Systems, devices, and methods for detecting occlusions in a biological subject including spectral learning

1. A non-invasive monitoring method, comprising:

non-invasively obtaining, via a non-invasive monitoring system configured to removably attach to an outer biological surface of a biological subject under test and operable to deliver an ex vivo generated interrogation stimulus to a region including a body fluid vessel of the biological subject, a first spectral information associated with an embolus, an occlusion, or a blood clot formation within the body fluid vessel of the biological subject while varying at least one of a wavelength or a frequency associated with the ex vivo generated interrogation stimulus;

partitioning the first spectral information into one or more information subsets according to one or more spectral learning protocols;

comparing at least one parameter associated with a second spectral information from the biological subject to at least one parameter associated with at least one of the one or more information subsets; and

generating a response indicative of at least one physical parameter associated with the embolus, the occlusion, or the blood clot formation within the body fluid vessel based on the comparison of the at least one parameter associated with the second spectral information to the at least one parameter associated with at least one of the one or more information subsets.

2. The non-invasive monitoring method of claim 1 , wherein partitioning the first spectral information into the one or more information subsets includes grouping the first spectral information into one or more information subsets using a clustering protocol.

3. The non-invasive monitoring method of claim 1 , wherein partitioning the first spectral information into the one or more information subsets includes grouping thee first spectral information into one or more information subsets using at least one of a Spectral Clustering protocol or a Spectral Learning protocol.

4. The non-invasive monitoring method of claim 1 , wherein partitioning the first spectral information into the one or more information subsets includes grouping the first spectral information into one or more information subsets using at least one of a Fuzzy C-Means Clustering protocol, a Graph-Theoretic protocol, a Hierarchical Clustering protocol, a K-Means Clustering protocol, a Locality-Sensitive Hashing protocol, a Mixture of Gaussians protocol, a Model-Based Clustering protocol, a Cluster-Weighted Modeling protocol, an Expectations-Maximization protocol, a Principal Components Analysis protocol, or a Partitional protocol.

5. The non-invasive monitoring method of claim 1 , wherein partitioning the first spectral information into the one or more information subsets includes partitioning a detected spectrum into one or more information subsets with at least one of a prism, a monochromator, a diffraction grating, or a bypass filter.

6. The non-invasive monitoring method of claim 1 , wherein generating the response based on the comparison includes generating the response based on the comparison of the at least one parameter associated with the second spectral information to a threshold diameter of at least one cluster associated with a set of reference cluster information associated with the biological subject.

7. The non-invasive monitoring method of claim 1 , wherein generating the response based on the comparison includes generating the response based on the comparison of the second spectral information to an inverse of a distance to at least one cluster centroid associated with a reference information data.

8. The non-invasive monitoring method of claim 1 , wherein generating the response includes generating an occlusion aggregation rate.

9. The non-invasive monitoring method of claim 1 , wherein generating the response includes generating the response based on a voxel intensity.

10. The non-invasive monitoring method of claim 1 , wherein generating the response includes generating an occlusion aggregation rate based on a voxel intensity.

11. The non-invasive monitoring method of claim 1 , wherein generating the response includes generating at least one parameter associated with a degree of belonging to at least one cluster centroid associated with a reference information data.

12. The non-invasive monitoring method of claim 1 , wherein generating the response includes generating a rate of deviation from a threshold value.

13. The non-invasive monitoring method of claim 1 , further comprising:

performing a real-time update of at least one parameter associated with a spectral blood clotting model associated with the biological subject.

14. A non-invasive occlusion monitoring method, comprising:

obtaining spectral information from a biological subject, via a non-invasive monitoring system configured to removably attach to an outer biological surface of the biological subject under test and operable to deliver an ex vivo generated interrogation stimulus to a region including a body fluid vessel of the biological subject, while varying at least one of a wavelength or a frequency associated with an interrogation optical excitation energy source;

comparing at least one parameter associated with the obtained spectral information to one or more information subsets derived from partitioning spectral information associated with the biological subject;

generating a response indicative of at least one physical parameter associated with an embolus, an occlusion, or a blood clot formation within a body fluid vessel based on the comparison of the at least one parameter associated with the obtained spectral information to the one or more information subsets derived from partitioning spectral information associated with the biological subject, and

performing a real-time update of at least one parameter associated with a spectral blood clotting model associated with the biological subject under test.

15. The method of claim 14 , wherein obtaining the spectral information includes concurrently detecting an excitation radiation and an emission radiation to generate a spectrum.

16. The method of claim 14 , wherein comparing the at least one parameter associated with the obtained spectral information to the one or more information subsets derived from partitioning the spectral information includes comparing the at least one parameter associated with the obtained spectral information to one or more information subsets derived from grouping the spectral information into one or more information subsets using at least one of a Spectral Clustering protocol or a Spectral Learning protocol.

17. The method of claim 14 , wherein comparing the at least one parameter associated with the obtained spectral information to the one or more information subsets derived from partitioning spectral information includes comparing the at least one parameter associated with the obtained spectral information to one or more information subsets derived from grouping the spectral information into one or more information subsets using at least one of a Fuzzy C-Means Clustering protocol, a Graph-Theoretic protocol, a Hierarchical Clustering protocol, a K-Means Clustering protocol, a Locality-Sensitive Hashing protocol, a Mixture of Gaussians protocol, a Model-Based Clustering protocol, a Partitional protocol, a Spectral Clustering protocol, a Cluster-Weighted Modeling protocol, an Expectations-Maximization protocol, a Principal Components Analysis protocol, or a Spectral Learning protocol.

18. The method of claim 14 , wherein generating the response includes generating at least one of information associated with a statistical probability, a local cluster density, a deviation from a target cluster distance, a distance from a cluster centroid, an Euclidean distance, or a probability density associated with the at least one physical parameter associated with the embolus, the occlusion, or the blood clot formation within the body fluid vessel.

19. The method of claim 14 , wherein generating the response includes automatically updating at least one parameter associated with a spectral tissue model.