COMPUTATIONAL TOOL FOR PRE-SURGICAL EVALUATION OF PATIENTS WITH MEDICALLY REFRACTORY EPILEPSY

摘要

A method of identifying an epileptogenic zone of a brain includes receiving a plurality of electrical signals from a plurality of surgically implanted electrodes, calculating components of an adjacency matrix, calculating eigenvectors from the adjacency matrix, and selecting an eigenvector having a largest eigenvalue. The method includes assigning an integer rank to each component of the eigenvector, sliding the time window by a time increment and repeating the immediately preceding steps a plurality of times. The method includes normalizing each rank signal, extracting a multidimensional feature vector from each normalized signal, projecting each multidimensional feature vector onto a reduced dimensionality space, and receiving a plurality of training data points represented in the reduced dimensionality space. The method includes calculating grid weights for each feature vector, and assigning a numerical value to each electrode as an indication of whether the electrode is in an epileptogenic zone of the brain.

主权项

1. A method of identifying an epileptogenic zone of a subject's brain, comprising:
receiving a plurality N of electrical signals that extend over a seizure duration from a corresponding plurality N of surgically implanted electrodes in said subject's brain; calculating within a time window components of an N×N adjacency matrix between each pair of said plurality of surgically implanted electrodes based on at least a portion of each of said plurality N of electrical signals; calculating N eigenvectors from said N×N adjacency matrix; selecting an eigenvector of the N eigenvectors having a largest eigenvalue; assigning an integer rank from 1 to N to each component of the selected eigenvector to provide an N×1 rank vector corresponding to said time window; sliding said time window by a time increment and repeating the immediately preceding three steps for the incremented time window; repeating the immediately preceding step a plurality of times to provide said rank vector for a plurality of times, wherein each component of said rank vector corresponds to one of said N electrodes thus providing a rank signal for each of the N electrodes; normalizing each rank signal by the number of electrodes N and said seizure duration to provide corresponding normalized signals; extracting a multidimensional feature vector from each normalized signal to provide a plurality N of multidimensional feature vectors; projecting each of said plurality N of multidimensional feature vectors onto a reduced dimensionality space; receiving a plurality of training data points represented in said reduced dimensionality space, each of said plurality of training data points containing information of an electrode node regarding whether brain tissue corresponding to said electrode node was resected or non-resected brain tissue and whether a corresponding surgery was successful; calculating grid weights for each feature vector projected into said reduced dimensionality space using said training data points; and assigning a numerical value to each surgically implanted electrode using said grid weights as an indication of whether the corresponding surgically implanted electrode is in an epileptogenic zone of said subject's brain.