Detecting Neuronal Action Potentials Using a Convolutive Compound Action Potential Model

Abstract

A system and method detect neuronal action potential signals from tissue responding to electrical stimulation signals. A compound discharge latency distribution (CDLD) of the neural tissue is derived by deconvolving a tissue response measurement signal taken responsive to electrical stimulation of the neural tissue by a stimulation electrode, with an elementary unit response signal representing voltage change at a measurement electrode due to the electrical stimulation. The CDLD is compared to known physiological data to detect an NAP signal from the tissue response measurement signal.

What is claimed is: 
     
         1 . A system for detecting a neuronal action potential (NAP) signal from electrically stimulated neural tissue, the system comprising:
 a physiological database containing physiological data characterizing neural tissue response to electrical stimulation; and   a response measurement module configured to:
 i. derive a compound discharge latency distribution (CDLD) of the neural tissue by deconvolving:
 (a) a tissue response measurement signal taken responsive to electrical stimulation of the neural tissue by a stimulation electrode, with 
 (b) an elementary unit response signal representing voltage change at a measurement electrode due to the electrical stimulation; 
 
 ii. compare the CDLD to physiological data from the physiological database to detect an NAP signal from the tissue response measurement signal; 
   
     
     
         2 . The system according to  claim 1 , wherein the physiological data is characterized by a plurality of Gaussian mixture models (GMMs). 
     
     
         3 . The system according to  claim 2 , wherein the response measurement module is configured to compare the CDLD to the GMM physiological data using a least mean square fitting. 
     
     
         4 . The system according to  claim 2 , wherein the plurality of GMMs are two-component GMMs. 
     
     
         5 . The system according to  claim 2 , wherein the plurality of GMMs include parameter distributions as a function of one or more of stimulation amplitude, inter-pulse interval during a recovery sequence, masker and stimulation level during a recovery sequence, stimulation pulse polarity, distant between a probe electrode and a masker electrode during a spread of excitation sequence, and medical device generation. 
     
     
         6 . The system according to  claim 2 , wherein the plurality of GMMs include parameter distributions trained online by an expert to reflect a patient deviant parameter space. 
     
     
         7 . The system according to  claim 1 , wherein response measurement module is configured to use one or more of scale, latency and variation to compare the CDLD to the physiological data. 
     
     
         8 . The system according to  claim 1 , wherein the response measurement module is configured for deconvolving using a fast-Fourier transform algorithm. 
     
     
         9 . The system according to  claim 1 , wherein the NAP signal is an electrically-evoked compound action potential (eCAP) signal. 
     
     
         10 . A method for detecting a neuronal action potential (NAP) signal from electrically stimulated neural tissue, the method comprising:
 deriving a compound discharge latency distribution (CDLD) of the neural tissue by deconvolving:
 i. a tissue response measurement signal taken responsive to electrical stimulation of the neural tissue by a stimulation electrode, with 
 ii. an elementary unit response signal representing voltage change at a measurement electrode due to the electrical stimulation; 
   comparing the CDLD to known physiological data to detect an NAP signal from the tissue response measurement signal.   
     
     
         11 . The method according to  claim 10 , wherein the known physiological data is characterized by a plurality of Gaussian mixture models (GMMs). 
     
     
         12 . The method according to  claim 10 , wherein comparing the CDLD to the GMM physiological data uses a least mean square fitting. 
     
     
         13 . The method according to  claim 10 , wherein the plurality of GMMs are two-component GMMs. 
     
     
         14 . The method according to  claim 10 , wherein the plurality of GMMs includes parameter distributions as a function of one or more of stimulation amplitude, inter-pulse interval during a recovery sequence, masker and stimulation level during a recovery sequence, stimulation pulse polarity, distant between a probe electrode and a masker electrode during a spread of excitation sequence, and medical device generation. 
     
     
         15 . The method according to  claim 10 , wherein the plurality of GMMs includes parameter distributions trained online by an expert to reflect a patient deviant parameter space. 
     
     
         16 . The method according to  claim 10 , wherein comparing the CDLD to the GMM physiological data includes comparing one or more of scale, latency and variation. 
     
     
         17 . The method according to  claim 10 , wherein the deconvolving uses a fast-Fourier transform algorithm. 
     
     
         18 . The method according to  claim 10 , wherein the NAP signal is an electrically-evoked compound action potential (eCAP) signal.