Methods and systems for automatically identifying whether a neural recording signal includes a neural response signal

• US 7,818,052 B2
• Filed: 06/01/2005
• Issued: 10/19/2010
• Est. Priority Date: 06/01/2005
• Status: Expired due to Fees

• Current Assignee: Advanced Bionics LLC (Sonova Holding AG)
• Original Assignee: Advanced Bionics LLC (Sonova Holding AG)
• Inventors: Litvak, Leonid M., Overstreet, Edward H.
• Primary Examiner(s): Mallari; Patricia C
• Assistant Examiner(s): JANG, CHRISTIAN YONGKYUN
• Application Number:
  US11/142,152
• Publication Number:
  US 20060287609A1
• Time in Patent Office: 1,966 Days
• Field of Search: 600/544, 600/545, 600/559, 607/2, 607/56, 607/117
• US Class Current: 600/544
• 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 5/7217 of noise originating from a therapeutic or surgical apparatus, e.g. from a pacemaker
• CPC Class Codes: A61N 1/36039 fitting procedures

Abstract

Methods of automatically identifying whether a neural recording signal includes a neural response signal include fitting an artifact model to a neural recording signal to produce a fitted artifact model signal, determining a strength-of-response metric that describes a distance of the neural recording signal from the fitted artifact model signal, and identifying the neural recording signal as including a neural response signal if the strength-of-response metric is above a pre-determined threshold. Corresponding systems are also described.

Claims

1. A method of automatically identifying whether a neural recording signal includes a neural response signal, said method comprising:

providing an artifact model that describes a model stimulus artifact signal that is correlated with a stimulus used to evoke said neural recording signal, said artifact model being represented by a plurality of model parameters;

fitting, by a computer, said artifact model to a neural recording signal to produce a fitted artifact model signal, said fitting comprising determining values for said model parameters that result in a minimized error between the artifact model and said neural recording signal;

determining, by the computer, a strength-of-response (SOR) metric that describes a distance of said neural recording signal from said fitted artifact model signal; and

identifying, by the computer, said neural recording signal as including a neural response signal if said strength-of-response metric is above a pre-determined threshold.

2. The method of claim 1, further comprising identifying said neural recording signal as including only noise and/or stimulus artifact if said strength-of-response metric is below said pre-determined threshold.

3. The method of claim 1, further comprising averaging additional neural recording signals if said strength-of-response metric is within a pre-determined distance from said pre-determined threshold.

4. The method of claim 1, further comprising conditioning said neural response signal and estimating an amount of noise in said neural recording signal prior to fitting said artifact model to said neural recording signal.

5. The method of claim 1, further comprising denoising said neural recording signal and said fitted artifact model signal prior to determining said strength-of-response metric.

6. The method of claim 5, wherein said step of denoising said neural recording signal and said fitted artifact model signal comprises using principal component analysis.

7. The method of claim 1, further comprising:

determining a net confidence interval corresponding to said fitted artifact model signal and said neural recording signal; and

using said net confidence interval to determine the strength-of-response metric.

8. The method of claim 1, wherein said strength-of-response metric is determined by evaluating

9. The method of claim 1, wherein the artifact model is represented by a_m(t), wherein a_m(t) is equal to A₁*e⁻

α

t+A₂*t+A₃, and wherein α

, A₁, A₂, and A₃ comprise the plurality of model parameters.

10. A method of automatically identifying whether a neural recording signal includes a neural response signal, said method comprising:

fitting, by a computer, an artifact model to a neural recording signal to produce a fitted artifact model signal;

determining, by the computer, a strength-of-response (SOR) metric that describes a distance of said neural recording signal from said fitted artifact model signal; and

identifying, by the computer, said neural recording signal as including a neural response signal if said strength-of-response metric is above a pre-determined threshold;

wherein said strength-of-response metric is determined by evaluating

11. A system for automatically identifying whether a neural recording signal includes a neural response signal, said system comprising one or more devices configured to:

provide an artifact model that describes a model stimulus artifact signal that is correlated with a stimulus used to evoke said neural recording signal, said artifact model being represented by a plurality of model parameters;

fit said artifact model to a neural recording signal to produce a fitted artifact model signal by determining values for said model parameters that result in a minimized error between said artifact model and said neural recording signal;

determine a strength-of-response (SOR) metric that describes a distance of said neural recording signal from said fitted artifact model signal; and

automatically identify said neural recording signal as including a neural response signal if said strength-of-response metric is above a pre-determined threshold.

12. The system of claim 11, wherein said one or more devices comprises at least one or more of a computer, digital signal processor, and software application.

13. The system of claim 11, wherein said one or more devices are further configured to identify said neural recording signal as including only noise and/or stimulus artifact if said strength-of-response metric is below said pre-determined threshold.

14. The system of claim 11, wherein said one or more devices are further configured to average additional neural recording signals if said strength-of-response metric is within a pre-determined distance from said pre-determined threshold.

15. The system of claim 11, wherein said one or more devices are further configured to:

determine a net confidence interval corresponding to said fitted artifact model signal and said neural recording signal; and

use said net confidence interval to determine the strength-of-response metric.

16. A system for automatically identifying whether a neural recording signal includes a neural response signal, said system comprising:

means for fitting an artifact model to a neural recording signal to produce a fitted artifact model signal;

means for determining a strength-of-response (SOR) metric that describes a distance of said neural recording signal from said fitted artifact model signal; and

means for identifying said neural recording signal as including a neural response signal if said strength-of-response metric is above a pre-determined threshold;

wherein said strength-of-response metric is determined by evaluating

17. The system of claim 16, further comprising means for conditioning said neural response signal and means for estimating an amount of noise in said neural recording signal prior to fitting said artifact model to said neural recording signal.

18. The system of claim 16, further comprising means for denoising said neural recording signal and said fitted artifact model signal prior to determining said strength-of-response metric.

19. The system of claim 16, further comprising means for identifying said neural recording signal as including only noise and/or stimulus artifact if said strength-of-response metric is below said pre-determined threshold.

20. The system of claim 16, further comprising means for averaging additional neural recording signals if said strength-of-response metric is within a pre-determined distance from said pre-determined threshold.