Ariane Schad

Testing statistical significance of multivariate time series analysis techniques for epileptic seizure prediction

Nonlinear time series analysis techniques have been proposed to detect changes in the electroencephalography dynamics prior to epileptic seizures. Their applicability in practice to predict seizure onsets is hampered by the present lack of generally accepted standards to assess their performance. We propose an analytic approach to judge the prediction performance of multivariate seizure prediction methods. Statistical tests are introduced to assess patient individual results, taking into account that prediction methods are applied to multiple time series and several seizures. Their performance is illustrated utilizing a bivariate seizure prediction method based on synchronization theory.

Do False Predictions of Seizures Depend on the State of Vigilance? A Report from Two Seizure-Prediction Methods and Proposed Remedies

Summary:  Purpose: Available seizure‐prediction algorithms are accompanied by high numbers of false predictions to achieve high sensitivity. Little is known about the extent to which changes in EEG dynamics contribute to false predictions. This study addresses potential causes and the circadian distribution of false predictions as well as their relation to the sleep–wake cycle.

Application of a multivariate seizure detection and prediction method to non-invasive and intracranial long-term EEG recordings

OBJECTIVE Retrospective evaluation and comparison of performances of a multivariate method for seizure detection and prediction on simultaneous long-term EEG recordings from scalp and intracranial electrodes. METHODS Two multivariate techniques based on simulated leaky integrate-and-fire neurons were investigated in order to detect and predict seizures. Both methods were applied and assessed on 423h of EEG and 26 seizures in total, recorded simultaneously from the scalp and intracranially continuously over several days from six patients with pharmacorefractory epilepsy. RESULTS Features generated from simultaneous scalp and intracranial EEG data showed a similar dynamical behavior. Significant performances with sensitivities of up to 73%/62% for scalp/invasive EEG recordings given an upper limit of 0.15 false detections per hour were obtained. Up to 59%/50% of all seizures could be predicted from scalp/invasive EEG, given a maximum number of 0.15 false predictions per hour. A tendency to better performances for scalp EEG was obtained for the detection algorithm. CONCLUSIONS The investigated methods originally developed for non-invasive EEG were successfully applied to intracranial EEG. Especially, concerning seizure detection the method shows a promising performance which is appropriate for practical applications in EEG monitoring. Concerning seizure prediction a significant prediction performance is indicated and a modification of the method is suggested. SIGNIFICANCE This study evaluates simultaneously recorded non-invasive and intracranial continuous long-term EEG data with respect to seizure detection and seizure prediction for the first time.

Spatio-temporal patient–individual assessment of synchronization changes for epileptic seizure prediction

OBJECTIVE Abnormal synchronization of neurons plays a central role for the generation of epileptic seizures. Therefore, multivariate time series analysis techniques investigating relationships between the dynamics of different neural populations may offer advantages in predicting epileptic seizures. METHODS We applied a phase and a lag synchronization measure to a selected subset of multicontact intracranial EEG recordings and assessed changes in synchronization with respect to seizure prediction. RESULTS Patient individual results, group results, spatial aspects using focal and extra-focal electrode contacts as well as two evaluation schemes analyzing decreases and increases in synchronization were examined. Averaged sensitivity values of 60% are observed for a false prediction rate of 0.15 false predictions per hour, a seizure occurrence period of half an hour, and a prediction horizon of 10 min. For approximately half of all 21 patients, a statistically significant prediction performance is observed for at least one synchronization measure and evaluation scheme. CONCLUSIONS The results indicate that synchronization changes in the EEG dynamics preceding seizures can be used for seizure prediction. Nevertheless, the underlying pathogenic mechanisms differ and both decreases and increases in synchronization may precede epileptic seizures depending on the structures investigated. SIGNIFICANCE The prediction method, optimized values of intervention times, as well as preferred brain structures for the EEG recordings have to be determined for each patient individually offering the chance of a better patient-individual prediction performance.

Global Helioseismic Evidence for a Deeply Penetrating Solar Meridional Flow Consisting of Multiple Flow Cells

We use a novel global helioseismic analysis method to infer the meridional flow in the deep Solar interior. The method is based on the perturbation of eigenfunctions of Solar p modes due to meridional flow. We apply this method to time series obtained from Dopplergrams measured by the Michelson Doppler Imager aboard the Solar and Heliospheric Observatory covering the observation period 2004-2010. Our results show evidence that the meridional flow reaches down to the base of the convection zone. The flow profile has a complex spatial structure consisting of multiple flow cells distributed in depth and latitude. Toward the Solar surface, our results are in good agreement with flow measurements from local helioseismology.

A UNIFIED APPROACH TO THE HELIOSEISMIC INVERSION PROBLEM OF THE SOLAR MERIDIONAL FLOW FROM GLOBAL OSCILLATIONS

Measurements from tracers and local helioseismology indicate the existence of a meridional flow in the Sun with strength in the order of 15 m s −1 near the solar surface. Different attempts were made to obtain information on the flow profile at depths up to 20 Mm below the solar surface. We propose a method using global helioseismic Doppler measurements with the prospect of inferring the meridional flow profile at greater depths. Our approach is based on the perturbation of the p-mode eigenfunctions of a solar model due to the presence of a flow. The distortion of the oscillation eigenfunctions is manifested in the mixing of p-modes, which may be measured from global solar oscillation time series. As a new helioseismic measurement quantity, we propose amplitude ratios between oscillations in the Fourier domain. We relate this quantity to the meridional flow and unify the concepts presented here for an inversion procedure to infer the meridional flow from global solar oscillations.

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.

Observation of oscillation coupling ratios and the meridional flow

Measurements from local helioseismology indicate the existence of a meridional flow in the Sun with a strength up to 15 m/s near the solar surface. The flow profile at depths below ≈ 15 Mm are not accessible. We propose a method using global helioseismic measurements with the prospect to infer the meridional flow profile throughout the solar convection zone and show its performance on simulated data.

The Direct Effect of Toroidal Magnetic Fields on Stellar Oscillations: An Analytical Expression for the General Matrix Element

Where is the solar dynamo located and what is its modus operandi? These are still open questions in solar physics. Helio- and asteroseismology can help answer them by enabling us to study solar and stellar internal structures through global oscillations. The properties of solar and stellar acoustic modes are changing with the level of magnetic activity. However, until now, the inference on subsurface magnetic fields with seismic measures has been very limited. The aim of this paper is to develop a formalism to calculate the effect of large-scale toroidal magnetic fields on solar and stellar global oscillation eigenfunctions and eigenfrequencies. If the Lorentz force is added to the equilibrium equation of motion, stellar eigenmodes can couple. In quasi-degenerate perturbation theory, this coupling, also known as the direct effect, can be quantified by the general matrix element. We present the analytical expression of the matrix element for a superposition of subsurface zonal toroidal magnetic field configurations. The matrix element is important for forward calculations of perturbed solar and stellar eigenfunctions and frequency perturbations. The results presented here will help to ascertain solar and stellar large-scale subsurface magnetic fields, and their geometric configuration, strength, and their change over the course of activity cycles.

Cross-spectral analysis of solar oscillation time series

Spatial leakage is an unavoidable artifact in the extraction of solar oscillation modes by spheric harmonic decomposition from the wavefield observed on the solar surface. The determination of solar frequencies by spectral analysis is therefore greatly affected by spatial leakage. Here we show in which way spatial leakage also influences the cross-spectra between different solar oscillation modes. Simulations show that spatial leakage induces significant coherences between oscillations of degree I and I + 2 with low azimuthal order m.