Method for denoising and data fusion of biophysiological rate features into a single rate estimate

Abstract

A computer-implemented method for analyzing biophysiological periodic data includes receiving a stream of feature data points, determining whether each of the feature data points lies within or outside a predetermined limit, and eliminating a first subset of the feature data points in response to having determined that the each of the data points in the first subset lies outside the predetermined limit. The method further includes extracting a feature from the feature data points that lie within the predetermined limit over a time window, performing multiple hypothesis tests to determine whether or not the feature corresponds to a any of multiple hypothesis distributions, and qualifying the feature as a qualified estimate of an actual feature if the feature corresponds to statistical mean of a plurality of recent qualified estimates.

What is claimed is: 
     
         1 . A device, comprising:
 a memory that stores machine instructions; and   a processor coupled to the memory that executes the machine instructions to receive a plurality of feature data points,   extract a feature from a feature data point of the plurality of feature data points that satisfy a predetermined range,   perform a plurality of hypothesis tests to determine whether the feature corresponds to each of a plurality of predetermined hypothesis distributions comprising a first hypothesis distribution, and   qualify the feature as a qualified estimate of an actual feature if the feature corresponds to the first hypothesis distribution.   
     
     
         2 . The device of  claim 1 , wherein the processor further executes the machine instructions to determine a rate of change associated with a first subset of the feature data points, and eliminate at least one of the feature data points in response to having determined that the rate of change is outside a predetermined rate limit. 
     
     
         3 . The device of  claim 2 , wherein the predetermined rate limit comprises a confidence interval based on a second subset of the feature data points that precedes the first subset in time. 
     
     
         4 . The device of  claim 1 , wherein the processor further executes the machine instructions to modify a first subset of the feature data points to create a filtered subset of feature data points, determine a rate of change associated with the filtered subset, and eliminate at least one of the feature data points in response to having determined that the rate of change is outside a predetermined rate limit. 
     
     
         5 . The device of  claim 4 , wherein the processor further executes the machine instructions to implement an unscented Kalman filter to create the filtered subset of feature data points. 
     
     
         6 . The device of  claim 1 , wherein the first hypothesis distribution represents a statistical mean of a plurality of recent qualified estimates. 
     
     
         7 . The device of  claim 1 , wherein the processor further executes the machine instructions to modify the feature based on the feature corresponding to a second hypothesis distribution, and qualify the modified feature as a qualified estimate of an actual feature based on the feature corresponding to the first hypothesis distribution, wherein the plurality of hypothesis distributions further comprise the second hypothesis distribution. 
     
     
         8 . The device of  claim 7 , wherein the second hypothesis distribution represents half of a statistical mean of a plurality of recent qualified estimates. 
     
     
         9 . The device of  claim 1 , wherein the processor further executes the machine instructions to reject the feature if the feature corresponds to a third hypothesis distribution, wherein the plurality of hypothesis distributions further comprise the third hypothesis distribution, which represents an artifact that is not correlated with the actual feature. 
     
     
         10 . A method, comprising:
 receiving a plurality of feature data points;   extracting a feature from a feature data point of the plurality of feature data points that satisfy a predetermined range;   performing a plurality of hypothesis tests to determine whether or not the feature corresponds to each of a plurality of predetermined hypothesis distributions comprising a first hypothesis distribution; and   qualifying the feature as a qualified estimate of an actual feature if the feature corresponds to the first hypothesis distribution.   
     
     
         11 . The method of  claim 10 , further comprising:
 determining a rate of change associated with a first subset of the feature data points; and   eliminating at least one of the feature data points in response to having determined that the rate of change is outside a predetermined rate limit.   
     
     
         12 . The method of  claim 11 , wherein the predetermined rate limit comprises a confidence interval based on a second subset of the feature data points that precedes the first subset in time. 
     
     
         13 . The method of  claim 10 , further comprising:
 applying a filter to a first subset of the feature data points to create a filtered subset of feature data points;   determining a rate of change associated with the filtered subset; and   eliminating at least one of the feature data points in response to having determined that the rate of change is outside a predetermined rate limit.   
     
     
         14 . The method of  claim 13 , wherein the filter comprises an unscented Kalman filter. 
     
     
         15 . The method of  claim 10 , wherein the first hypothesis distribution represents a statistical mean of a plurality of recent qualified estimates. 
     
     
         16 . The method of  claim 10 , further comprising:
 modifying the feature if the feature corresponds to a second hypothesis distribution; and   qualifying the modified feature as a qualified estimate of an actual feature if the feature corresponds to the first hypothesis distribution, wherein the plurality of hypothesis distributions further comprise the second hypothesis distribution.   
     
     
         17 . The method of  claim 14 , wherein the second hypothesis distribution represents half of a statistical mean of a plurality of recent qualified estimates. 
     
     
         18 . The method of  claim 10 , further comprising rejecting the feature if the feature corresponds to a third hypothesis distribution, wherein the plurality of hypothesis distributions further comprise the third hypothesis distribution. 
     
     
         19 . The method of  claim 16 , wherein the third hypothesis distribution represents an artifact that is not correlated with the actual feature. 
     
     
         20 . A computer program product, comprising:
 a non-transitory, computer-readable storage medium encoded with instructions adapted to be executed by a processor to implement:   receiving a plurality of feature data points;   extracting a feature from a feature data point of the plurality of feature data points that satisfy a predetermined range;   performing a plurality of hypothesis tests to determine whether or not the feature corresponds to each of a plurality of predetermined hypothesis distributions comprising a first hypothesis distribution; and   qualifying the feature as a qualified estimate of an actual feature if the feature corresponds to the first hypothesis distribution.   
     
     
         21 . The computer program product of  claim 20 , wherein the instructions are further adapted to implement:
 determining a first rate of change associated with a first subset of the feature data points;   eliminating at least one of the feature data points in response to having determined that the first rate of change is outside a first predetermined rate limit;   applying a filter to a second subset of the feature data points to create a filtered subset of feature data points;   determining a second rate of change associated with the filtered subset; and   eliminating at least one of the feature data points in response to having determined that the second rate of change is outside a second predetermined rate limit.