Methods and systems for automatically determining a neural response threshold current level

1. A method of automatically determining a neural response threshold current level, said method comprising:

obtaining a plurality of neural recording signals at a plurality of different stimulation current levels;

fitting an artifact model to each of said neural recording signals to obtain a plurality of fitted artifact model signals;

denoising each of said neural recording signals and each of said fitted artifact model signals;

computing a strength-of-response metric for each of said neural recording signals, said computation being based on at least one of said denoised neural recording signals and at least one of said fitted artifact model signals;

comparing each of said strength-of-response metrics to a strength-of-response threshold to identify one or more neural response signals and one or more non-response signals contained within said neural recording signals; and

analyzing a trend between said neural response signals and said non-response signals to determine said neural response threshold current level.

2. The method of claim 1 , wherein each of said neural response signals and said non-response signals corresponds to one of said stimulation current levels and wherein said neural response threshold current level is substantially equal to a current level located in between a highest current level out of said stimulation current levels corresponding to said non-response signals and a lowest current level out of said stimulation current levels corresponding to said neural response signals.

3. The method of claim 1 , further comprising identifying at least four neural response signals contained within said neural recording signals.

4. The method of claim 1 , further comprising identifying at least two non-response signals within said neural recording signals.

5. The method of claim 1 , wherein a highest stimulation current level out of said stimulation current levels corresponding to said neural response signals is less than or substantially equal to a maximum allowable current level.

6. The method of claim 1 , wherein said step of analyzing said trend between said neural response signals and said non-response signals comprises generating and analyzing a growth curve corresponding to amplitudes of said neural response signals and said non-response signals.

7. The method of claim 1 , further comprising:

computing a net confidence interval for each of said neural recording signals, said computation based on said denoised neural recording signals and said denoised fitted artifact model signals; and

basing said computation of said strength-of-response metric on said net confidence intervals.

8. A system for automatically determining a neural response threshold current level, said system comprising:

a stimulator configured to obtain a plurality of neural recording signals at a plurality of different stimulation current levels; and

one or more devices communicatively coupled to said stimulator and configured to

fit an artifact model to each of said neural recording signals to obtain a plurality of fitted artifact model signals;

denoise each of said neural recording signals and each of said fitted artifact model signals;

compute a strength-of-response metric for each of said neural recording signals, said computation being based on at least one of said denoised neural recording signals and at least one of said fitted artifact model signals;

compare each of said strength-of-response metrics to a strength-of-response threshold to identify one or more neural response signals and one or more non-response signals contained within said neural recording signals; and

analyze a trend between said neural response signals and said non-response signals to determine said neural response threshold current level.

9. The system of claim 8 , wherein each of said neural response signals and said non-response signals corresponds to one of said stimulation current levels and wherein said neural response threshold current level is substantially equal to a current level located in between a highest current level out of said stimulation current levels corresponding to said non-response signals and a lowest current level out of said stimulation current levels corresponding to said neural response signals.

10. The system of claim 8 , wherein said one or more devices are further configured to identify at least four neural response signals contained within said neural recording signals.

11. The system of claim 8 , wherein said one or more devices are further configured to identify at least two non-response signals within said neural recording signals.

12. The system of claim 8 , wherein a highest stimulation current level out of said stimulation current levels corresponding to said neural response signals is less than or substantially equal to a maximum allowable current level.

13. The system of claim 8 , wherein said one or more devices are further configured to generate and analyze a growth curve corresponding to amplitudes of said neural response signals and said non-response signals to determine said neural response threshold current.

14. The system of claim 8 , wherein said one or more devices comprises at least one or more of a computer, digital signal processor, and software application.

15. The system of claim 8 , wherein said one or more devices are further configured to:

compute a net confidence interval for each of said neural recording signals, said computation based on said denoised neural recording signals and said denoised fitted artifact model signals; and

base said computation of said strength-of-response metric on said net confidence intervals.

16. A system for automatically determining a neural response threshold current level, said system comprising:

means for obtaining a plurality of neural recording signals at a plurality of different stimulation current levels;

means for fitting an artifact model to each of said neural recording signals to obtain a plurality of fitted artifact model signals;

means for denoising each of said neural recording signals and each of said fitted artifact model signals;

means for computing a strength-of-response metric for each of said neural recording signals, said computation being based on at least one of said denoised neural recording signals and at least one of said fitted artifact model signals;

means for comparing each of said strength-of-response metrics to a strength-of-response threshold to identify one or more neural response signals and one or more non-response signals contained within said neural recording signals; and

means for analyzing a trend between said neural response signals and said non-response signals to determine said neural response threshold current level.

17. The system of claim 16 , wherein each of said neural response signals and said non-response signals corresponds to one of said stimulation current levels and wherein said neural response threshold current level is substantially equal to a current level located in between a highest current level out of said stimulation current levels corresponding to said non-response signals and a lowest current level out of said stimulation current levels corresponding to said neural response signals.

18. The system of claim 16 , wherein a highest stimulation current level out of said stimulation current levels corresponding to said neural response signals is less than or substantially equal to a maximum allowable current level.

19. The system of claim 16 , wherein said means for analyzing said trend between said neural response signals and said non-response signals comprises means for generating and analyzing a growth curve corresponding to amplitudes of said neural response signals and said non-response signals.

20. The system of claim 16 , further comprising:

means for computing a net confidence interval for each of said neural recording signals, said computation based on said denoised neural recording signals and said denoised fitted artifact model signals; and

means for basing said computation of said strength-of-response metric on said net confidence intervals.