Berg, Sebastian

[["dc.bibliographiccitation.journal","Frontiers in Physiology"],["dc.bibliographiccitation.volume","11"],["dc.contributor.author","Kottlarz, Inga"],["dc.contributor.author","Berg, Sebastian"],["dc.contributor.author","Toscano-Tejeida, Diana"],["dc.contributor.author","Steinmann, Iris"],["dc.contributor.author","Bähr, Mathias"],["dc.contributor.author","Luther, Stefan"],["dc.contributor.author","Wilke, Melanie"],["dc.contributor.author","Parlitz, Ulrich"],["dc.contributor.author","Schlemmer, Alexander"],["dc.date.accessioned","2021-04-14T08:29:50Z"],["dc.date.available","2021-04-14T08:29:50Z"],["dc.date.issued","2021"],["dc.description.abstract","In this study, ordinal pattern analysis and classical frequency-based EEG analysis methods are used to differentiate between EEGs of different age groups as well as individuals. As characteristic features, functional connectivity as well as single-channel measures in both the time and frequency domain are considered. We compare the separation power of each feature set after nonlinear dimensionality reduction using t-distributed stochastic neighbor embedding and demonstrate that ordinal pattern-based measures yield results comparable to frequency-based measures applied to preprocessed data, and outperform them if applied to raw data. Our analysis yields no significant differences in performance between single-channel features and functional connectivity features regarding the question of age group separation."],["dc.identifier.doi","10.3389/fphys.2020.614565"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/82999"],["dc.language.iso","en"],["dc.notes.intern","DOI Import GROB-399"],["dc.publisher","Frontiers Media S.A."],["dc.relation.eissn","1664-042X"],["dc.rights","http://creativecommons.org/licenses/by/4.0/"],["dc.title","Extracting Robust Biomarkers From Multichannel EEG Time Series Using Nonlinear Dimensionality Reduction Applied to Ordinal Pattern Statistics and Spectral Quantities"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","4049"],["dc.bibliographiccitation.lastpage","4052"],["dc.contributor.author","Schlemmer, Alexander"],["dc.contributor.author","Berg, Sebastian"],["dc.contributor.author","Shajahan, T. K."],["dc.contributor.author","Luther, Stefan"],["dc.contributor.author","Parlitz, Ulrich"],["dc.date.accessioned","2019-02-27T15:58:02Z"],["dc.date.available","2019-02-27T15:58:02Z"],["dc.date.issued","2015"],["dc.description.abstract","Analyzing the dynamics of complex excitation wave patterns in cardiac tissue plays a key role for understanding the origin of life-threatening arrhythmias and for devising novel approaches to control them. The quantification of spatiotemporal complexity, however, remains a challenging task. This holds in particular for the analysis of data from fluorescence imaging (optical mapping), which allows for the measurement of membrane potential and intracellular calcium at high spatial and temporal resolution. Hitherto methods, like dominant frequency maps and the analysis of phase singularities, address important aspects of cardiac dynamics, but they consider very specific properties of excitable media, only. This article focuses on the benchmark of spatial complexity measures over time in the context of cardiac cell cultures. Standard Shannon Entropy and Spatial Permutation Entropy, an adaption of [1], have been implemented and applied to optical mapping data from embryonic chicken cell culture experiments. We introduce spatial separation of samples when generating ordinal patterns and show its importance for Spatial Permutation Entropy. Results suggest that Spatial Permutation Entropies provide a robust and interpretable measure for detecting qualitative changes in the dynamics of this excitable medium."],["dc.identifier.doi","10.1109/EMBC.2015.7319283"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/57653"],["dc.identifier.url","https://sfb1002.med.uni-goettingen.de/production/literature/publications/145"],["dc.language.iso","en"],["dc.notes.status","final"],["dc.publisher","IEEE"],["dc.relation","SFB 1002: Modulatorische Einheiten bei Herzinsuffizienz"],["dc.relation","SFB 1002 | C03: Erholung nach Herzinsuffizienz: Analyse der transmuralen mechano-elektrischen Funktionsstörung"],["dc.relation.conference","37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)"],["dc.relation.eventend","2015-08-29"],["dc.relation.eventlocation","Milan, Italy"],["dc.relation.eventstart","2015-08-25"],["dc.relation.isbn","978-1-4244-9270-1"],["dc.relation.ispartof","2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)"],["dc.relation.workinggroup","RG Luther (Biomedical Physics)"],["dc.title","Quantifying spatiotemporal complexity of cardiac dynamics using ordinal patterns"],["dc.type","conference_paper"],["dc.type.internalPublication","unknown"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.journal","Frontiers