Pacemaker for treating physiological system dysfunction

• US 20090264952A1
• Filed: 07/14/2005
• Published: 10/22/2009
• Est. Priority Date: 07/14/2004
• Status: Active Grant

• Current Assignee: Arizona Board of Regents (University of Arizona)
• Original Assignee: Arizona Board of Regents (University of Arizona)
• Inventors: Jassemidis, Leonidas D, Tsakalis, Konstantinos S.
• Granted Patent: US 8,197,395 B2
• Time in Patent Office: Days
• US Class Current: 607/45
• 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 5/4094 Diagnosing or monitoring seizure diseases, e.g. epilepsy
• CPC Class Codes: A61N 1/36064 Epilepsy
• CPC Class Codes: A61N 1/36082 Cognitive or psychiatric applications, e.g. dementia or Alzheimer's disease

Abstract

Disclosed is a device and method for preventing seizures due to physiological system dysfunction. The method is based on a conjectural model of the brain wherein each brain site is modeled as a chaotic oscillator; a normal brain generates an internal feedback signal to prevent long-term entrainment among the oscillators; and a pathological brain fails to provide this feedback signal. The device of the present invention measures and characterizes the brain sites to determine if entrainment is occurring among the oscillators, derives an appropriate feedback signal to counteract the entrainment, and applies the feedback signal to the critical brain sites. The feedback signal generated by the device supplements or takes the place of the feedback signal that would otherwise be generated by the normal brain.

Claims

1. A method for treating a dynamical disorder, comprising:

acquiring a plurality of signals corresponding to a plurality of electrodes, wherein the plurality of electrodes corresponds to a plurality of brain sites;

computing a correlation between a number of signals within the plurality of signals, the number of signals corresponding to a number of brain sites within the plurality of brain sites;

computing a feedback signal that is a function of the correlation and the number of signals; and

applying the feedback signal to a number of transmit electrodes within the plurality of electrodes, the number of transmit electrodes corresponding to the number of brain sites.

2. The method of claim 1, wherein the dynamical disorder includes one of epilepsy, Parkinson'"'"'s Disease, migraines and schizophrenia.

3. The method of claim 1, wherein computing the correlation includes computing a T-index value corresponding to the number of electrodes.

4. The method of claim 3, further comprising:

assembling a state vector corresponding to each electrode within the plurality of electrodes; and

computing a Short Term Lyapunov exponent corresponding to each state vector, before computing the T-index.

5. The method of claim 4, further comprising:

computing an average difference between Short Term Lyapunov exponents, after computing the Short Term Lyapunov exponent; and

computing a standard deviation of differences between Short Term Lyapunov exponents, before computing the T-index.

6. The method of claim 1, wherein computing the feedback signal includes:

deriving a plurality of control parameters;

computing a gain factor based on the correlation and the plurality of control parameters; and

computing the feedback signal based on the plurality of signals and the gain factor.

7. The method of claim 6, wherein computing the gain factor includes applying the correlation, the control parameters, and a threshold to a Proportional Integral feedback mechanism.

8. The method of claim 6, wherein computing the gain factor includes applying the correlation and a threshold to an adaptive feedback mechanism.

9. The method of claim 6, wherein deriving the plurality of control parameters comprises performing a preliminary system identification test.

10. The method of claim 9, wherein performing the preliminary system identification test comprises:

transmitting a plurality of system identification signals to the plurality of electrodes;

acquiring a second plurality of signals corresponding to the plurality of system identification signals; and

determining a plurality of control parameters corresponding to the second plurality of signals and the plurality of system identification signals.

11. The method of claim 1, wherein computing the feedback signal comprises computing a feedback signal that is a function of a threshold.

12. The method of claim 1, wherein computing the feedback signal comprises applying the plurality of signals to a nonlinear filter.

13. The method of claim 1, wherein the plurality of electrodes includes a plurality of sensor electrodes and a plurality of transmit electrodes, wherein each brain site within the plurality of brain sites has a corresponding sensor electrode and transmit electrode.

14. A device for treating a dynamical disorder, comprising:

a plurality of electrodes corresponding to a plurality of brain sites; and

a microprocessor having a computer readable medium encoded with a program for acquiring a plurality of signals corresponding to the plurality of electrodes;

computing a correlation between a number of signals within the plurality of signals, the number of signals corresponding to a number of brain sites within the plurality of brain sites;

computing a feedback signal that is a function of the correlation and the number of signals; and

applying the feedback signal to a number of transmit electrodes within the plurality of electrodes, the number of transmit electrodes corresponding to the number of brain sites.

15. The device of claim 14, wherein the dynamical disorder includes one of epilepsy, Parkinson'"'"'s Disease, migraines and schizophrenia.

16. The device of claim 14, wherein the program for computing the correlation includes a program for computing a T-index value corresponding to the number of electrodes.

17. The device of claim 16, wherein the program further comprises:

a program for assembling a state vector corresponding to each electrode within the plurality of electrodes; and

a program for computing a Short Term Lyapunov exponent corresponding to each state vector, before computing the T-index.

18. The device of claim 17, wherein the program further comprising:

a program for computing an average difference between Short Term Lyapunov exponents, after computing the Short Term Lyapunov exponent; and

a program for computing a standard deviation of differences between Short Term Lyapunov exponents, before computing the T-index.

19. The device of claim 14, wherein the program for computing the feedback signal comprises:

a program for deriving a plurality of control parameters;

a program for computing a gain factor based on the correlation and the plurality of control parameters; and

a program for computing the feedback signal based on the plurality of signals and the gain factor.

20. The device of claim 19, wherein the program for computing the gain factor includes a program for applying the correlation, the control parameters, and a threshold to a Proportional Integral feedback mechanism.

21. The method of claim 19, wherein the program for computing the gain factor includes a program for applying the correlation and a threshold to an adaptive feedback mechanism.

22. A method for treating a dynamical disorder, comprising:

acquiring a plurality of signals corresponding to a plurality of electrodes, the plurality of electrodes corresponding to a plurality of brain sites

identifying entrainment between a number of brain sites within the plurality of brain sites; and

performing neurocontrol on the number of brain sites, based on a result of identifying entrainment.

23. The method of claim 22, wherein identifying entrainment comprises:

computing a T index corresponding to the number of brain sites; and

determining if the T-index is below a threshold.

24. The method of claim 23, wherein identifying entrainment further comprises determining if the T-index is below a threshold for a window of time.

25. The method of claim 22, wherein performing neurocontrol comprises performing neuromodulation.

26. The method of claim 25, wherein performing neuromodulation comprises:

determining a plurality of control parameters;

determining a correlation between a number of signals corresponding to the number of brain sites;

computing a feedback signal based on the plurality of control parameters and the correlation; and

applying the feedback signal to a number of electrodes corresponding to the number of brain sites.

27. The method of claim 22, wherein the dynamical disorder includes one of epilepsy, Parkinson'"'"'s Disease, migraines and schizophrenia.

28. The method of claim 22, wherein performing neurocontrol comprises minimizing a correlation between a number of signals corresponding to the number of brain sites.