Monitoring electrical activity

• US 6,748,263 B2
• Filed: 10/08/2002
• Issued: 06/08/2004
• Est. Priority Date: 02/17/2000
• Status: Expired due to Fees

• Current Assignee: Alpha-Active Limited
• Original Assignee: University of Bristol
• Inventors: Preece, Alan W, Griffiths, Mark James
• Primary Examiner(s): Marmor, Charles
• Assistant Examiner(s): Mallari, Patricia C.
• Application Number:
  US10/203,954
• Publication Number:
  US 20030109796A1
• Time in Patent Office: 609 Days
• Field of Search: 600/300-301, 600/500-505, 600/544-546
• US Class Current: 600/544
• CPC Class Codes: A61B 5/301 providing electrical separation, e.g. by using isolating transformers or optocouplers
• CPC Class Codes: A61B 5/374 Detecting the frequency distribution of signals, e.g. detecting delta, theta, alpha, beta or gamma waves
• CPC Class Codes: A61B 5/4821 Determining level or depth of anaesthesia A61B5/1106 takes precedence

Abstract

A method and apparatus for monitoring electrical activity, such as brainwaves, in an animal comprising detecting said activity to produce a corresponding output signal, combining the output signal with a random noise signal to produce a modified signal, and analyzing the modified signal using an autocorrelation technique to detect the relative power density values at a plurality of different frequencies. The random noise signal may be a random number. The autocorrelation technique may involve the Yule-Walker method.

Claims

1. A method of monitoring electrical activity in an animal comprising detecting said activity to produce a corresponding output signal, combining the output signal with a random noise signal to produce a modified signal, and analyzing the modified signal using an autocorrelation technique to detect the relative power density values at a plurality of different frequencies.

2. A method as claimed in claim 1 in which the output signal is sampled at intervals.

3. A method as claimed in claim 2 in which the samples a_k of the output signal are digital samples.

4. A method as claimed in claim 3 in which the random noise signal consists in a random number that is added to each sample a_k.

5. A method as claimed in claim 4 in which successive samples are averaged over an epoch and the average a_k′

subtracted from each sample a_k to produce a modified sample a_k″

.

6. A method as claimed in claim 4 in which the samples a_k, a_k″

are processed to derive a number of autocorrelation products x_p, using the Yule-Walker method.

7. A method as claimed in claim 6, in which $x_p=\frac{1}{n-p}$

∑

k=0n-p-1

 



ak″



ak+p″

where p is the number of the autocorrelation product between 0 and m.

8. A method as claimed in claim 7 in which the autocorrelation products x₀ to x_m are averaged over successive epochs.

9. A method as claimed in claim 8 in which a running average R_p of the autocorrelation products is derived from the averages of successive epochs.

10. A method as claimed in claim 8 in which the averaged autocorrelation products are analysed according to the Yule-Walker equation to derive Yule-Walker coefficients y_oto y_m.

11. A method as claimed in claim 10 in which the Levinson-Durbin algorithm is used to derive the Yule-Walker coefficients y₀ to y_m from the Yule-Walker equation.

12. A method as claimed in claim 10 in which the Yule-Walker coefficients are used to derive the relative power density D_f at a frequency f of the output signal, where $D_f=\frac{1}{|1+\sum _{}^{}}$

p=1M

 



yp

exp

(-i·

a·

f·

p)

|2

13. A method as claimed in claim 12 in which the relative power density D_f is derived for multiple frequencies of the output signal, and the relative power density D_f at one frequency or over a first range of frequencies is compared with the power densities D_f over a wider range of frequencies to detect a change in power density at said one frequency or first range of frequencies.

14. A method as claimed in claim 1 applied to the monitoring of brain waves.

15. Apparatus for monitoring electrical activity in an animal comprising a detector to produce an output signal corresponding to the electrical activity, a random noise generator to produce a random noise signal, and a processor to combine the output signal and random noise signal to produce a modified signal and to analyse the modified signal using an autocorrelation technique to detect the elative power density values at a plurality of different frequencies.

16. Apparatus as claimed in claim 15, in which the processor samples the output signal at intervals.

17. Apparatus as claimed in claim 16 in which the processor samples digital samples a_k of the output signal.

18. Apparatus as claimed in claim 17 in which the random noise generator produces a random noise signal in the form of a random number that is added to each sample a_k.

19. Apparatus as claimed in claim 18 in which the processor averages successive samples over an epoch and subtracts the average a_k′

from each sample a_k to produce a modified sample a_k″

.

20. Apparatus as claimed in claim 18 in which the processor processes samples a_k, a_k″

to derive a number of autocorrelation products x_p using the Yule-Walker method.

21. Apparatus as claimed in claim 20, in which $x_p=\frac{1}{n-p}$

∑

k=0n-p-1

 



ak″



ak+p″

where p is the number of the autocorrelation product between 0 and m.

22. Apparatus as claimed in claim 21 in which the autocorrelation products x_o to X_m are averaged by the processor over successive epochs.

23. Apparatus as claimed in claim 22 in which a running average R_p of the autocorrelation products is derived by the processor from the averages of successive epochs.

24. Apparatus as claimed in claim 22 in which the averaged autocorrelation products are analysed by the processor according to the Yule-Walker equation to derive Yule-Walker coefficients y_o to y_m.

25. Apparatus as claimed in claim 24 in which the processor uses the Levinson-Durbin algorithm to derive the Yule-Walker coefficients y_o to y_m from the Yule-Walker equation.

26. Apparatus as claimed in claim 24 in which the processor uses the Yule-Walker coefficients to derive the relative power density D_f at a frequency f of the output signal, where $D_f=\frac{1}{|1+\sum _{}^{}}$

p=1M

 



yp

exp

(-i·

a·

f·

p)

|2

and a is a constant and M is the order of the Yule-Walker equation.

27. Apparatus as claimed in claim 26 in which the processor derives the relative power density D_f for multiple frequencies of the output signal, and compares the relative power density D_f at one frequency or over a first range of frequencies with the power densities D_f over a wider range of frequencies to detect a change in power density at said one frequency or first range of frequencies.

28. An electroencephalograph comprising apparatus as claimed in claim 15.