Luther, Stefan

[["dc.bibliographiccitation.firstpage","157"],["dc.bibliographiccitation.lastpage","158"],["dc.contributor.author","Schlemmer, Alexander"],["dc.contributor.author","Zwirnmann, Henning"],["dc.contributor.author","Zabel, Markus"],["dc.contributor.author","Parlitz, Ulrich"],["dc.contributor.author","Luther, Stefan"],["dc.date.accessioned","2019-02-26T15:08:20Z"],["dc.date.available","2019-02-26T15:08:20Z"],["dc.date.issued","2014"],["dc.description.abstract","We evaluate several machine learning algorithms in the context of long-term prediction of cardiac diseases. Results from applying K Nearest Neighbors Classifiers (KNN), Support Vector Machines (SVM) and Random Forests (RF) to data from a cardiological long-term study suggests that multivariate methods can significantly improve classification results. SVMs were found to yield the best results in Matthews Correlation Coefficient and are most stable with respect to a varying number of features."],["dc.identifier.doi","10.1109/ESGCO.2014.6847567"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/57623"],["dc.identifier.url","https://sfb1002.med.uni-goettingen.de/production/literature/publications/53"],["dc.language.iso","en"],["dc.notes.status","fcwi"],["dc.publisher","IEEE"],["dc.publisher.place","Piscataway, NJ"],["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","8th Conference of the European Study Group on Cardiovascular Oscillations (ESGCO)"],["dc.relation.eventend","2014-05-28"],["dc.relation.eventlocation","Trento, Italy"],["dc.relation.eventstart","2014-05-25"],["dc.relation.isbn","978-1-4799-3969-5"],["dc.relation.isbn","978-1-4799-3968-8"],["dc.relation.isbn","978-1-4799-3970-1"],["dc.relation.ispartof","2014 8th Conference of the European Study Group on Cardiovascoular Oscillations (ESGCO 2014)"],["dc.relation.workinggroup","RG Luther (Biomedical Physics)"],["dc.title","Evaluation of Machine Learning Methods for the Long-Term Prediction of Cardiac Diseases"],["dc.type","conference_paper"],["dc.type.internalPublication","unknown"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","1561"],["dc.bibliographiccitation.issue","8"],["dc.bibliographiccitation.journal","Physiological Measurement"],["dc.bibliographiccitation.lastpage","1575"],["dc.bibliographiccitation.volume","38"],["dc.contributor.author","Schlemmer, Alexander"],["dc.contributor.author","Baig, T."],["dc.contributor.author","Luther, Stefan"],["dc.contributor.author","Parlitz, Ulrich"],["dc.date.accessioned","2020-12-10T18:15:46Z"],["dc.date.available","2020-12-10T18:15:46Z"],["dc.date.issued","2017"],["dc.identifier.doi","10.1088/1361-6579/aa7be0"],["dc.identifier.eissn","1361-6579"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/74950"],["dc.notes.intern","DOI Import GROB-354"],["dc.title","Detection and characterization of intermittent complexity variations in cardiac arrhythmia"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dspace.entity.type","Publication"]]

[["dc.contributor.author","Spreckelsen, Florian"],["dc.contributor.author","Rüchardt, Baltasar"],["dc.contributor.author","Lebert, Jan"],["dc.contributor.author","Luther, Stefan"],["dc.contributor.author","Parlitz, Ulrich"],["dc.contributor.author","Schlemmer, Alexander"],["dc.date.accessioned","2022-05-13T10:05:43Z"],["dc.date.available","2022-05-13T10:05:43Z"],["dc.date.issued","2020"],["dc.identifier.doi","10.20944/preprints202004.0035.v1"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/107869"],["dc.identifier.url","https://sfb1002.med.uni-goettingen.de/production/literature/publications/352"],["dc.relation","SFB 1002: Modulatorische Einheiten bei Herzinsuffizienz"],["dc.relation","SFB 1002 | C03: Erholung nach Herzinsuffizienz: Analyse der transmuralen mechano-elektrischen Funktionsstörung"],["dc.relation.workinggroup","RG Luther (Biomedical Physics)"],["dc.title","Guidelines for a Standardized File Structure for Scientific Data"],["dc.type","preprint"],["dc.type.internalPublication","unknown"],["dspace.entity.type","Publication"]]

[["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.artnumber","057201"],["dc.bibliographiccitation.issue","5"],["dc.bibliographiccitation.journal","Physical Review. E"],["dc.bibliographiccitation.volume","83"],["dc.contributor.author","Parlitz, Ulrich"],["dc.contributor.author","Schlemmer, Alexander"],["dc.contributor.author","Luther, Stefan"],["dc.date.accessioned","2018-11-07T08:56:06Z"],["dc.date.available","2018-11-07T08:56:06Z"],["dc.date.issued","2011"],["dc.description.abstract","Transient dynamics of spiral waves in a two-dimensional Barkley model is shown to be governed by pattern formation processes resulting in regions of synchronized activity separated by moving interfaces. During the transient the number of internally synchronized regions decreases as synchronization fronts move to the boundary of the simulated area. This spatiotemporal transient dynamics in an excitable medium is detected and visualized by means of an analysis of the local periodicity and by evaluation of prediction errors across the spatial domain. During the (long) transient both analyses show patterns that must not be misinterpreted as any information about (spatial) structure of the underlying (completely homogeneous) system."],["dc.identifier.doi","10.1103/PhysRevE.83.057201"],["dc.identifier.isi","000290899200003"],["dc.identifier.pmid","21728699"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/23059"],["dc.notes.status","zu prüfen"],["dc.notes.submitter","Najko"],["dc.publisher","Amer Physical Soc"],["dc.relation.issn","1550-2376"],["dc.relation.issn","1539-3755"],["dc.title","Synchronization patterns in transient spiral wave dynamics"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dc.type.status","published"],["dspace.entity.type","Publication"]]

