System and method of prediction of response to neurological treatment using the electroencephalogram

• US 7,706,871 B2
• Filed: 02/18/2005
• Issued: 04/27/2010
• Est. Priority Date: 05/06/2003
• Status: Active Grant

• Current Assignee: Covidien LP (Medtronic Plc)
• Original Assignee: Nellcor Puritan Bennett LLC (Medtronic Plc)
• Inventors: Devlin, Philip H., Greenwald, Scott D.
• Primary Examiner(s): Nasser; Robert L
• Application Number:
  US11/061,103
• Publication Number:
  US 20050216071A1
• Time in Patent Office: 1,894 Days
• Field of Search: 600/544, 600/545
• US Class Current: 600/544
• CPC Class Codes: A61B 5/369 Electroencephalography [EEG] devices for psychotechnics A61B5/16
• 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: A61N 1/0534 Electrodes for deep brain stimulation
• CPC Class Codes: A61N 1/0551 Spinal or peripheral nerve electrodes
• CPC Class Codes: A61N 1/36025 for treating a mental or cerebral condition
• CPC Class Codes: A61N 1/36082 Cognitive or psychiatric applications, e.g. dementia or Alzheimer's disease

Abstract

Disclosed is a system and method of assessing the efficacy of and predicting response to treatment of neurological or psychological disorders. The preferred embodiment uses at least two surface electrodes to acquire EEG signals from the surface of a patient'"'"'s body, a processor for computing from the EEG signals various features and indices that are representative of the patient'"'"'s neurological or psychological state. Pretreatment indices represent a patient'"'"'s neurological or psychological state and therefore may be used to predict the response to treatment. Changes in these parameters may be used to assess the efficacy of treatment and to modify the treatment to optimize the resultant patient state.

Claims

1. A system for predicting response to treatment of a neurological disorder comprising:

at least two electrodes for acquiring at least one electrophysiological signal from the body;

a processor for calculating from said at least one electrophysiological signal at least one feature relating to the response to said treatment without comparing said at least one feature to a reference dataset, at least one of the at least one electrophysiological signal being acquired before the initiation of said treatment and one of said at least one feature being a prediction of the effectiveness of a treatment in achieving a change in a self-reported mood or anxiety score or a prediction of response to a treatment in achieving a change in a self-reported mood or anxiety score;

wherein said processor calculates at least two features and combines said at least two features into an index.

2. The system for predicting response to treatment of a neurological disorder of claim 1 wherein said treatment is neurostimulation.

3. The system for predicting response to treatment of a neurological disorder of claim 2 wherein said neurostimulation is deep brain stimulation.

4. The system for predicting response to treatment of a neurological disorder of claim 2 wherein said neurostimulation is vagus nerve stimulation.

5. The system for predicting response to treatment of a neurological disorder of claim 1 wherein said treatment is the administration of a pharmacological agent.

6. The system for predicting response to treatment of a neurological disorder of claim 1 wherein said treatment is electroconvulsive therapy.

7. The system for predicting response to treatment of a neurological disorder of claim 1 wherein said treatment is transcranial magnetic stimulation.

8. The system for predicting response to treatment of a neurological disorder of claim 1 wherein said processor calculates at least one feature from a spectral array.

9. The system for predicting response to treatment of a neurological disorder of claim 1 wherein said processor calculates at least one feature from a power spectral array.

10. The system for predicting response to treatment of a neurological disorder of claim 1 wherein said processor calculates at least one feature from a bispectral array.

11. The system for predicting response to treatment of a neurological disorder of claim 1 wherein said at least one feature is a time domain feature.

12. The system for predicting response to treatment of a neurological disorder of claim 1 wherein said at least two electrodes are placed in a bilateral montage.

13. The system for predicting response to treatment of a neurological disorder of claim 1 wherein said at least two electrodes are placed in a unilateral montage.

14. A system for predicting response to treatment of a neurological disorder comprising:

at least two electrodes for acquiring at least one electrophysiological signal from the body;

a processor for calculating from said at least one electrophysiological signal at least one feature relating to the response to said treatment without comparing said at least one feature to a reference dataset, at least one of the at least one electrophysiological signal being acquired before the initiation of said treatment and one of said at least one feature being a prediction of the effectiveness of a treatment in achieving a change in a self-reported mood or anxiety score or a prediction of response to a treatment in achieving a change in a self-reported mood or anxiety score;

wherein said feature is the interhemispheric difference in a metric calculated from each electrophysiological signal.

