System and Method For Signal Denoising Using Independent Component Analysis and Fractal Dimension Estimation

• US 20090264786A1
• Filed: 04/21/2008
• Published: 10/22/2009
• Est. Priority Date: 04/21/2008
• Status: Abandoned Application

• Current Assignee: Brainscope SPV LLC
• Original Assignee: Brainscope Company Inc.
• Inventors: Jacquin, Arnaud
• Application Number:
  US12/106,657
• Publication Number:
  US 20090264786A1
• Time in Patent Office: Days
• US Class Current: 600/544
• CPC Class Codes: A61B 5/0002 Remote monitoring of patients using telemetry, e.g. transmission of vital signals via a communication network A61B5/07 takes precedence; transmission systems for measured values G08C; transmission H04B; transmission of digital information H04L; wireless communication networks H04W
• CPC Class Codes: A61B 5/316 Modalities, i.e. specific diagnostic methods
• CPC Class Codes: A61B 5/369 Electroencephalography [EEG] devices for psychotechnics A61B5/16
• CPC Class Codes: A61B 5/7203 for noise prevention, reduction or removal
• CPC Class Codes: G06F 2218/04 Denoising

Abstract

A system and method of signal denoising using Independent Component Analysis (ICA) and fractal dimension analysis of the signal components in the ICA domain is described. The signal components with fractal dimensions higher than a pre-determined threshold are automatically attenuated or canceled in order to alleviate the noise in the signal. The denoised signal is reconstructed using inverse ICA transform of the signal components.

Claims

1. A method for signal denoising, comprising the steps of:

i. decomposing the signal into a plurality of independent signal components using a signal transform;

ii. computing fractal dimensions of the components in the transform domain;

iii. identifying noise components based on their fractal dimensions;

iv. modifying the identified noise components;

V. reconstructing a denoised signal using inverse transform.

2. The method of claim 1, wherein the signal is decomposed into a plurality of independent signal components using Independent Component Analysis (ICA).

3. The method of claim 1, wherein the step of identifying noise components is performed automatically.

4. The method of claim 1, wherein the step of modifying comprises attenuation of signal components having a fractal dimension higher than a threshold value.

5. The method of claim 4, wherein the threshold value is predetermined.

6. The method of claim 4, wherein the attenuation is a non-linear process.

7. The method of claim 1, further comprising the step of automatically forwarding the denoised signal for further signal analysis.

8. A system for denoising a signal, the system comprising a processor configured for:

i. transforming the signal into a plurality of independent signal components;

ii. measuring the fractal dimensions of the components;

iii. processing the components with fractal dimensions higher than a predetermined value; and

iv. reconstructing a denoised signal using inverse transform.

9. The system of claim 8, wherein the processor is configured to separate the signal into a plurality of independent signal components using Independent Component Analysis (ICA).

10. The system of claim 9, wherein the processor is configured to reconstruct the denoised signal using inverse ICA transform.

11. The system of claim 8, wherein the processor is configured to cancel signal components with fractal dimensions higher that a predetermined threshold.

12. The system of claim 8, wherein the processor is configured to attenuate signal components with fractal dimensions higher that a predetermined threshold.

13. The system of claim 11, wherein the processor is configured to reconstruct a denoised signal using inverse transform of remaining signal components.

14. The system of claim 12, wherein the processor is configured to reconstruct a denoised signal using inverse transform of intact and the attenuated signal components.

15. A system for denoising brain electrical signals, the system comprising a processor configured for:

i. separating the signals into a plurality of independent signal sources/components using Independent Component Analysis;

ii. measuring the fractal dimensions of the components;

iii. automatically attenuating the components with fractal dimensions higher than a predetermined value; and

iv. reconstructing a denoised signal using inverse ICA transform of the attenuated and intact components.

16. An apparatus for acquiring and denoising brain electrical signals of a subject, comprising:

a headset comprising at least one electrode;

a base unit;

wherein

said base unit further comprises a processor configured to utilize one or more operating instructions to perform denoising of the received signal using Independent Component Analysis and fractal dimension analysis.

17. The apparatus of claim 16, wherein the processor is configured to further analyze the denoised signal and output a result.

18. The apparatus of claim 17, further comprising a display wherein the result of one or more operations performed by the processor is displayed.

19. The apparatus of claim 18, wherein the display is operatively connected to the processor;

and

wherein the display can be integrated into the base unit, or can be external to the base unit.

20. The apparatus of claim 16, wherein the headset communicates wirelessly with the base unit.

21. The apparatus of claim 16, wherein the headset comprises at least one analog amplification channel.

22. The apparatus of claim 21, wherein the headset further comprises an analog-to-digital converter.

23. The apparatus of claim 16, wherein the base unit communicates wirelessly with an external display.

24. The apparatus of claim 16, wherein the base unit comprises a stimulus generator to apply stimuli to the subject;

and

wherein the processor is configured to denoise spontaneous brain electrical signals and evoked potentials generated in response to the applied stimuli.

25. The apparatus of claim 22, wherein the headset and the base unit are configured to reside on a single platform to be connected to the subject;

and

wherein the processor, the at least one analog amplification channel, and the analog-to-digital converter are configured to reside on a single integrated physical circuit.