BRAIN STATE ANALYSIS BASED ON SELECT SEIZURE ONSET CHARACTERISTICS AND CLINICAL MANIFESTATIONS

• US 20100168603A1
• Filed: 12/23/2009
• Published: 07/01/2010
• Est. Priority Date: 12/23/2008
• Status: Abandoned Application

• Current Assignee: Cyberonics, Inc
• Original Assignee: Cyberonics, Inc
• Inventors: Rolfe, Sara M., HIMES, David M., Snyder, David E.
• Application Number:
  US12/646,783
• Publication Number:
  US 20100168603A1
• Time in Patent Office: Days
• US Class Current: 600/544
• 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: A61B 5/6814 Head
• CPC Class Codes: A61B 5/6846 specially adapted to be brought in contact with an internal body part, i.e. invasive
• CPC Class Codes: A61B 5/7275 Determining trends in physiological measurement data; Predicting development of a medical condition based on physiological measurements, e.g. determining a risk factor
• CPC Class Codes: G16H 50/20 for computer-aided diagnosis, e.g. based on medical expert systems

Abstract

Systems and methods for developing a brain state analysis system using subject EEG data are provided. The analysis system distinguishes clinical from subclinical electrographic seizures and optionally distinguishes among different seizure onset characteristics. An algorithm trained on only clinical electrographic seizures may predict clinical seizures more accurately with fewer perceived false positives. In addition, algorithms trained on a particular onset condition may distinguish and advise on that onset condition when used by the patient.

Claims

1. A method of developing a brain state advisory system, comprising:

deriving a brain state advisory algorithm, the deriving step comprising;

analyzing patient EEG data,

identifying within the EEG data pro-ictal states correlated with clinical electrographic seizures, and

generating pro-ictal state alerts corresponding to pro-ictal states preferentially correlated with clinical electrographic seizures over pro-ictal states correlated with subclinical electrographic seizures;

and

placing the advisory algorithm in memory of the brain state advisory system.

2. The method of claim 1 wherein the identifying step comprises comparing EEG data with primary confirmation of clinical seizure.

3. The method of claim 1 wherein the identifying step comprises comparing EEG data with known EEG data seizure characteristics.

4. The method of claim 1 wherein the identifying step comprises identifying a seizure onset characteristic and the generating step comprises generating a pro-ictal state alert corresponding to the seizure onset characteristic.

5. The method of claim 4 wherein the seizure onset characteristic comprises a first seizure onset characteristic and the identifying step comprises identifying a second seizure onset characteristic and the generating step further comprises generating a pro-ictal state alert corresponding to the second seizure onset characteristic.

6. The method of claim 5 wherein the pro-ictal state alert corresponding to the second seizure onset characteristic is distinct from the pro-ictal state alert corresponding to the first seizure onset characteristic.

7. The method of claim 1 further comprising identifying within the EEG data pro-ictal states correlated with subclinical electrographic seizures and generating subclinical pro-ictal state alerts distinct from the pro-ictal state alerts corresponding to pro-ictal states correlated with clinical electrographic seizures.

8. The method of claim 1 further comprising identifying within the EEG data a pro-ictal state correlated with a subclinical electrographic seizure and suppressing a pro-ictal state alert if a pro-ictal state is also correlated with a clinical electrographic seizure within the same EEG data.

9. The method of claim 1 further comprising identifying within the EEG data a pro-ictal state correlated with a subclinical electrographic seizure and generating a subclinical pro-ictal state alert distinct from the pro-ictal state alerts if a pro-ictal state is also correlated with a clinical electrographic seizure within the same EEG data.

10. The method of claim 1 further comprising generating no pro-ictal alerts correlated with subclinical electrographic seizures.

11. A brain state system comprising:

an advisory system having a controller programmed to generate a pro-ictal state alert preferentially correlated with clinical electrographic seizures over subclinical electrographic seizures; and

an alert indicator communicating with the controller to indicate the pro-ictal state alert.

