SYSTEM AND METHOD FOR ADAPTIVE STIMULUS-RESPONSE SIGNAL FILTERING

• US 20090149148A1
• Filed: 06/12/2008
• Published: 06/11/2009
• Est. Priority Date: 06/12/2007
• Status: Active Grant

• Current Assignee: Vivosonic, Inc.
• Original Assignee: Vovosonic Incorporated
• Inventors: Steinman, Aaron, KURTZ, Isaac, Sokolov, Yuri
• Granted Patent: US 8,484,270 B2
• Time in Patent Office: Days
• US Class Current: 455/307
• CPC Class Codes: A61B 5/316 Modalities, i.e. specific diagnostic methods
• CPC Class Codes: A61B 5/367 Electrophysiological study [EPS], e.g. electrical activation mapping or electro-anatomical mapping
• CPC Class Codes: A61B 5/7253 characterised by using transforms
• CPC Class Codes: G06F 2218/12 Classification; Matching
• CPC Class Codes: H03H 17/0211 using specific transformation algorithms, e.g. WALSH functions, Fermat transforms, Mersenne transforms, polynomial transforms, Hilbert transforms correlation computation G06F17/156
• CPC Class Codes: H03H 2222/06 using wavelets

Abstract

A system and method for filtering a signal comprising: receiving a signal of interest; receiving a signal indicating that a stimulus has been applied; receiving the synchronized stimulus signal and signal of interest; recursively selecting a portion of the signal of interest associated with a stimulus being applied and assign the selected portion of the signal of interest to one of the plurality of buffers; combining all responses in each of said plurality of buffers; transforming the combination of all responses in each buffer to a transform space; comparing the transform components of the buffers to determine a scaling factor; applying the scaling factor to the spectral components of the buffers; performing an inverse transform on the result of combining the buffers to return to the time domain to produce a filtered signal, and outputting the filtered signal received from the processor.

Claims

1. A system for filtering a signal comprising:

a) An input module for receiving a signal of interest;

b) A stimulus input module for receiving a signal indicating that a stimulus has been applied;

c) A plurality of memory buffers;

d) A processor configured to;

i) receive the synchronized stimulus signal and signal of interest;

ii) recursively select a portion of the signal of interest associated with a stimulus being applied and assign the selected portion of the signal of interest to one of the plurality of buffers;

iii) combine all responses in each of said plurality of buffers;

iv) transform the combination of all responses in each buffer to a transform space

v) compare the transform components of the buffers to determine a scaling factor;

vi) apply the scaling factor to the spectral components of the buffers;

vii) perform an inverse transform on the result of combining the buffers to return to the time domain to produce a filtered signal, and

e) an output module configured to output the filtered signal received from the processor.

2. The system of claim 1 wherein steps d) i) to v) are repeated for a plurality of random orderings of the data corresponding to a plurality of buffer assignments and step d) vi) uses a scaling factor that is estimated from the plurality of scaling factors determined in step v.

3. The system of claim 2 wherein the output signal is chosen from the plurality of buffer assignments such that the chosen buffer assignment is determined to be statistically representative of the plurality of buffers.

4. The system of claim 2 wherein the output signal is chosen from the plurality of buffer assignments such that the chosen buffer assignment is determined to optimize a statistical measure of repeatability between the buffers

10. The system of claim 1, wherein the transform the combination of all responses in each buffer to a spectral domain comprises performing a Discrete Wavelet Transform.

11. The system of claim 1, wherein the compare the spectral components of the buffers to determine a scaling factor comprises determining the synchronicity as defined by the equation:

$s_{n;<br><br>r}<br><br>\left(\mathrm{\tau <br><br>}\right)=\frac{{\int <br><br>}_{\mathrm{\tau <br><br>}-\frac{T_n}{2}}^{\mathrm{\tau <br><br>}+\frac{T_n}{2}}<br><br>A_{n;<br><br>r}^{\mathrm{\Psi <br><br>}}<br><br>\left(t\right)<br><br>B_{n;<br><br>r}^{\mathrm{\Psi <br><br>}}<br><br>\left(t\right)<br><br><br><br>t}{\sqrt{{\int <br><br>}_{\mathrm{\tau <br><br>}-\frac{T_n}{2}}^{\mathrm{\tau <br><br>}+\frac{T_n}{2}}<br><br>{\left(A_{n;<br><br>r}^{\mathrm{\Psi <br><br>}}<br><br>\left(t\right)\right)}^2<br><br><br><br>t·<br><br>{\int <br><br>}_{\mathrm{\tau <br><br>}-\frac{T_n}{2}}^{\mathrm{\tau <br><br>}+\frac{T_n}{2}}<br><br>{\left(B_{n;<br><br>r}^{\mathrm{\Psi <br><br>}}<br><br>\left(t\right)\right)}^2<br><br><br><br>t}}$

Where;

n represents the scale in the wavelet transform domain

r represents the value for a particular random ordering of the raw data

τ

represents time relative to the start of the ER analysis window

T_n represents the period of the wavelet transform for a particular scale n

A^ψ

<sub>n;

r</sub> and B^ψ

<sub>n;

r</sub> are the wavelet transforms of the A and B buffers.

5. A system for buffering a signal comprising:

a) An input module for receiving a signal of interest;

b) A stimulus input module for receiving a signal indicating that a stimulus has been applied;

c) each received signal associated with a stimulus is assigned to a plurality of buffers comprising;

i) Sampling the signal of interest at a time corresponding to the associated stimulus to obtain a sample signal; and

ii) Assigning the sampled signal to one of the plurality of buffers in a biased manner based on a comparative statistic related to measure of presence or the ER signal.

6. The system of claim 5, wherein the comparative statistic is mean squared error and the assigning the sampled signal to one of the plurality of buffers in a biased manner comprises:

a) First assigning a plurality of sampled signals to the plurality of buffers and calculating an error estimate between buffers;

b) Second assigning the plurality of sampled signals to the plurality of buffers in an order that is different from the first assigning and calculating a error estimate between buffers; and

c) Selecting the assignment that provides an error estimate that is closer to a target error estimate.

7. The system of claim 6 wherein the mean squared error between the buffers is used as an error estimate.

8. The system of claim 6 wherein the variance of a single point in a plurality of input data buffers is used as an error estimate.

9. The system of claim 6 wherein the said target error is the expected mean squared error predicted by a Kalman Filter used to weight the signal data prior to averaging in the buffer.

12. A method for filtering a signal comprising:

receiving a signal of interest;

receiving a signal indicating that a stimulus has been applied;

receiving the synchronized stimulus signal and signal of interest;

recursively selecting a portion of the signal of interest associated with a stimulus being applied and assign the selected portion of the signal of interest to one of the plurality of buffers;

combining all responses in each of said plurality of buffers;

transforming the combination of all responses in each buffer to a transform space

comparing the transform components of the buffers to determine a scaling factor;

applying the scaling factor to the spectral components of the buffers;

performing an inverse transform on the result of combining the buffers to return to the time domain to produce a filtered signal, and outputting the filtered signal received from the processor.

13. A method according to claim 12, wherein the combining all responses in each of said plurality of buffers comprises:

receiving a signal of interest;

receiving a signal indicating that a stimulus has been applied;

assigning each received signal associated with a stimulus to a plurality of buffers by;

sampling the signal of interest at a time corresponding to the

associated stimulus to obtain a sample signal; and

assigning the sampled signal to one of the plurality of buffers in a biased manner based on a comparative statistic related to measure of presence or the ER signal.