Non-invasive method and system for characterizing cardiovascular systems

• US 9,968,275 B2
• Filed: 05/05/2017
• Issued: 05/15/2018
• Est. Priority Date: 08/17/2012
• Status: Active Grant

• Current Assignee: Analytics For Life
• Original Assignee: Analytics For Life, Inc.
• Inventors: Gupta, Sunny, Yazdi, Mohsen Najafi, Burton, Timothy William Fawcett, Ramchandani, Shyamlal, Exner, Derek Vincent
• Primary Examiner(s): MAHMOOD, NADIA AHMAD
• Application Number:
  US15/588,148
• Publication Number:
  US 20170332927A1
• Time in Patent Office: 375 Days
• Field of Search: 600508, 600523, 600529, 600509
• CPC Class Codes: A61B 2576/023 for the heart
• 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 5/14551 for measuring blood gases
• CPC Class Codes: A61B 5/316 Modalities, i.e. specific diagnostic methods
• CPC Class Codes: A61B 5/318 Heart-related electrical modalities, e.g. electrocardiography [ECG]
• CPC Class Codes: A61B 5/339 Displays specially adapted therefor
• CPC Class Codes: A61B 5/341 Vectorcardiography [VCG]
• CPC Class Codes: A61B 5/7235 Details of waveform analysis A61B5/349 takes precedence
• CPC Class Codes: G16H 30/40 for processing medical images, e.g. editing

Abstract

The present disclosure uses physiological data, ECG signals as an example, to evaluate cardiac structure and function in mammals. Two approaches are presented, e.g., a model-based analysis and a space-time analysis. The first method uses a modified Matching Pursuit (MMP) algorithm to find a noiseless model of the ECG data that is sparse and does not assume periodicity of the signal. After the model is derived, various metrics and subspaces are extracted to image and characterize cardiovascular tissues using complex-sub-harmonic-frequencies (CSF) quasi-periodic and other mathematical methods. In the second method, space-time domain is divided into a number of regions, the density of the ECG signal is computed in each region and inputted into a learning algorithm to image and characterize the tissues.

Claims

1. A system for localizing and characterizing an architectural feature and/or a function of cardiovascular tissue, the system comprising:

one or more processors; and

a memory having instructions stored thereon, wherein execution of the instructions by the one or more processors causes the one or more processors to;

obtain a data set associated with at least one measured physiological signal associated with the tissue;

process data within the obtained data set to display, in an image of a three-dimensional representation of the tissue, at least one abnormality associated with the tissue by;

creating a phase space diagram based on the data;

dividing the phase space diagram into a plurality of regions; and

computing one or more space-time density values associated with each region,

wherein the one or more space-time density values contain information about non-linear variability of the at least one physiological signal; and

link, via one or more learning algorithms, one or more nonlinear nested sinusoidal Gaussian equations to a plurality of locations associated with the tissue based on the one or more space-time density values, each location of the plurality of locations being associated with an architectural feature and/or a function of the tissue to display the at least one abnormality in the image of the three-dimensional representation of the tissue.

2. The system of claim 1, wherein the at least one measured physiological signal comprises a biopotential signal.

3. The system of claim 1, wherein the plurality of regions comprise 12 regions associated with ventricular cardiovascular tissue.

4. The system of claim 1, wherein the 12 regions comprise an associated 12 quantities of space-time density values.

5. The system of claim 4, wherein the memory further comprises instructions that, when executed by the one or more processors, cause the one or more processors to compute spatial changes in a phase space matrix comprising the 12 quantities of space-time density values based on non-Fourier multi-dimensional fractional integral summation across leads that acquired the at least one physiological signal.

6. The system of claim 4, wherein the memory further comprises instructions that, when executed by the one or more processors, cause the one or more processors to compute spatial changes in a phase space matrix comprising the 12 quantities of space-time density values based on Fourier multi-dimensional fractional integral summation across leads that acquired the at least one physiological signal.

7. The system of claim 1, wherein the memory further comprises instructions that, when executed by the one or more processors, cause the one or more processors to compute dynamical signal density using non-Fourier or Fourier n dimensional fractional integral summation across leads that acquired the at least one physiological signal, wherein the order of the fractional integral is in the range of −

1.5 to −

2.5.

