Seizure detection device and systems

Patent metadata

Field	Value
Publication number	US-9737230-B2
Application number	US-201213977477-A
Country	US
Kind code	B2
Filing date	Jan 6, 2012
Priority date	Jan 6, 2011
Publication date	Aug 22, 2017
Grant date	Aug 22, 2017

Abstract

A neurostimulation device includes a plurality of electrodes adapted to be electrically connected to a subject to receive multichannel electrical signals from the subject's brain, a multichannel seizure detection unit electrically connected to the plurality of electrical leads to receive the multichannel electrical signals, and a neurostimulation unit in communication with the multichannel seizure detection unit. The plurality of electrodes are at least three electrodes such that the multichannel electrical signals are at least three channels of electrical signals, and the multichannel seizure detection unit detects a presence of a seizure based on multichannel statistics from the multichannel electrical signals including higher order combinations than two-channel combinations.

First claim

We claim:

1. A neurostimulation device, comprising:
a plurality of electrodes adapted to be electrically connected to a subject to receive multichannel electrical signals from a brain of said subject;
a multichannel seizure detection unit electrically connected to said plurality of electrodes to receive said multichannel electrical signals; and
a neurostimulation unit in communication with said multichannel seizure detection unit,
wherein said plurality of electrodes are at least three electrodes such that said multichannel electrical signals are at least three channels of electrical signals,
wherein said multichannel seizure detection unit detects a presence of a seizure based on multichannel statistics from said multichannel electrical signals including higher order combinations than two-channel combinations,
wherein said multichannel seizure detection unit is configured to detect said presence of said seizure based on optimizing a cost function, and
wherein said cost function is dependent on a time delay between an actual seizure and a prediction of said seizure.

2. A neurostimulation device according to claim 1 , wherein said plurality of electrodes are at least ten electrodes such that said multichannel electrical signals are at least ten channels of electrical signals.

3. A neurostimulation device according to claim 2 , wherein said multichannel statistics includes statistics for at least all pairs of said multichannel electrical signals.

4. A neurostimulation device according to claim 1 , wherein said multichannel seizure detection unit is configured to model said multichannel electrical signals based on a brain network model.

5. A neurostimulation device according to claim 4 , wherein said brain network model models time-dependent variations of said multichannel statistics.

6. A neurostimulation device according to claim 4 , wherein said brain network model is a Hidden Markov Model.

7. A neurostimulation device according to claim 1 , wherein said multichannel seizure detection unit is configured to detect said presence of said seizure based on a time-dependent threshold.

8. A neurostimulation device according to claim 1 , wherein said cost function is further dependent on a probability of a false positive detection.

9. A neurostimulation device according to claim 1 , wherein said neurostimulation unit is triggered by said multichannel seizure detection unit to provide an electrical stimulation.

10. A neurostimulation device according to claim 1 , wherein said neurostimulation unit is triggered by said multichannel seizure detection unit to provide a chemical stimulation.

11. A neurostimulation device according to claim 1 , wherein said neurostimulation unit is triggered by said multichannel seizure detection unit to provide at least one of a visual or auditory warning.

12. A neurostimulation device according to claim 1 , wherein said neurostimulation device is an implantable device.

13. A multichannel seizure detection system, comprising:
a signal interface adapted to receive multichannel electrical signals from a brain of a subject; and
a data processor configured to receive said multichannel electrical signals and detect a presence of a seizure based on multichannel statistics from said multichannel electrical signals including higher order combinations than two-channel combinations,
wherein said multichannel electrical signals are at least three channels of electrical signals,
wherein said data processor is configured to detect said presence of said seizure based on optimizing a cost function, and
wherein said cost function is dependent on a time delay between an actual seizure and a prediction of said seizure.

14. A multichannel seizure detection system according to claim 13 , wherein said multichannel electrical signals are at least ten channels of electrical signals.

15. A multichannel seizure detection system according to claim 14 , wherein said multichannel statistics includes statistics for at least all pairs of said multichannel electrical signals.

16. A multichannel seizure detection system according to claim 13 , wherein said data processor is further configured to model said multichannel electrical signals based on a brain network model.

17. A multichannel seizure detection system according to claim 16 , wherein said brain network model models time-dependent variations of said multichannel statistics.

18. A multichannel seizure detection system according to claim 16 , wherein said brain network model is a Hidden Markov Model.

19. A multichannel seizure detection system according to claim 16 , wherein said brain network model is a two-state model.

20. A multichannel seizure detection system according to claim 16 , wherein said brain network model is a multi-state model.

21. A multichannel seizure detection system according to claim 13 , wherein said data processor is configured to detect said presence of said seizure based on a time-dependent threshold.

22. A multichannel seizure detection system according to claim 13 , wherein said cost function is further dependent on a probability of a false positive detection.

Assignees

• Sarma Sridevi V
• Santaniello Sabato
• Burns Samuel P
• Dahleh Munther
• Univ Johns Hopkins

Inventors

• Sarma Sridevi V
• Santaniello Sabato
• Burns Samuel P
• Dahleh Munther

Classifications

• A61N1/36135 Primary

  using physiological parameters · CPC title

• A61N1/36064

  Epilepsy · CPC title

• A61B5/4094

  Diagnosing or monitoring seizure diseases, e.g. epilepsy · CPC title

• A61B5/0476 Primary

  Human Necessities · mapped topic

• A61B5/0482

  Human Necessities · mapped topic