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

Abstract

Systems, devices, and methods are described for detecting an embolus, thrombus, or a deep vein thrombus in a biological subject.

1 .- 16 . (canceled) 
     
     
         17 . A computer program product, comprising:
 one or more signal-bearing media containing computer instructions which, when run on a computing device, cause the computing device to implement a method including   obtaining a first spectral information from a biological subject while varying at least one of a wavelength or a frequency associated with an interrogation optical excitation energy source;   partitioning the spectral information into one or more information subsets; and   comparing at least one parameter associated with a second spectral information from a biological subject associated to at least one parameter associated with at least one of the one or more information subsets.   
     
     
         18 . The computer program product of  claim 17 , wherein the one or more signal-bearing media containing computer instructions further comprises:
 computer instructions which, when run on a computing device, cause the computing device to   generating a response 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.   
     
     
         19 . The computer program product of  claim 17 , wherein partitioning the spectral information into the one or more information subsets includes grouping the spectral information into one or more information subsets using a clustering protocol. 
     
     
         20 . The computer program product of  claim 17 , wherein partitioning the spectral information into the one or more information subsets includes grouping the spectral information into one or more information subsets using at least one of a Spectral Clustering protocol or a Spectral Learning protocol. 
     
     
         21 . The computer program product of  claim 17 , wherein partitioning the spectral information into the one or more information subsets includes 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 Cluster-Weighted Modeling protocol, an Expectations-Maximization protocol, a Principal Components Analysis protocol, or a Partitional protocol 
     
     
         22 . The computer program product of  claim 17 , wherein the one or more signal-bearing media containing computer instructions further includes:
 computer instructions which, when run on a computing device, cause the computing device to   performing a real-time update of at least one parameter associated with a spectral blood vessel occlusion model associated with the biological subject.   
     
     
         23 . A computer program product, comprising:
 one or more signal-bearing media containing computer instructions which, when run on a computing device, cause the computing device to implement a method including   obtaining a first spectral information from a biological subject while varying at least one of a wavelength or a frequency associated with an interrogation optical excitation energy source;   partitioning the spectral information into one or more information subsets; and   comparing at least one parameter associated with a second spectral information from a biological subject to at least one parameter associated with at least one of the one or more information subsets.   
     
     
         24 . The computer program product of  claim 23 , wherein partitioning the spectral information into the one or more information subsets includes automatically generating one or more data clusters using at least one of a Spectral Clustering protocol or a Spectral Learning protocol. 
     
     
         25 . The computer program product of  claim 23 , wherein partitioning the spectral information into the one or more information subsets automatically generating one or more data clusters 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. 
     
     
         26 . The computer program product of  claim 23 , wherein the one or more signal-bearing media containing computer instructions further comprises:
 computer instructions which, when run on a computing device, cause the computing device to   generating a response based at least in part 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.   
     
     
         27 . The computer program product of  claim 23 , 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 euclidina distance, or a probability density. 
     
     
         28 . An apparatus, comprising:
 circuitry for obtaining spectral information from a biological subject while varying at least one of a wavelength or a frequency associated with an interrogation optical excitation energy source; and   circuitry for partitional clustering the obtained spectral information into one or more information subsets.   
     
     
         29 . The apparatus of  claim 28 , further comprising:
 circuitry for real-time comparing at least one parameter associated with an obtained spectral information from a biological subject to at least one parameter associated with at least one of the one or more information subsets.   
     
     
         30 .- 36 . (canceled)