Systems and methods for identifying ablation locations using principal component analysis

1. A computer-implemented method for identifying potential ablation sites using principal component analysis (PCA), the method comprising:

generating, using a computing device, a dataset for analysis, the dataset including a plurality of variables associated with a three-dimensional geometry that includes a plurality of vertices;

performing, using the computing device, PCA on the generated dataset to identify a plurality of principal components and to generate, for each vertex of the plurality of vertices, a metric associated with each of the plurality of principal components; and

transposing, using the computing device, the metrics for each vertex onto the three-dimensional geometry, wherein the transposed metrics facilitate identifying potential ablation sites.

2. The method of claim 1 , wherein generating a dataset comprises generating a dataset including raw variables and processed variables.

3. The method of claim 2 , further comprising calculating the processed variables for each vertex based on neighboring vertices located within a predetermined radius of the vertex.

4. The method of claim 1 , further comprising normalizing the dataset prior to performing PCA.

5. The method of claim 1 , wherein performing PCA comprises calculating a loading for each of the plurality of variables for each of the plurality of principal components.

6. The method of claim 1 , further comprising displaying, using the computing device, the three-dimensional geometry including the transposed metrics.

7. The method of claim 1 , wherein at least some of the plurality of variables are associated with perfusion.

8. A computing device for identifying potential ablation sites using principal component analysis (PCA), the computing device comprising:

a memory device; and

a processor communicatively coupled to the memory device, the processor configured to:

generate a dataset for analysis, the dataset including a plurality of variables associated with a three-dimensional geometry that includes a plurality of vertices;

perform PCA on the generated dataset to identify a plurality of principal components and to generate, for each vertex of the plurality of vertices, a metric associated with each of the plurality of principal components; and

transpose the metrics for each vertex onto the three-dimensional geometry, wherein the transposed metrics facilitate identifying potential ablation sites.

9. The computing device of claim 8 , wherein to generate a dataset, the processor is configured to generate a dataset including raw variables and processed variables.

10. The computing device of claim 9 , wherein the processor is further configured to calculate the processed variables for each vertex based on neighboring vertices located within a predetermined radius of the vertex.

11. The computing device of claim 8 , wherein the processor is further configured to normalize the dataset prior to performing PCA.

12. The computing device of claim 8 , wherein to perform PCA, the processor is configured to calculate a loading for each of the plurality of variables for each of the plurality of principal components.

13. The computing device of claim 8 , wherein the processor is further configured to display the three-dimensional geometry including the transposed metrics.

14. The computing device of claim 8 , wherein at least some of the plurality of variables are associated with perfusion.

15. Non-transitory computer-readable media having computer-executable instructions thereon, wherein when executed by a processor of a computing device, cause the processor of the computing device to:

generate a dataset for analysis, the dataset including a plurality of variables and associated with a three-dimensional geometry that includes a plurality of vertices;

perform PCA on the generated dataset to identify a plurality of principal components and to generate, for each vertex of the plurality of vertices, a metric associated with each of the plurality of principal components; and

transpose the metrics for each vertex onto the three-dimensional geometry, wherein the transposed metrics facilitate identifying potential ablation sites.

16. The non-transitory computer-readable media of claim 15 , wherein to generate a dataset, the processor is configured to generate a dataset including raw variables and processed variables.

17. The non-transitory computer-readable media of claim 16 , wherein the computer-executable instructions are configured to further cause the processor to calculate the processed variables for each vertex based on neighboring vertices located within a predetermined radius of the vertex.

18. The non-transitory computer-readable media of claim 15 , wherein the computer-executable instructions are configured to further cause the processor to normalize the dataset prior to performing PCA.

19. The non-transitory computer-readable media of claim 15 , wherein to perform PCA, the computer-executable instructions are configured to cause the processor to calculate a loading for each of the plurality of variables for each of the plurality of principal components.

20. The non-transitory computer-readable media of claim 15 , wherein the computer-executable instructions are configured to further cause the processor to display the three-dimensional geometry including the transposed metrics.