Bivariate and Multivariate Time Series Analysis Techniques and their Potential Impact for Seizure Prediction

Osterhage, Hannes; Bialonski, Stephan; Staniek, Matthäus; Schindler, Kaspar; Wagner, Tobias; Elger, Christian E.; Lehnertz, Klaus

doi:10.1002/9783527625192.ch15

Cited by 2 publications

(2 citation statements)

References 82 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…These observations indicate the SOZ to be a driving brain region (cf. [30,31,78,79]) with interactions that are not only local but involve large parts of the epileptic network.…”

Section: Measuring Strength and Direction Of Interactions In The Human Epileptic Brainmentioning

confidence: 99%

Assessing directionality and strength of coupling through symbolic analysis: an application to epilepsy patients

Lehnertz

Dickten

2015

Phil. Trans. R. Soc. A.

Self Cite

View full text Add to dashboard Cite

Inferring strength and direction of interactions from electroencephalographic (EEG) recordings is of crucial importance to improve our understanding of dynamical interdependencies underlying various physiological and pathophysiological conditions in the human epileptic brain. We here use approaches from symbolic analysis to investigate—in a time-resolved manner—weighted and directed, short- to long-ranged interactions between various brain regions constituting the epileptic network. Our observations point to complex spatial–temporal interdependencies underlying the epileptic process and their role in the generation of epileptic seizures, despite the massive reduction of the complex information content of multi-day, multi-channel EEG recordings through symbolization. We discuss limitations and potential future improvements of this approach.

show abstract

“…These observations indicate the SOZ to be a driving brain region (cf. [30,31,78,79]) with interactions that are not only local but involve large parts of the epileptic network.…”

Section: Measuring Strength and Direction Of Interactions In The Human Epileptic Brainmentioning

confidence: 99%

Assessing directionality and strength of coupling through symbolic analysis: an application to epilepsy patients

Lehnertz

Dickten

2015

Phil. Trans. R. Soc. A.

Self Cite

View full text Add to dashboard Cite

show abstract

“…A different approach bases on an evaluation of the significance of predictive information of measure profiles. When investigating synchronization phenomena in the epileptic brain (Mormann et al, 2005 ; Schelter et al, 2007 ; Osterhage et al, 2008 ; Lehnertz et al, 2009 ; Mirowski et al, 2009 ; Kuhlmann et al, 2010 ), however, a time-resolved analysis of interactions between all possible pairs of recording sites results in a large number of measure profiles. A significance evaluation will inevitably lead to the statistical problem of multiple testing.…”

Section: Introductionmentioning

confidence: 99%

Identification of Preseizure States in Epilepsy: A Data-Driven Approach for Multichannel EEG Recordings

Feldwisch-Drentrup¹,

Staniek²,

Schulze‐Bonhage³

et al. 2011

Front. Comput. Neurosci.

Self Cite

View full text Add to dashboard Cite

The retrospective identification of preseizure states usually bases on a time-resolved characterization of dynamical aspects of multichannel neurophysiologic recordings that can be assessed with measures from linear or non-linear time series analysis. This approach renders time profiles of a characterizing measure – so-called measure profiles – for different recording sites or combinations thereof. Various downstream evaluation techniques have been proposed to single out measure profiles that carry potential information about preseizure states. These techniques, however, rely on assumptions about seizure precursor dynamics that might not be generally valid or face the statistical problem of multiple testing. Addressing these issues, we have developed a method to preselect measure profiles that carry potential information about preseizure states, and to identify brain regions associated with seizure precursor dynamics. Our data-driven method is based on the ratio S of the global to local temporal variance of measure profiles. We evaluated its suitability by retrospectively analyzing long-lasting multichannel intracranial EEG recordings from 18 patients that included 133 focal onset seizures, using a bivariate measure for the strength of interactions. In 17/18 patients, we observed S to be significantly correlated with the predictive performance of measure profiles assessed retrospectively by means of receiver-operating-characteristic statistics. Predictive performance was higher for measure profiles preselected with S than for a manual selection using information about onset and spread of seizures. Across patients, highest predictive performance was not restricted to recordings from focal areas, thus supporting the notion of an extended epileptic network in which even distant brain regions contribute to seizure generation. We expect our method to provide further insight into the complex spatial and temporal aspects of the seizure generating process.

show abstract

Bivariate and Multivariate Time Series Analysis Techniques and their Potential Impact for Seizure Prediction

Cited by 2 publications

References 82 publications

Assessing directionality and strength of coupling through symbolic analysis: an application to epilepsy patients

Assessing directionality and strength of coupling through symbolic analysis: an application to epilepsy patients

Identification of Preseizure States in Epilepsy: A Data-Driven Approach for Multichannel EEG Recordings

Contact Info

Product

Resources

About