Estimating the prevalence of activation patterns in data segments during electrophysiology mapping

Patent metadata

Field	Value
Publication number	US-9737227-B2
Application number	US-201414471477-A
Country	US
Kind code	B2
Filing date	Aug 28, 2014
Priority date	Aug 28, 2013
Publication date	Aug 22, 2017
Grant date	Aug 22, 2017

Abstract

A system and method for mapping an anatomical structure includes sensing activation signals of physiological activity with a plurality of mapping electrodes disposed in or near the anatomical structure. Patterns among the sensed activation signals are identified based on a similarity measure generated between each unique pair of identified patterns which are classified into groups based on a correlation between the corresponding pairs of similarity measures. A characteristic representation is determined for each group of similarity measures and displayed as a summary plot of the characteristic representations.

First claim

We claim:

1. A method for diagnosing and treating pathologies in a heart, the method comprising:
using a system including a mapping probe and a processing system for:
sensing activation signals of physiological activity in the heart with the mapping probe including a plurality of mapping electrodes disposed in the heart, each of the plurality of mapping electrodes having an electrode location;
identifying patterns among the sensed activation signals representing activation propagation;
generating a similarity measure between each unique pair of identified patterns;
classifying the patterns into groups based on the similarity measure;
determining a plurality of characteristic representations, each of the plurality of characteristic representations corresponding to a unique one of the groups and comprising a single numerical representation that summarizes the patterns in the corresponding group; and
displaying at least one of the plurality of characteristic representations to aid in visualization;

identifying at least one site in the heart having a pathology based on the at least one of the plurality of characteristic representations displayed; and
treating myocardial tissue at or near the at least one site in the heart to treat the pathology.

2. The method according to claim 1 , wherein:
displaying the at least one of the plurality of characteristic representations comprises displaying, for each group, a characteristic pattern corresponding to the group and prevalence information associated with the characteristic pattern.

3. The method according to claim 2 , wherein the characteristic representation includes at least one of a mean, variance, covariance, standard deviation, median, and prevalence.

4. The method according to claim 1 , wherein identifying patterns further includes generating a pattern map for each sensed activation signal, each pattern map having at least one of a vector field map that represents a direction and magnitude of activation signal propagation, a voltage propagation map that represents a direction and magnitude of voltage propagation, a phase propagation map that represents a direction and magnitude of phase propagation, and an action potential duration map that represents a duration of an action potential.

5. The method according to claim 1 , wherein the patterns classified into groups are compared with at least one pattern template for each of the groups.

6. The method of claim 1 , wherein identifying patterns further includes:
identifying unclassifiable patterns that are not classifiable into any groups of similar patterns; and
determining a measure of randomness based on the unclassifiable patterns.

7. The method according to claim 1 , wherein generating the similarity measure further includes generating a similarity matrix including the patterns, each entry of the similarity matrix representing the similarity measure for each unique pair of identified patterns generated based on a correlation of the corresponding patterns.

8. The method according to claim 1 , wherein classifying the patterns further includes:
determining a correlation coefficient for each unique pair of patterns; and
classifying the patterns into distinct groups based on a percentage of patterns among each group having a particular correlation coefficient.

9. A method for diagnosing and treating pathologies in a heart, comprising:
using a system including a mapping probe and a processing system for:
sensing activation signals of cardiac activity with the mapping probe including a plurality of mapping electrodes disposed in the heart, each of the plurality of mapping electrodes having an electrode location;
identifying patterns among the sensed activation signals;
generating a similarity measure between each of unique pairs of identified patterns;
classifying the patterns into groups based on the similarity measure;
determining a characteristic representation for each group of the groups, wherein each characteristic representation comprises a single numerical representation that summarizes the patterns in the corresponding group; and
displaying at least one characteristic representation determined for the groups to aid in visualization;

identifying at least one site in the heart having a pathology based on the at least one of the plurality of characteristic representations displayed; and
treating myocardial tissue at or near the at least one site in the heart to treat the pathology.

10. The method according to claim 9 , wherein the characteristic representation includes at least one of a mean, variance, covariance, standard deviation, median, and a prevalence of the pattern.

11. The method according to claim 9 , further comprising generating a plurality of pattern maps for each activation signal, each pattern map having at least one of a vector field map which represents a direction and a magnitude of an activation signal propagation, a voltage propagation map which representation a direction and a magnitude of voltage propagation, a phase propagation map which represents a direction and a magnitude of phase propagation, and an action potential duration map which represents a duration of an action potential.

12. The method according to claim 11 , wherein generating the plurality of pattern maps further includes:
identifying unclassifiable pattern maps that are not classifiable into any groups of similar patterns; and
determining a measure of randomness based on the unclassifiable pattern maps.

13. The method according to claim 9 , wherein generating the similarity measure further comprises generating a similarity matrix including the patterns, each entry of the similarity matrix representing the similarity measure for each unique pair of identified patterns generated based on a correlation of the corresponding patterns.

14. The method according to claim 9 , wherein classifying the patterns further comprises:
determining a correlation coefficient for each unique pair of patterns; and
classifying the patterns into distinct groups based on a percentage of patterns among each group having a particular correlation coefficient.

Assignees

• Boston Scient Scimed Inc

Inventors

• Thakur Pramodsingh Hirasingh
• Shome Shibaji
• Shuros Allan C
• Arcot-Krishnamurthy Shantha
• Maskara Barun
• Saha Sunipa

Classifications

• A61B5/6858

  with a distal basket, e.g. expandable basket · CPC title

• G16H50/20

  for computer-aided diagnosis, e.g. based on medical expert systems · CPC title

• A61B5/743 Primary

  Displaying an image simultaneously with additional graphical information, e.g. symbols, charts, function plots · CPC title

• G06N5/047

  Pattern matching networks; Rete networks · CPC title

• A61B5/367

  Electrophysiological study [EPS], e.g. electrical activation mapping or electro-anatomical mapping · CPC title