8. The system of claim 1, wherein the memory further comprises instructions that, when executed by the one or more processors, cause the one or more processors to compute dynamical signal density using non-Fourier or Fourier n dimensional fractional integral summation across leads that acquired the at least one physiological signal, wherein the order of the fractional integral is an irrational number.

9. The system of claim 1, wherein the memory further comprises instructions that when are executed by the one or more processors, cause the one or more processors to compute dynamical signal density using non-Fourier or Fourier n dimensional fractional integral summation across leads that acquired the at least one physiological signal, wherein the order of the fractional integral is a complex number.

10. The system of claim 1, wherein the memory further comprises instructions that, when executed by the one or more processors, cause the one or more processors to compute dynamical signal density using non-Fourier or Fourier n dimensional fractional integral summation across leads that acquired the at least one physiological signal, wherein the order of the fractional integral is a real number.

11. The system of claim 1, wherein the plurality of regions comprise six regions associated with cardiovascular atrial tissue.

12. The system of claim 11, wherein the six regions comprise an associated six quantities of space-time density values.

13. The system of claim 1, wherein the plurality of locations correspond to a 17-segment model of the heart.

14. The system of claim 1, wherein the one or more nonlinear nested sinusoidal Gaussian equations are used to produce a probability value corresponding to the presence of the abnormality.

15. The system of claim 1, wherein the abnormality is associated with a pathophysiological abnormality selected from the group consisting of hypertrophy, atrophy, scar, ischemia, edema, and fibrosis.

16. The system of claim 1, wherein the obtained data set comprise high resolution ECG data.

17. The system of claim 1, wherein at least one of the one or more learning algorithms comprises a genetic algorithm.

18. The system of claim 1, wherein the obtained data set is acquired via a lead selected from the group consisting of a single lead ECG, a 3-lead ECG, and a 12-lead ECG.

19. The system of claim 1, wherein the memory further comprises instructions that, when executed by the one or more processors, cause the one or more processors to remove a baseline wander from the obtained data set.

20. The system of claim 1, wherein the memory further comprises instructions that, when executed by the one or more processors, cause the one or more processors to further process the data within the obtained data set with a Wavelet Packets and use a resulting data set of the processing as a basis for creating the created phase space diagram.

21. The system of claim 1, wherein the memory further comprises instructions that, when executed by the one or more processors, cause the one or more processors to further process the data within the obtained data set with a Cosine Packets and use a resulting data set obtained via the further processing as a basis for creating the created phase space diagram.

22. The system of claim 1, wherein the memory further comprises instructions that, when executed by the one or more processors, cause the one or more processors to further process the data with one or more Chirplets and use a resulting data set obtained via the further processing as a basis for creating the created phase space diagram.

23. A non-transitory computer readable medium having instructions stored thereon, wherein execution of the instructions by one or more processors causes the one or more processors to:

obtain a data set associated with at least one measured physiological signal associated with a tissue;

process data within the obtained data set to display, in an image of a three-dimensional representation of the tissue, at least one abnormality associated with the tissue by;

creating a phase space diagram based on the data;

dividing the phase space diagram into a plurality of regions; and

computing one or more space-time density values associated with each region,

wherein the one or more space-time density values contain information about non-linear variability of the at least one physiological signal; and

link, via one or more learning algorithms, one or more nonlinear nested sinusoidal Gaussian equations to a plurality of locations associated with the tissue based on the one or more space-time density values, each location of the plurality of locations being associated with an architectural feature and/or a function of the tissue to display the at least one abnormality in the image of the three-dimensional representation of the tissue.

24. A system comprising:

a plurality of orthogonal leads configured to non-invasively acquire at least one physiological signal from a mammal, wherein the at least one physiological signal is used in analysis to localize and characterize an architectural feature and/or a function of cardiovascular tissue, wherein the analysis comprises;

processing data within the obtained data set to display, in an image of a three-dimensional representation of the tissue, at least one abnormality associated with the tissue by;

creating a phase space diagram based on data within the obtained data set;

dividing the phase space diagram into a plurality of regions; and

computing one or more space-time density values associated with each region,

wherein the one or more space-time density values contain information about non-linear variability of the at least one physiological signal; and

linking, via one or more learning algorithms, one or more nonlinear nested sinusoidal Gaussian equations to a plurality of locations associated with the tissue based on the one or more space-time density values, each location of the plurality of locations being associated with an architectural feature and/or a function of the tissue to display the at least one abnormality in the image of the three-dimensional representation of the tissue.