Detecting Neuronal Action Potentials Using a Convolutive Compound Action Potential Model

• US 20150173637A1
• Filed: 12/19/2014
• Published: 06/25/2015
• Est. Priority Date: 12/20/2013
• Status: Abandoned Application

• Current Assignee: Med El Elektromedizinische Geraete GmbH
• Original Assignee: Med El Elektromedizinische Geraete GmbH
• Inventors: Strahl, Stefan, Schwarz, Konrad, Spitzer, Philipp, Dierker, Angelika
• Application Number:
  US14/576,804
• Publication Number:
  US 20150173637A1
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: A61B 5/0031 Implanted circuitry
• CPC Class Codes: A61B 5/125 objective methods
• CPC Class Codes: A61B 5/24 Detecting, measuring or recording bioelectric or biomagnetic signals of the body or parts thereof
• CPC Class Codes: A61B 5/4041 Evaluating nerves condition
• CPC Class Codes: A61B 5/6817 Ear canal
• CPC Class Codes: A61B 5/7257 using Fourier transforms
• CPC Class Codes: A61N 1/0541 Cochlear electrodes
• CPC Class Codes: A61N 1/36039 fitting procedures
• CPC Class Codes: G06N 7/01 Probabilistic graphical models, e.g. probabilistic networks

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.

Claims

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.