Multi-dimensional multi-parameter time series processing for seizure warning and prediction

• US 7,263,467 B2
• Filed: 09/30/2003
• Issued: 08/28/2007
• Est. Priority Date: 09/30/2002
• Status: Expired due to Fees

• Current Assignee: University of Florida Research Foundation, Incorporated (State University System of Florida), Arizona Board of Regents (University of Arizona)
• Original Assignee: University of Florida Research Foundation, Incorporated (State University System of Florida), Arizona Board of Regents (University of Arizona)
• Inventors: Iasemidis, Leonidas D., Shiau, Deng-Shan, Sackellares, James Chris, Chaovalitwongse, Wanpracha A., Dance, Linda, Pardalos, Panos M.
• Primary Examiner(s): Raymond; Edward
• Assistant Examiner(s): Charioui; Mohamed
• Application Number:
  US10/673,329
• Publication Number:
  US 20040127810A1
• Time in Patent Office: 1,428 Days
• Field of Search: 702/66, 702/67, 702/70, 702/73, 702/179, 702/181, 702/182, 702/183, 702/184, 702/185, 702/189, 702/190, 600/544, 600/545, 600/547
• US Class Current: 702/183
• CPC Class Codes: A61B 5/245 specially adapted for magnetoencephalographic [MEG] signals
• 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 5/7275 Determining trends in physiological measurement data; Predicting development of a medical condition based on physiological measurements, e.g. determining a risk factor

Abstract

Characterizing the behavior of a chaotic, multi-dimensional system is achieved by measuring each of a number of signals associated with the system, and generating therefrom, a spatio-temporal response based on each signal. Multiple dynamical profiles are then generated for each spatio-temporal response, where each of the multiple dynamical profiles correspond to a different one of multiple dynamical parameters. Over a period of time, a determination is made as to whether a certain level of dynamic entrainment and/or disentrainment exists between the dynamical profiles associated with a selected one or a selected combination of dynamical parameters. Seizure warnings and/or predictions are provided based on this determination.

Claims

1. A computer-implemented method of analyzing a multi-dimensional system comprising the steps of:

generating a multi-dimensional signal for each of a plurality of channels associated with the multi-dimensional system;

generating multiple dynamical profiles for each channel based on the corresponding multi-dimensional signal, where each profile reflects the dynamical characteristics of the corresponding channel in accordance with one of multiple dynamical parameters;

for a group of channels, generating a number of statistical measures, wherein each of the statistical measures reflects a correlation level between corresponding dynamical profiles, and wherein each of the corresponding dynamical profiles is associated with one of the multiple dynamical parameters;

selecting at least one dynamical parameter from amongst the multiple dynamical parameters;

generating a statistical measure for the channel group, wherein the statistical measure reflects a correlation level between corresponding dynamical profiles associated with the selected at least one dynamical parameter;

characterizing the behavior of the multi-dimensional system as a function of the statistical measure, generated for the channel group, that reflects the correlation level between corresponding dynamical profiles associated with the selected at least one dynamical parameter; and

outputting a signal indicative of the characterization of the behavior of the multi-dimensional system.

2. The method of claim 1, wherein said step of selecting at least one dynamical parameter from amongst the multiple dynamical parameters comprises the step of:

selecting a combination of dynamical parameters.

3. The method of claim 2, wherein the combination of dynamical parameters corresponds with one of the statistical measures generated for the channel group.

4. The method of claim 2, where in the statistical measure corresponding to the selected combination of dynamical parameters was determined to characterize the behavior of the multidimensional system better than the other statistical measures generated for the channel group.

5. The method of claim 1, wherein each of the statistical measures generated for the channel group are a function of corresponding dynamical profiles associated with one or a combination of dynamical parameters.