12. The brain state system of claim 11 wherein the pro-ictal state alert is a first pro-ictal state alert corresponding to a first seizure onset characteristic, wherein the controller is programmed to generate a second pro-ictal state alert corresponding to a second seizure onset characteristic, the second pro-ictal state alert being distinct from the first pro-ictal state alert.

13. The brain state system of claim 11 wherein the controller is programmed to generate a subclinical pro-ictal state alert correlated with subclinical electrographic seizures and distinct from the pro-ictal state alert corresponding to pro-ictal states correlated with clinical electrographic seizures.

14. The brain state system of claim 11 wherein the controller is programmed to generate no pro-ictal state alerts correlated with subclinical electrographic seizures.

15. The brain state system of claim 11 further comprising a patient therapy system communicating with the controller to provide therapy in response to an alert generated by the advisory system.

16. The brain state system of claim 15 wherein the patient therapy system is adapted to provide distinct therapies in response to alerts corresponding to distinct seizure onset characteristics.

17. The brain state system of claim 15 wherein the patient therapy system is adapted to provide distinct therapies in response to alerts correlated with clinical and subclinical electrographic seizures.

18. A method of treating a subject comprising:

obtaining an EEG dataset from the subject;

identifying a pro-ictal state preferentially correlated with a clinical electrographic seizure over a subclinical electrographic seizure; and

generating a pro-ictal state alert corresponding to the pro-ictal state identified in the identifying step.

19. The method of claim 18 wherein the identifying step comprises identifying a pro-ictal state corresponding with a seizure onset characteristic and generating step comprises generating a pro-ictal state alert corresponding to the seizure onset characteristic.

20. The method of claim 19 wherein the seizure onset characteristic comprises a first seizure onset characteristic, identifying step further comprises identifying a pro-ictal state corresponding with a second seizure onset characteristic and the generating step comprises generating a pro-ictal state alert corresponding to the second seizure onset characteristic.

21. The method of claim 20 wherein the pro-ictal state alert corresponding to the first seizure onset characteristic is distinct from the pro-ictal state alert corresponding to the second seizure onset characteristic.

22. The method of claim 18 further comprising identifying a pro-ictal state correlated with a subclinical electrographic seizure;

and generating a subclinical pro-ictal state alert corresponding to the pro-ictal state correlated with the subclinical electrographic seizure.

23. The method of claim 22 wherein the pro-ictal state alert corresponding to the pro-ictal state correlated with the clinical electrographic seizure is distinct from the subclinical pro-ictal state alert corresponding to the pro-ictal state correlated with the subclinical electrographic seizure.

24. The method of claim 20 further comprising generating no pro-ictal alerts correlated with subclinical electrographic seizures.

25. The method of claim 20 further comprising identifying within the EEG data a pro-ictal state correlated with a subclinical electrographic seizure and suppressing a pro-ictal state alert if a pro-ictal state is also correlated with a clinical electrographic seizure within the same EEG data.

26. The method of claim 20 further comprising identifying within the EEG data a pro-ictal state correlated with a subclinical electrographic seizure and generating a subclinical pro-ictal state alert distinct from the pro-ictal state alert if a pro-ictal state is also correlated with a clinical electrographic seizure within the same EEG data.

27. The method of claim 18 further comprising automatically providing a therapy to the subject in response to the alert.

28. The method of claim 18 further comprising automatically providing distinct therapies to the subject in response to alerts corresponding to distinct seizure onset characteristics.

29. The method of claim 18 further comprising automatically providing distinct therapies to the subject in response to alerts correlated with clinical and subclinical electrographic seizures.

30. A method of developing a seizure prediction system, comprising:

analyzing a patient EEG data set including clinical electrographic seizures and subclinical electrographic seizures; and

developing a seizure prediction algorithm for predicting seizures based on brain states preferentially correlated with clinical electrographic seizures over subclinical electrographic seizures.

31. The method of claim 30, further comprising:

storing the seizure prediction algorithm in memory of seizure prediction system.