Method for automated detection of A-waves

Abstract

In one form of the present invention, there is provided a method for detecting an A-wave, the method comprising: applying a series of stimuli to a nerve; recording a series of late responses; creation of a feature space map from an ensemble of late responses; identification of clusters within the feature space that represent A-wave components; consolidation of A-wave components into a discrete collection of A-waves; removal of false positive A-waves; and extraction of A-wave characteristics. In another form of the present invention, there is provided a system for detecting an A-wave comprising: a stimulation electrode; a stimulation circuit connected to the stimulation electrode for applying a series of stimuli to a nerve; a detection electrode; a detection circuit connected to the detection electrode; and an analyzer connected to the detection electrode and adapted to detect an A-wave by: recording a series of late responses detected by the detection circuit; creation of a feature space map from an ensemble of late responses; identification of clusters within the feature space map that represent A-wave components; consolidation of A-wave components into a discrete collection of A-waves; removal of false positive A-waves; and extraction of A-wave characteristics.

1 . A method for detecting an A-wave, the method comprising:
 applying a series of stimuli to a nerve;   recording a series of late responses;   creation of a feature space map from an ensemble of late responses;   identification of clusters within the feature space that represent A-wave components;   consolidation of A-wave components into a discrete collection of A-waves;   removal of false positive A-waves; and   extraction of A-wave characteristics.   
     
     
         2 . A method according to  claim 1  wherein the feature space map is created from a translation of the late responses. 
     
     
         3 . A method according to  claim 2  wherein the translation comprises the first derivative of the late responses. 
     
     
         4 . A method according to  claim 2  wherein the translation comprises the second derivative of the late responses. 
     
     
         5 . A method according to  claim 2  wherein the translation comprises a linear translation of the late responses. 
     
     
         6 . A method according to  claim 2  wherein the translation comprises a non-linear translation of the late responses. 
     
     
         7 - 8 . (canceled) 
     
     
         9 . A method for detecting an A-wave, the method comprising:
 applying a series of stimuli to a nerve;   recording a series of evoked bioelectrical responses;   identifying one or more attributes in each of the responses;   utilizing each of the one or more attributes of each of the responses to create a new data set;   identifying trends in the new data set; and   analyzing the trends to identify the A-wave.   
     
     
         10 . A method according to  claim 9  wherein the one or more attributes comprise the amplitude of local maxima and/or local minima. 
     
     
         11 . A method according to  claim 9  wherein the one or more attributes comprise the absolute value amplitude. 
     
     
         12 . A method according to  claim 9  wherein the one or more attributes comprise the second derivate of amplitude. 
     
     
         13 . A method for diagnosing a disorder in a patient comprising:
 detecting an A-wave in a patient by:
 applying a series of stimuli to a nerve; 
 recording a series of late responses; 
 creation of a feature space map from an ensemble of late responses; 
 identification of clusters within the feature space map that represent A-wave components; 
 consolidation of A-wave components into a discrete collection of A-waves; 
 removal of false positive A-waves; and 
 extraction of A-wave characteristics; and 
   comparing the A-wave of the patient with the A-wave of known disorder.   
     
     
         14 . A system for detecting an A-wave comprising:
 a stimulation electrode;   a stimulation circuit connected to the stimulation electrode for applying a series of stimuli to a nerve;   a detection electrode;   a detection circuit connected to the detection electrode; and   an analyzer connected to the detection electrode and adapted to detect an A-wave by:
 recording a series of late responses detected by the detection circuit; 
 creation of a feature space map from an ensemble of late responses; 
 identification of clusters within the feature space map that represent A-wave components; 
 consolidation of A-wave components into a discrete collection of A-waves; 
 removal of false positive A-waves; and 
 extraction of A-wave characteristics.