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

• US 20060287609A1
• Filed: 06/01/2005
• Published: 12/21/2006
• Est. Priority Date: 06/01/2005
• Status: Active Grant

• Current Assignee: Advanced Bionics LLC (Sonova Holding AG)
• Original Assignee: Advanced Bionics LLC (Sonova Holding AG)
• Inventors: Litvak, Leonid M., Overstreet, Edward H.
• Granted Patent: US 7,818,052 B2
• Time in Patent Office: Days
• US Class Current: 600/554
• 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 (SOR) 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. Systems for automatically identifying whether a neural recording signal includes a neural response signal include one or more devices configured to fit an artifact model to a neural recording signal to produce a fitted artifact model signal, determine a strength-of-response (SOR) metric that describes a distance of the neural recording signal from the fitted artifact model signal, and automatically identify the neural recording signal as including a neural response signal if said strength-of-response metric is above a pre-determined threshold.

Claims

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

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

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

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

5. 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.

6. 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.

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

8. The method of claim 1, further comprising:

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

wherein said strength-of-response metric describes a distance of said neural recording signal from said net confidence interval.

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

fit an artifact model to a neural recording signal to produce a fitted artifact model 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.

10. The system of claim 9, wherein said one or more devices comprises at least one or more of a computer, digital signal processor, and software application.

11. The system of claim 9, 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.

12. The system of claim 9, 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.

13. The system of claim 9, 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;

wherein said strength-of-response metric describes a distance of said neural recording signal from said net confidence interval.

14. 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.

15. The system of claim 14, 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.

16. The system of claim 14, 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.

17. The system of claim 14, wherein said strength-of-response metric is determined by evaluating

18. The system of claim 14, 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.

19. The system of claim 14, further comprising means for denoising said neural recording signal and said fitted artifact model signal prior to determining said strength-of-response metric.

20. The system of claim 14, further comprising:

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

wherein said strength-of-response metric describes a distance of said neural recording signal from said net confidence interval.