Krefting, Dagmar

[["dc.bibliographiccitation.firstpage","959"],["dc.bibliographiccitation.issue","5"],["dc.bibliographiccitation.journal","Physiological Measurement"],["dc.bibliographiccitation.lastpage","975"],["dc.bibliographiccitation.volume","38"],["dc.contributor.author","Krefting, Dagmar"],["dc.contributor.author","Jansen, Christoph"],["dc.contributor.author","Penzel, Thomas"],["dc.contributor.author","Han, Fang"],["dc.contributor.author","Kantelhardt, Jan W."],["dc.date.accessioned","2021-09-17T10:19:03Z"],["dc.date.available","2021-09-17T10:19:03Z"],["dc.date.issued","2017-05"],["dc.description.abstract","Recently, time delay stability analysis of biosignals has been successfully applied as a multivariate time series analysis method to assess the human physiological network in young adults. The degree of connectivity between different network nodes is described by the so-called link strength. Based on polysomnographic recordings (PSGs), it could be shown that the network changes with the sleep stage. Here, we apply the method to a large set of healthy controls spanning six decades of age. As it is well known, that the overall sleep architecture is dependent both on age and on gender, we particularly address the question, if these changes are also found in the network dynamics. We find moderate dependencies of the network on gender. Significantly higher link strengths up to 13% are found in women for some links in different frequency bands of central and occipital regions in REM and light sleep (N2). Higher link strengths are found in men consistently in cardio-cerebral links in N2, but not significant. Age dependency is more pronounced. In particular a significant overall weakening of the network with age is found for wakefulness and non-REM sleep stages. The largest overall decrease is observed in N2 with 0.017 per decade. For individual links decrease rates up to 0.08 per decade are found, in particular for intra-brain links in non-REM sleep. Many of them show a significant decrease with age. Non-linear regression employing an artificial neural network can predict the age with a mean absolute error (MAE) of about five years, suggesting that an age-resolution of about a decade would be appropriate in normative data for physiological networks."],["dc.identifier.doi","10.1088/1361-6579/aa614e"],["dc.identifier.pmid","28212113"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/89655"],["dc.language.iso","en"],["dc.relation.eissn","1361-6579"],["dc.relation.issn","0967-3334"],["dc.title","Age and gender dependency of physiological networks in sleep"],["dc.type","journal_article"],["dc.type.internalPublication","unknown"],["dc.type.subtype","original_ja"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","123129"],["dc.bibliographiccitation.issue","12"],["dc.bibliographiccitation.journal","Chaos: an Interdisciplinary Journal of Nonlinear Science"],["dc.bibliographiccitation.volume","29"],["dc.contributor.author","Jansen, Christoph"],["dc.contributor.author","Penzel, Thomas"],["dc.contributor.author","Hodel, Stephan"],["dc.contributor.author","Breuer, Stefanie"],["dc.contributor.author","Spott, Martin"],["dc.contributor.author","Krefting, Dagmar"],["dc.date.accessioned","2020-04-03T14:34:57Z"],["dc.date.available","2020-04-03T14:34:57Z"],["dc.date.issued","2019-12"],["dc.description.abstract","Network physiology describes the human body as a complex network of interacting organ systems. It has been applied successfully to determine topological changes in different sleep stages. However, the number of network links can quickly grow above the number of parameters that are typically analyzed with standard statistical methods. Artificial Neural Networks (ANNs) are a promising approach as they are successful in large parameter spaces, such as in digital imaging. On the other hand, ANN models do not provide an intrinsic approach to interpret their predictions, and they typically require large training data sets. Both aspects are critical in biomedical research. Medical decisions need to be explainable, and large data sets of quality assured patient and control data are rare. In this paper, different models for the classification of insomnia-a common sleep disorder-have been trained with 59 patients and age and gender matched controls, based on their physiological networks. Feature relevance evaluation is employed for all methods. For ANNs, the extrinsic interpretation method DeepLift is applied. The results are not identical across methods, but certain network links have been rated as relevant by all or most of the models. While ANNs show less classification accuracy (0.89) than advanced tree-based models (0.92 and 0.93), DeepLift provides an in-depth ANN interpretation with feature relevance scores for individual data samples. The analysis revealed modifications in the pulmonar, ocular, and cerebral subnetworks that have not been described before but are consistent with known findings on the physiological impact of insomnia."],["dc.identifier.doi","10.1063/1.5128003"],["dc.identifier.pmid","31893662"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/63684"],["dc.language.iso","en"],["dc.relation","Research funded by Horizon 2020 Framework Programme (H2020-SC1-FA-DTS-2018-2020-826093) | Bundesministerium für Bildung und Forschung (3FH770IX613FH081N6) | Allianz Industrie Forschung (ZF4507601-B27)"],["dc.relation.eissn","1089-7682"],["dc.relation.issn","1054-1500"],["dc.relation.issn","1089-7682"],["dc.title","Network physiology in insomnia patients: Assessment of relevant changes in network topology with interpretable machine learning models"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dspace.entity.type","Publication"]]

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[["dc.bibliographiccitation.firstpage","466"],["dc.bibliographiccitation.journal","Future Generation Computer Systems"],["dc.bibliographiccitation.lastpage","480"],["dc.bibliographiccitation.volume","67"],["dc.contributor.author","Beier, Maximilian"],["dc.contributor.author","Jansen, Christoph"],["dc.contributor.author","Mayer, Geert"],["dc.contributor.author","Penzel, Thomas"],["dc.contributor.author","Rodenbeck, Andrea"],["dc.contributor.author","Siewert, René"],["dc.contributor.author","Witt, Michael"],["dc.contributor.author","Wu, Jie"],["dc.contributor.author","Krefting, Dagmar"],["dc.date.accessioned","2021-09-17T10:19:11Z"],["dc.date.available","2021-09-17T10:19:11Z"],["dc.date.issued","2017"],["dc.identifier.doi","10.1016/j.future.2016.03.025"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/89657"],["dc.relation.issn","0167-739X"],["dc.title","Multicenter data sharing for collaboration in sleep medicine"],["dc.type","journal_article"],["dc.type.internalPublication","unknown"],["dc.type.subtype","original_ja"],["dspace.entity.type","Publication"]]

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[["dc.bibliographiccitation.artnumber","124003"],["dc.bibliographiccitation.issue","12"],["dc.bibliographiccitation.journal","Physiological Measurement"],["dc.bibliographiccitation.volume","39"],["dc.contributor.author","Jansen, Christoph"],["dc.contributor.author","Hodel, Stephan"],["dc.contributor.author","Penzel, Thomas"],["dc.contributor.author","Spott, Martin"],["dc.contributor.author","Krefting, Dagmar"],["dc.date.accessioned","2021-09-17T10:18:53Z"],["dc.date.available","2021-09-17T10:18:53Z"],["dc.date.issued","2018"],["dc.description.abstract","Physiological networks (PN) model couplings between organs in a high-dimensional parameter space. Machine learning methods, in particular artifical neural networks (ANNs), are powerful on high-dimensional classification tasks. However, lack of interpretability of the resulting models has been a drawback in research. We assess relevant PN topology changes in obstructive sleep apnea (OSA) by novel ANN interpretation techniques."],["dc.identifier.doi","10.1088/1361-6579/aaf0c9"],["dc.identifier.pmid","30524083"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/89653"],["dc.language.iso","en"],["dc.relation.issn","1361-6579"],["dc.title","Feature relevance in physiological networks for classification of obstructive sleep apnea"],["dc.type","journal_article"],["dc.type.internalPublication","unknown"],["dc.type.subtype","original_ja"],["dspace.entity.type","Publication"]]