[["dc.contributor.author","Fitschen, Timm"],["dc.contributor.author","Schlemmer, Alexander"],["dc.contributor.author","Hornung, Daniel"],["dc.contributor.author","tom Wörden, Henrik"],["dc.contributor.author","Parlitz, Ulrich"],["dc.contributor.author","Luther, Stefan"],["dc.date.accessioned","2022-05-13T10:03:07Z"],["dc.date.available","2022-05-13T10:03:07Z"],["dc.date.issued","2018-01-23"],["dc.description.abstract","Here we present CaosDB, a Research Data Management System (RDMS) designed to ensure seamless integration of inhomogeneous data sources and repositories of legacy data. Its primary purpose is the management of data from biomedical sciences, both from simulations and experiments during the complete research data lifecycle. An RDMS for this domain faces particular challenges: Research data arise in huge amounts, from a wide variety of sources, and traverse a highly branched path of further processing. To be accepted by its users, an RDMS must be built around workflows of the scientists and practices and thus support changes in workflow and data structure. Nevertheless it should encourage and support the development and observation of standards and furthermore facilitate the automation of data acquisition and processing with specialized software. The storage data model of an RDMS must reflect these complexities with appropriate semantics and ontologies while offering simple methods for finding, retrieving, and understanding relevant data. We show how CaosDB responds to these challenges and give an overview of the CaosDB Server, its data model and its easy-to-learn CaosDB Query Language. We briefly discuss the status of the implementation, how we currently use CaosDB, and how we plan to use and extend it."],["dc.identifier.arxiv","1801.07653"],["dc.identifier.doi","10.3390/data4020083"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/107868"],["dc.identifier.url","https://sfb1002.med.uni-goettingen.de/production/literature/publications/199"],["dc.relation","SFB 1002: Modulatorische Einheiten bei Herzinsuffizienz"],["dc.relation","SFB 1002 | C03: Erholung nach Herzinsuffizienz: Analyse der transmuralen mechano-elektrischen Funktionsstörung"],["dc.relation.workinggroup","RG Luther (Biomedical Physics)"],["dc.title","CaosDB - Research Data Management for Complex, Changing, and Automated Research Workflows"],["dc.type","preprint"],["dc.type.internalPublication","unknown"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","83"],["dc.bibliographiccitation.issue","2"],["dc.bibliographiccitation.journal","Data"],["dc.bibliographiccitation.volume","4"],["dc.contributor.author","Fitschen, Timm"],["dc.contributor.author","Schlemmer, Alexander"],["dc.contributor.author","Hornung, Daniel"],["dc.contributor.author","tom Wörden, Henrik"],["dc.contributor.author","Parlitz, Ulrich"],["dc.contributor.author","Luther, Stefan"],["dc.date.accessioned","2020-12-10T18:47:01Z"],["dc.date.available","2020-12-10T18:47:01Z"],["dc.date.issued","2019"],["dc.description.abstract","Here we present CaosDB, a Research Data Management System (RDMS) designed to ensure seamless integration of inhomogeneous data sources and repositories of legacy data. Its primary purpose is the management of data from biomedical sciences, both from simulations and experiments during the complete research data lifecycle. An RDMS for this domain faces particular challenges: Research data arise in huge amounts, from a wide variety of sources, and traverse a highly branched path of further processing. To be accepted by its users, an RDMS must be built around workflows of the scientists and practices and thus support changes in workflow and data structure. Nevertheless it should encourage and support the development and observation of standards and furthermore facilitate the automation of data acquisition and processing with specialized software. The storage data model of an RDMS must reflect these complexities with appropriate semantics and ontologies while offering simple methods for finding, retrieving, and understanding relevant data. We show how CaosDB responds to these challenges and give an overview of the CaosDB Server, its data model and its easy-to-learn CaosDB Query Language. We briefly discuss the status of the implementation, how we currently use CaosDB, and how we plan to use and extend it."],["dc.identifier.doi","10.3390/data4020083"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/16675"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/78611"],["dc.identifier.url","https://sfb1002.med.uni-goettingen.de/production/literature/publications/275"],["dc.language.iso","en"],["dc.notes","GRO-Red/ SW 8.5.2020: Geprüft. Keine Anpassung erforderlich, da Preprint im Volltext verfügbar."],["dc.notes.intern","DOI Import GROB-354"],["dc.notes.intern","Merged from goescholar"],["dc.notes.intern","Merged from goescholar"],["dc.notes.status","final"],["dc.relation","SFB 1002: Modulatorische Einheiten bei Herzinsuffizienz"],["dc.relation","SFB 1002 | C03: Erholung nach Herzinsuffizienz: Analyse der transmuralen mechano-elektrischen Funktionsstörung"],["dc.relation.eissn","2306-5729"],["dc.relation.orgunit","Fakultät für Physik"],["dc.relation.workinggroup","RG Luther (Biomedical Physics)"],["dc.rights","CC BY 4.0"],["dc.rights.uri","https://creativecommons.org/licenses/by/4.0/"],["dc.title","CaosDB—Research Data Management for Complex, Changing, and Automated Research Workflows"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.subtype","original_ja"],["dc.type.version","published_version"],["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"]]