Medical devices for mapping cardiac tissue

• US 10,368,767 B2
• Filed: 10/08/2018
• Issued: 08/06/2019
• Est. Priority Date: 06/20/2014
• Status: Active Grant

• Current Assignee: Boston Scientific Scimed, Inc. (Boston Scientific Corporation)
• Original Assignee: Boston Scientific Scimed, Inc. (Boston Scientific Corporation)
• Inventors: Laughner, Jacob I., Shome, Shibaji, Hultz, Paul, Stalsberg, Kevin J., Thakur, Pramodsingh H.
• Primary Examiner(s): Stice, Paula J
• Application Number:
  US16/154,258
• Publication Number:
  US 20190038161A1
• Time in Patent Office: 302 Days
• CPC Class Codes: A61B 18/1492 having a flexible, catheter-like structure, e.g. for heart ablation A61B18/1477 takes precedence
• CPC Class Codes: A61B 2018/00357 Endocardium
• CPC Class Codes: A61B 2018/00577 Ablation
• CPC Class Codes: A61B 2018/00839 Bioelectrical parameters, e.g. ECG, EEG
• CPC Class Codes: A61B 2505/05 Surgical care
• CPC Class Codes: A61B 5/287 Holders for multiple electrodes, e.g. electrode catheters for electrophysiological study [EPS]
• CPC Class Codes: A61B 5/316 Modalities, i.e. specific diagnostic methods
• CPC Class Codes: A61B 5/339 Displays specially adapted therefor
• CPC Class Codes: A61B 5/35 by template matching
• CPC Class Codes: A61B 5/361 Detecting fibrillation
• CPC Class Codes: A61B 5/363 Detecting tachycardia or bradycardia
• CPC Class Codes: A61B 5/6858 with a distal basket, e.g. expandable basket
• 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 5/743 Displaying an image simultaneously with additional graphical information, e.g. symbols, charts, function plots

Abstract

Medical devices and methods for making and using medical devices are disclosed. An example medical device may include a system for mapping the electrical activity of the heart. The system may include a catheter shaft with a plurality of electrodes. The system may also include a processor. The processor may be capable of collecting a set of signals from at least one of the plurality of electrodes. The set of signals may be collected over a time period. The processor may also be capable of calculating at least one propagation vector from the set of signals, generating a data set from the at least one propagation vector, generating a statistical distribution of the data set and generating a visual representation of the statistical distribution.

Claims

1. A method for mapping the electrical activity of a targeted tissue region in a body, the method comprising:

receiving a data set corresponding to a plurality of cardiac signals sensed over a period of time;

determining propagation angles for the plurality of signals using the data set, the propagation angles including at least one propagation angle exceeding a threshold level of randomness; and

generating a propagation map including a visual indicator corresponding to the at least one propagation angle exceeding the threshold level of randomness.

2. The method of claim 1, further comprising determining vector magnitudes for the plurality of signals.

3. The method of claim 1, further comprising generating a diagnostic image including a visual representation, wherein the visual representation includes displaying one or more of a histogram, a circular histogram, a two-dimensional grid, a three-dimensional model, a three-dimensional surface and a propagation vector.

4. The method of claim 1, further comprising performing a statistical analysis on the propagation angles.

5. The method of claim 4, wherein performing the statistical analysis comprises determining statistical distributions of the propagation angles and the method further comprising comparing the statistical distributions.

6. The method of claim 5, further comprising characterizing a shape of the statistical distributions by performing algorithmic computations.

7. The method of claim 6, wherein performing an algorithmic computation comprises calculating at least one of the following:

standard deviation, entropy, kurtosis, skewness or the circular average of the data set.

8. The method of claim 1, further comprising generating at least one representative metric and wherein the at least one representative metric correlates to the propagation angles.

9. The method of claim 8, further comprising correlating the at least one representative metric to a metric indicator.

10. The method of claim 9, wherein the metric indicator comprises at least one of a representative:

number, color, texture, shape, and other visual indicator that can be easily visualized on a display.

11. A non-transitory computer-readable medium comprising executable instructions that, when executed by one or more processors, cause the one or more processors to:

receive a data set corresponding to a plurality of cardiac signals sensed by a plurality of electrodes over a period of time;

determine propagation angles for the plurality of signals;

determine when at least one propagation angle of the propagation angles exceeds a threshold level of randomness; and

generate a propagation map including a visual indicator corresponding to the at least one propagation angle.

12. The non-transitory computer-readable medium of claim 11, further comprising executable instructions that when executed by one or more processors cause the one or more processors to determine vector magnitudes for the plurality of signals.

13. The non-transitory computer-readable medium of claim 11, further comprising executable instructions that when executed by one or more processors cause the one or more processors to generate a diagnostic image including a visual representation, wherein the visual representation includes displaying one or more of a histogram, a circular histogram, a two-dimensional grid, a three-dimensional model, a three-dimensional surface and a propagation vector.

14. The non-transitory computer-readable medium of claim 11, further comprising executable instructions that when executed by one or more processors cause the one or more processors to perform a statistical analysis on the propagation angles.

15. The non-transitory computer-readable medium of claim 14, wherein performing the statistical analysis comprises determining statistical distributions of the propagation angles and the method further comprising comparing the statistical distributions.

16. The non-transitory computer-readable medium of claim 15, further comprising executable instructions that when executed by one or more processors cause the one or more processors to characterize a shape of the statistical distributions by performing algorithmic computations.

17. The non-transitory computer-readable medium of claim 16, wherein performing an algorithmic computation comprises calculating at least one of the following:

standard deviation, entropy, kurtosis, skewness or the circular average of the data set.

18. The non-transitory computer-readable medium of claim 11, further comprising executable instructions that when executed by one or more processors cause the one or more processors to generate at least one representative metric and wherein the at least one representative metric correlates to the propagation angles.

19. The non-transitory computer-readable medium of claim 18, further comprising executable instructions that when executed by one or more processors cause the one or more processors to correlate the at least one representative metric to a metric indicator.

20. The non-transitory computer-readable medium of claim 19, wherein the metric indicator comprises at least one of a representative:

number, color, texture, shape, and other visual indicator that can be easily visualized on a display.