15. The system for predicting response to treatment of a neurological disorder of claim 14 wherein said metric is a spectral feature.

16. The system for predicting response to treatment of a neurological disorder of claim 14 wherein said metric is a time domain feature.

17. A system for predicting response to treatment of a neurological disorder comprising:

at least two electrodes for acquiring electrophysiological signals from a body;

data acquisition circuitry for acquiring from said electrodes a first electrophysiological signal representing a baseline condition and a second electrophysiological signal representing a subsequent condition, said first electrophysiological signal being acquired before the initiation of said treatment;

a processor for calculating from said electrophysiological signals received from the data acquisition circuitry;

(a) at least one feature relating to the patient state during the baseline condition without comparing said at least one feature relating to the patient state during the baseline condition to a reference data set;

(b) at least one feature relating to the patient state during the subsequent condition without comparing said at least one feature relating to the patient state during the subsequent condition to a reference dataset; and

(c) the difference between said features relating to the baseline and subsequent conditions, such that said difference relates to a prediction of a change in a self-reported mood or anxiety score.

18. A method of predicting response to treatment of a neurological disorder comprising the steps of:

acquiring electrophysiological signals from a body through electrodes placed on the body;

calculating, using a processor, from said electrophysiological signals at least two features relating to the response to said treatment without comparing said at least two features to a reference dataset, at least one of said electrophysiological signals being acquired before the initiation of said treatment and one of said features being a prediction of the effectiveness of a treatment in achieving a change in a self reported mood or anxiety score; and

combining the at least two features into an index.

19. The method of predicting response to treatment of a neurological disorder of claim 18 wherein said treatment is neurostimulation.

20. The method of predicting response to treatment of a neurological disorder of claim 19 wherein said neurostimulation is deep brain stimulation.

21. The method of predicting response to treatment of a neurological disorder of claim 19 wherein said neurostimulation is vagus nerve stimulation.

22. The method of predicting response to treatment of a neurological disorder of claim 18 wherein said treatment is the administration of a pharmacological agent.

23. The method of predicting response to treatment of a neurological disorder of claim 18 wherein said treatment is electroconvulsive therapy.

24. The method of predicting response to treatment of a neurological disorder of claim 18 wherein said treatment is transcranial magnetic stimulation.

25. The method of predicting response to treatment of a neurological disorder of claim 18 wherein said at least one feature is calculated from a spectral array.

26. The method of predicting response to treatment of a neurological disorder of claim 18 wherein at least one feature is calculated from a power spectral array.

27. The method of predicting response to treatment of a neurological disorder of claim 18 wherein at least one feature is calculated from a bispectral array.

28. The method of predicting response to treatment of a neurological disorder of claim 18 wherein at least one feature is a time domain feature.

29. The method of predicting response to treatment of a neurological disorder of claim 18 wherein at least two electrodes are placed in a bilateral montage.

30. The method of predicting response to treatment of a neurological disorder of claim 18 wherein at least two electrodes are placed in a unilateral montage.

31. A method of predicting response to treatment of a neurological disorder comprising the steps of:

acquiring electrophysiological signals from a body through electrodes placed on the body;

calculating, using a processor, from said electrophysiological signals at least one feature relating to the response to said treatment without comparing said at least one feature to a reference dataset, at least one of said electrophysiological signals being acquired before the initiation of said treatment and one of said at least one feature being a prediction of the effectiveness of a treatment in achieving a change in a self reported mood or anxiety score;

wherein said feature is the interhemispheric difference in a metric calculated from each electrophysiological signal.

32. The method of predicting response to treatment of a neurological disorder of claim 31 wherein said metric is a spectral feature.

33. The method of predicting response to treatment of a neurological disorder of claim 31 wherein said metric is a time domain feature.

34. A method of predicting response to treatment of a neurological disorder comprising:

positioning a least two electrodes on a body being treated;

acquiring a first electrophysiological signal from the body at a baseline condition;

acquiring a second electrophysiological signal from the body during a subsequent condition, said first electrophysiological signal being acquired before the initiation of said treatment;

calculating at least one feature relating to the patient state during the baseline condition without comparing said at least one feature relating to the patient state during the baseline condition to a reference dataset;

calculating at least one feature relating to the patient state during the subsequent condition without comparing said at least one feature relating to the patient state during the subsequent condition to a reference dataset;

calculating, using a processor, the difference between the features calculated during the baseline and subsequent conditions, such that the difference relates to a change in a self-reported mood or anxiety score.