaging system used to acquire said time-varying source images of said proximal and distal regions comprises two video cameras, a first video camera acquiring a first video of said proximal region and a second video camera acquiring a second video of said distal region.

17. The system of claim 16 , wherein, in advance of extracting said time-series signals, further comprising synchronizing said first and second videos with respect to time.

18. The system of claim 10 , wherein the video imaging system is used in a non-contact, remote sensing environment.

19. The method of claim 1 , wherein said time-varying source is a single color channel.

20. The method of claim 19 , wherein said single color channel is a green color channel.

21. The method of claim 1 , wherein said computing said respective slopes includes fitting a polynomial equation to each of said phase vs frequency curves.

22. The method of claim 1 , wherein said computing said respective slopes includes computing a derivative with respect to frequency.

23. The system of claim 10 , wherein said time-varying source is a single color channel.

24. The system of claim 23 , wherein said single color channel is a green color channel.

25. The system of claim 10 , wherein said computing said respective slopes includes fitting a polynomial equation to each of said phase vs frequency curves.

26. The system of claim 10 , wherein said computing said respective slopes includes computing a derivative with respect to frequency.

Assignments (9)
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Nov 6, 2025
From: XEROX CORPORATION
To: GENESEE VALLEY INNOVATIONS, LLC
Reel/Frame 073842/0479 →
SECOND LIEN NOTES PATENT SECURITY AGREEMENT Recorded Jul 2, 2025
From: XEROX CORPORATION
To: U.S. BANK TRUST COMPANY, NATIONAL ASSOCIATION, AS COLLATERAL AGENT
Reel/Frame 071785/0550 →
FIRST LIEN NOTES PATENT SECURITY AGREEMENT Recorded Apr 11, 2025
From: XEROX CORPORATION
To: U.S. BANK TRUST COMPANY, NATIONAL ASSOCIATION, AS COLLATERAL AGENT
Reel/Frame 070824/0001 →
SECURITY INTEREST Recorded Feb 13, 2024
From: XEROX CORPORATION
To: CITIBANK, N.A., AS COLLATERAL AGENT
Reel/Frame 066741/0001 →
TERMINATION AND RELEASE OF SECURITY INTEREST IN PATENTS RECORDED AT RF 064760/0389 Recorded Feb 13, 2024
From: CITIBANK, N.A., AS COLLATERAL AGENT
To: XEROX CORPORATION
Reel/Frame 068261/0001 →
SECURITY INTEREST Recorded Nov 20, 2023
From: XEROX CORPORATION
To: JEFFERIES FINANCE LLC, AS COLLATERAL AGENT
Reel/Frame 065628/0019 →
SECURITY INTEREST Recorded Jun 22, 2023
From: XEROX CORPORATION
To: CITIBANK, N.A., AS COLLATERAL AGENT
Reel/Frame 064760/0389 →
RELEASE OF SECURITY INTEREST IN PATENTS AT R/F 062740/0214 Recorded May 18, 2023
From: CITIBANK, N.A., AS AGENT
To: XEROX CORPORATION
Reel/Frame 063694/0122 →
SECURITY INTEREST Recorded Nov 10, 2022
From: XEROX CORPORATION
To: CITIBANK, N.A., AS AGENT
Reel/Frame 062740/0214 →