in Physics"],["dc.bibliographiccitation.volume","6"],["dc.contributor.author","Schlemmer, Alexander"],["dc.contributor.author","Berg, Sebastian"],["dc.contributor.author","Lilienkamp, Thomas"],["dc.contributor.author","Luther, Stefan"],["dc.contributor.author","Parlitz, Ulrich"],["dc.date.accessioned","2020-12-10T18:44:37Z"],["dc.date.available","2020-12-10T18:44:37Z"],["dc.date.issued","2018"],["dc.description.abstract","Permutation entropy (PE) is a robust quantity for measuring the complexity of time series. In the cardiac community it is predominantly used in the context of electrocardiogram (ECG) signal analysis for diagnoses and predictions with a major application found in heart rate variability parameters. In this article we are combining spatial and temporal PE to form a spatiotemporal PE that captures both, complexity of spatial structures and temporal complexity at the same time. We demonstrate that the spatiotemporal PE (STPE) quantifies complexity using two datasets from simulated cardiac arrhythmia and compare it to phase singularity analysis and spatial PE (SPE). These datasets simulate ventricular fibrillation (VF) on a two-dimensional and a three-dimensional medium using the Fenton-Karma model. We show that SPE and STPE are robust against noise and demonstrate its usefulness for extracting complexity features at different spatial scales."],["dc.identifier.doi","10.3389/fphy.2018.00039"],["dc.identifier.eissn","2296-424X"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/78528"],["dc.language.iso","en"],["dc.notes.intern","DOI Import GROB-354"],["dc.publisher","Frontiers Media S.A."],["dc.relation.eissn","2296-424X"],["dc.rights","http://creativecommons.org/licenses/by/4.0/"],["dc.title","Spatiotemporal Permutation Entropy as a Measure for Complexity of Cardiac Arrhythmia"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","950"],["dc.bibliographiccitation.issue","3"],["dc.bibliographiccitation.journal","Entropy"],["dc.bibliographiccitation.lastpage","967"],["dc.bibliographiccitation.volume","17"],["dc.contributor.author","Schlemmer, Alexander"],["dc.contributor.author","Berg, Sebastian"],["dc.contributor.author","Shajahan, T. K."],["dc.contributor.author","Luther, Stefan"],["dc.contributor.author","Parlitz, Ulrich"],["dc.date.accessioned","2018-11-07T10:00:06Z"],["dc.date.available","2018-11-07T10:00:06Z"],["dc.date.issued","2015"],["dc.description.abstract","The characterization of spatiotemporal complexity remains a challenging task. This holds in particular for the analysis of data from fluorescence imaging (optical mapping), which allows for the measurement of membrane potential and intracellular calcium at high spatial and temporal resolutions and, therefore, allows for an investigation of cardiac dynamics. Dominant frequency maps and the analysis of phase singularities are frequently used for this type of excitable media. These methods address some important aspects of cardiac dynamics; however, they only consider very specific properties of excitable media. To extend the scope of the analysis, we present a measure based on entropy rates for determining spatiotemporal complexity patterns of excitable media. Simulated data generated by the Aliev-Panfilov model and the cubic Barkley model are used to validate this method. Then, we apply it to optical mapping data from monolayers of cardiac cells from chicken embryos and compare our findings with dominant frequency maps and the analysis of phase singularities. The studies indicate that entropy rate maps provide additional information about local complexity, the origins of wave breakup and the development of patterns governing unstable wave propagation."],["dc.identifier.doi","10.3390/e17030950"],["dc.identifier.isi","000352612500003"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/11891"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/37726"],["dc.identifier.url","https://sfb1002.med.uni-goettingen.de/production/literature/publications/83"],["dc.notes.intern","Merged from goescholar"],["dc.notes.status","zu prüfen"],["dc.notes.submitter","Najko"],["dc.relation","info:eu-repo/grantAgreement/EC/FP7/241526/EU//EUTRIGTREAT"],["dc.relation","SFB 1002: Modulatorische Einheiten bei Herzinsuffizienz"],["dc.relation","SFB 1002 | C03: Erholung nach Herzinsuffizienz: Analyse der transmuralen mechano-elektrischen Funktionsstörung"],["dc.relation.issn","1099-4300"],["dc.relation.orgunit","Fakultät für Physik"],["dc.relation.workinggroup","RG Luther (Biomedical Physics)"],["dc.rights","CC BY 4.0"],["dc.title","Entropy Rate Maps of Complex Excitable Dynamics in Cardiac Monolayers"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dc.type.subtype","original_ja"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]