Optimization of spatio-temporal pattern processing for seizure warning and prediction
View Patent ↗An exemplary method for analyzing behavior of a system includes receiving dynamical measurement data regarding the system, applying a quadratically constrained quadratic 0-1 problem to identify data among the received data, and storing the identified data. An exemplary seizure warning method includes continuously calculating STL max values, identifying critical sites by applying a quadratically constrained quadratic 0-1 solution to data (e.g. STL max profiles) derived from pre-seizure onset and post-seizure onset EEG signals, monitoring a T-index curve of the identified critical sites, warning of an impending seizure, observing a seizure and then repeating the cycle by identifying critical sites using the new data, and then monitoring them.
1. A computer-implemented method for predicting an impending seizure, comprising:
receiving dynamical measurement data regarding the system;
applying a quadratically constrained quadratic 0-1 program to identify data components among the received data, wherein the applying comprises linearizing the quadratically constrained quadratic 0-1 program, and wherein the linearizing comprises introducing a new 0-1 variable for each product of two variables and then formulating the quadratically constrained quadratic 0-1 program as a linear 0-1 problem;
storing the identified data components; and
predicting an impending seizure based on the identified data components.
2. A computer-implemented method for predicting an impending seizure, comprising:
receiving dynamical measurement data regarding the system;
applying a quadratically constrained quadratic 0-1 program to identify data components among the received data, wherein the applying comprises linearizing the quadratically constrained quadratic 0-1 program and comparing measures of statistical distance between mean values of the dynamical measurement data;
storing the identified data components; and
predicting an impending seizure based on the identified data components.
3. A computer-implemented method for predicting an impending seizure, comprising:
receiving dynamical measurement data regarding the system, wherein the dynamical measurement data includes maximum Lyapunov exponents;
applying a quadratically constrained quadratic 0-1 program to identify data components among the received data, wherein the applying comprises linearizing the quadratically constrained quadratic 0-1 program;
storing the identified data components; and
predicting an impending seizure based on the identified data components.
4. The method of claim 3 , wherein the applying comprises applying a T-statistic to the dynamical measurement data.
5. A computer-implemented method for predicting an impending seizure, comprising:
receiving dynamical measurement data regarding the system;
applying a quadratically constrained quadratic 0-1 program to identify data components among the received data, wherein the applying comprises minimizing statistical distances between mean values of the dynamical measurement data;
storing the identified data components; and
predicting an impending seizure based on the identified data components.
6. The method of claim 1 , wherein the dynamical measurement data comprise multi-dimensional time series data.
7. A computer-implemented method for predicting an impending seizure, comprising:
receiving dynamical measurement data regarding the system;
applying a quadratically constrained quadratic 0-1 program to identify data components among the received data, wherein the applying comprises applying Karush Kuhn optimality conditions and then formulating the quadratically constrained quadratic 0-1 program as a linear mixed integer 0-1 problem;
storing the identified data components; and
predicting an impending seizure based on the identified data components.
8. The method of claim 7 , comprising displaying the predicted future behavior.
9. The method of claim 7 , wherein the applying comprises comparing measures of statistical distance between mean values of the dynamical measurement data.
10. The method of claim 7 , wherein the dynamical measurement data includes maximum Lyapunov exponents.
11. The method of claim 10 , wherein the applying comprises applying a T-statistic to the dynamical measurement data.
12. A computer-implemented method for predicting an impending seizure, comprising:
receiving dynamical measurement data regarding the system, wherein the receiving comprises receiving largest Lyapunov exponent (STLmax) values calculated based on multi-channel data signals recorded from a brain;
applying a quadratically constrained quadratic 0-1 program to identify data components among the received data;
storing the identified data components; and
predicting an impending seizure based on the identified data components.
13. The method of claim 12 , wherein the applying comprises applying the quadratically constrained quadratic 0-1 program to profiles of the calculated STLmax values to identify critical, data channels recorded from a brain area.
14. The method of claim 13 , further comprising:
monitoring a T-index profile of the identified critical data channels; and
warning of the impending seizure, based on the monitoring.
15. The method of claim 13 wherein the applying comprises linearizing the quadratically constrained quadratic 0-1 program, wherein the linearizing comprises introducing a new 0-1 variable for each product of two variables and then formulating the quadratically constrained quadratic 0-1 program as a linear 0-1 problem.
16. The method of claim 13 , wherein the applying comprises applying Karush-Kuhn optimality conditions and then formulating the quadratically constrained quadratic 0-1 program as a linear mixed integer 0-1 problem.
17. The method of claim 13 , wherein the identifying comprises minimizing statistical distances between mean values and/or other synchronization degrees of the calculated STLmax values.
18. The method of claim 13 , wherein the applying comprises comparing measures of statistical distance between mean values and/or other synchronization degrees of the calculated STLmax values.