Michael Jachan

Joining the benefits: Combining epileptic seizure prediction methods

Purpose:  In recent years, a variety of methods developed in the field of linear and nonlinear time series analysis have been used to obtain reliable predictions of epileptic seizures. Because individual methods for seizure prediction so far have shown statistical significance but insufficient performance for clinical applications, we investigated possible improvements by combining algorithms capturing different aspects of electroencephalogram (EEG) dynamics.

Time-variant estimation of directed influences during Parkinsonian tremor

The inference of interaction structures in multidimensional time series is a major challenge not only in neuroscience but in many fields of research. To gather information about the connectivity in a network from measured data, several parametric as well as non-parametric approaches have been proposed and widely examined. Today a lot of interest is focused on the evolution of the network connectivity in time which might contain information about ongoing tasks in the brain or possible dynamic dysfunctions. Therefore an extension of the current approaches towards time-resolved analysis techniques is desired. We present a parametric approach for time variant analysis, test its performance for simulated data, and apply it to real-world data.

Approaches to the Detection of Direct Directed Interactions in Neuronal Networks

In this chapter, we address the challenge of detecting interactions among neuronal processes by means of bivariate and multivariate linear analysis techniques. For linear systems, both undirected and directed measures exist. Coherence is a commonly used undirected bivariate measure to detect the interaction between two nodes of a network, while multivariate measures like the partial coherence distinguish direct and indirect connections. The partial directed coherence additionally features the direction influences between nodes. We introduce the theoretical framework of these analysis techniques, discuss their estimation, and present their application to simulated and real-world data.

Probabilistic Forecasts of Epileptic Seizures and Evaluation by the Brier Score

We present a probabilistic epileptic seizure predictor and an according method for its statistical evaluation. The probabilistic predictor is based on a combination of feature channels, which are derived from the intracranial electroencephalogram (EEG), by a logistic regression map. The evaluation is done by the Brier score, an established assessment method in meteorology, which quantifies the prediction error. From the prediction features, the weights of the logistic regression are learned in a training phase and in a test phase the Brier score is assessed. A test for significance of the probabilistic predictor, based on seizure time surrogates, is computed. For 3 of 5 patients we obtained significant predictive power with the mean phase coherence and with the dynamical similarity index we obtained for 2 of the 5 patients significant results. The concept of probabilistic prediction can be a valuable tool for the development of future seizure intervention systems.