SYSTEM AND METHOD FOR ICTAL SOURCE ANALYSIS

• US 20100049482A1
• Filed: 10/21/2008
• Published: 02/25/2010
• Est. Priority Date: 04/21/2006
• Status: Abandoned Application

• Current Assignee: Regents of The University of Minnesota (University of Minnesota)
• Original Assignee: Regents of The University of Minnesota (University of Minnesota)
• Inventors: Ding, Lei, Lai, Yuan, He, Bin
• Application Number:
  US12/255,324
• Publication Number:
  US 20100049482A1
• Time in Patent Office: Days
• US Class Current: 703/2
• 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

Abstract

This document discloses, among other things, ictal source analysis and causal interaction estimation which considers the structure of seizures in the space, time, and frequency domains. The dynamic causal interaction can distinguish the primary source, which initiates the ictal activity, from the secondary source, which is generated due to the ictal activity propagation.

Claims

1-42. -42. (canceled)

43. A system comprising:

an interface configured to receive epileptiform data from a brain;

a memory to store a model for the brain; and

a processor coupled to the memory and to the interface, the memory having instructions stored thereon for executing an algorithm to determine a solution using the epileptiform data and the model, the solution including identification of a plurality of sources, an estimate of spatio-temporal distribution for the plurality of sources, and including an estimate of connectivity for at least two sources of the plurality of sources.

44. The system of claim 43 wherein the algorithm is configured to determine connectivity at a particular frequency.

45. The system of claim 43 wherein the algorithm is configured to identify at least one primary source of the plurality of sources.

46. The system of claim 43 wherein the interface is configured to receive data corresponding to at least one of intracranial data, electroencephalogram (EEG) data, and magnetoencephalogram (MEG) data.

47. The system of claim 43 wherein the model includes at least one of a distributed dipole source model and a distributed multipole source model.

48. A method comprising:

receiving data corresponding to spatio-temporal cortical electrical potential distribution, the data including a plurality of interictal spikes;

calculating latencies between interictal spikes; and

determining a cortical neuronal activation sequence using the latencies.

49. The method of claim 48 further including identifying an epileptic seizure onset zone based on the latencies.

50. The method of claim 48 wherein the algorithm is configured to generate a solution using at least one of an amplitude, a first derivative, and a second derivative.

51. The method of claim 48 wherein calculating latencies between interictal spikes includes calculating using a peak amplitude.

52. The method of claim 48 further including generating a map using the cortical neuronal activation sequence.

53. A system comprising:

means for receiving data corresponding to brain activation over at least one of a brain surface and a brain volume;

means for determining a brain activation sequence using the data; and

means for generating an activation map using the activation sequence.

54. The system of claim 53 wherein the means for receiving data includes means for receiving intracranial electrical data, means for receiving noninvasive magnetic data, or means for receiving noninvasive electrical data.

55. The system of claim 53 wherein at least one of the means for determining and the means for generating includes a processor.

56. The system of claim 53 wherein the means for generating includes at least one of a printer and a visual display.

57. The system of claim 53 wherein the means for determining includes a dipole source model or a multipole source model, wherein the multipole source model includes more than two poles.