Fast estimation of weak bio-signals using novel algorithms for generating multiple additional data frames

• US 7,054,453 B2
• Filed: 03/29/2002
• Issued: 05/30/2006
• Est. Priority Date: 03/29/2002
• Status: Expired due to Term

• Current Assignee: Brainscope SPV LLC
• Original Assignee: Everest Biomedical Instruments Co. (Stryker Corporation)
• Inventors: Causevic, Elvir, Causevic, Eldar
• Primary Examiner(s): Chin, Vivian
• Assistant Examiner(s): Faulk, Devona E.
• Application Number:
  US10/113,425
• Publication Number:
  US 20030187638A1
• Time in Patent Office: 1,523 Days
• Field of Search: 381/94.1, 381/94.3, 381/94.2, 600/559, 704/226, 704/211
• US Class Current: 381/94.1
• CPC Class Codes: G06F 2218/06 by applying a scale-space analysis, e.g. using wavelet analysis
• CPC Class Codes: G10L 2021/065 Aids for the handicapped in understanding
• CPC Class Codes: G10L 21/0208 Noise filtering

Abstract

A method and apparatus for de-noising weak bio-signals having a relatively low signal to noise ratio utilizes an iterative process of de-noising a data set comprised of a new set of frames. The method separately performs a non-linear de-noising operation on each of the component frames and combines the resultant de-noised frames to form a combined resultant de-noised input signal. The method is preferably carried out in a digital processor.

Claims

1. A signal processor for de-noising an input signal, comprising:

a memory for storing the input signal,

wherein the input signal is divided into a plurality of frames of data points; and

a processor configured to;

separately perform a non-linear de-noising operation on each of the plurality of frames of data points;

re-order the frames of data points before de-noising them another time; and

combine all resultant de-noised frames of data points to form a combined resultant de-noised input signal,

wherein the non-linear de-noising operation includes;

performing a discrete wavelet transform on each of the plurality of frames of data points.

2. The signal processor of claim 1 wherein

the processor is further configured to iteratively de-noise the frames of data points for a plurality of times before they are finally combined into a combined resultant de-noised input signal.

3. The signal processor of claim 2 wherein

the processor is configured to combine multiple numbers of frames of data points before each iteration.

4. The signal processor of claim 2 wherein

the processor is further configured to use a different threshold for each iteration of de-noising.

5. The signal processor of claim 1 wherein

the input signal is comprised of an array of A1 or 2^N frames of data points, and the processor is further configured to perform up to N−

1 de-noising iterations.

21. The signal processor of claim 1, wherein

the segmented input signal is a bio-signal.

22. The signal processor of claim 21, wherein

the bio-signal is a weak bio-signal having a signal to noise ratio less than about 0 dB.

6. A signal processor for de-noising an input signal, comprising:

a memory for storing the input signal,

wherein the input signal is divided into a plurality of frames of data points; and

a processor configured to;

separately perform a non-linear de-noising operation on each of the plurality of frames of data points;

rearrange the plurality of frames of data points to create new frames of data points and perform a non-linear de-noising operation on each of the new frames of data points; and

combine the resultant de-noised frames of data points and the resultant de-noised new frames of data points to form a combined resultant de-noised input signal.

7. The signal processor of claim 6 wherein

the processor is further configured to iteratively de-noise the frames of data points for a plurality of times as part of each de-noising operation before they are finally combined into a combined resultant de-noised input signal.

8. The signal processor of claim 7 wherein

the process is configured to combine multiple numbers of frames of data points before each iteration.

9. The signal processor of claim 7 wherein

the processor is further configured to use a different threshold for each iteration of de-noising.

10. The signal processor of claim 6 wherein

the input signal is comprised of an array of A1 or 2^N frames of data points, and the processor is further configured to perform up to N−

1 de-noising iterations.

11. A method for de-noising an input signal comprising:

dividing the input signal into a plurality of frames of data points;

performing a separate non-linear de-noising operation on each of the plurality of frames of data points,

wherein the non-linear de-noising operation includes performing a discrete wavelet transform on each of the plurality of frames of data points;

re-ordering the frames of data points before de-noising them another time; and

combining the resultant de-noised frames of data points to form a combined resultant de-noised input signal.

12. The method of claim 11 wherein

the de-noising step further comprises iteratively de-noising the frames of data points for a plurality of times before they are finally combined into a combined resultant de-noised input signal.

13. The method of claim 12, wherein

the de-noising step further comprises combining multiple numbers of frames of data points before each iteration.

14. The method of claim 12 wherein

the de-noising step further comprises using a different threshold for each iteration of de-noising.

15. The method of claim 11 wherein

the input signal is comprised of an array of A1 or 2^N frames of data points, and the de-noising step includes performing up to N−

1 de-noising iterations.

16. A method for de-noising an input signal comprising:

dividing the input signal into a plurality of frames of data points;

performing a separate non-linear de-noising operation on each of the plurality of frames of data points;

rearranging the plurality of frames of data points to create new frames of data points and performing a non-linear de-noising operation on each of the new frames of data points; and

combining the resultant de-noised frames of data points and the resultant de-noised new frames of data points to form a combined resultant de-noised input signal.

17. The method of claim 16 wherein

the de-noising step further comprises iteratively de-noising the frames of data points for a plurality of times as part of each de-noising operation before they are finally combined into a combined resultant de-noised input signal.

18. The method of claim 17 further comprising the step of

combining multiple numbers of frames of data points before each iteration.

19. The method of claim 17 further comprising the step of

using a different threshold for each iteration of de-noising.

20. The method of claim 16 wherein

the input signal is comprised of an array of A1 or 2^N frames of data points, and wherein the de-noising step includes performing up to N−

1 de-noising iterations.

23. A signal processor for removing noise from a signal, comprising:

a memory for storing an input signal,

wherein the input signal is divided into a plurality of frames; and

a processor configured to;

perform a separate discrete wavelet transform on each of the plurality of frames to create an array of wavelet coefficients;

de-noise the array of wavelet coefficients;

re-order the wavelet coefficients in the array of wavelet coefficients to create rearranged frames;

de-noise the rearranged frames; and

combine the de-noised rearranged frames to form a combined resultant de-noised input signal.

24. A method for removing noise from an input signal comprising:

dividing the input signal into a plurality of frames;

performing a separate discrete wavelet transform on each of the plurality of frames to create an array of wavelet coefficients;

de-noising the array of wavelet coefficients;

re-ordering the wavelet coefficients in the array of wavelet coefficients to create rearranged frames;

de-noising the rearranged frames; and

combining the de-noised rearranged frames to form a combined resultant de-noised signal.