Dynamic Electrical Source Imaging (DESI) of Seizures and Interictal Epileptic Discharges Without Ensemble Averaging
Abstract: We propose an algorithm for electrical source imaging of epileptic discharges that takes a data-driven driven approach to regularizing the dynamics of solutions. The method is based on linear system identification on short time segments, combined with a classical inverse solution approach. Whereas ensemble averaging of segments or epochs discards inter inter-segment segment variations by averaging across them, our approach explicitly models them. Indeed, it may even be possible to avoid the need for the time time-consuming process off marking epochs containing discharges altogether. We demonstrate that this approach can produce both stable and accurate inverse solutions in experiments using simulated data and real data from epilepsy patients. In an illustrative example, we show that wee are able to image propagation using this approach. We show that when applied to imaging seizure data, our approach reproducibly localized frequent seizure activity to within the margins of surgeries that led to patients' seizure freedom. The same approac approach h could be used in the planning of epilepsy surgeries, as a way to localize potentially epileptogenic tissue that should be resected.