Karin Schiecke

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For the past decade, the detection and quantification of interactions within and between physiological networks has become a priority-in-common between the fields of biomedicine and computer science. Prominent examples are the interaction analysis of brain networks and of the cardiovascular-respiratory system. The aim of the study is to show how and to what(More)
The major aim of our study is to demonstrate that a concerted combination of time-variant, frequency-selective, linear and nonlinear analysis approaches can be beneficially used for the analysis of heart rate variability (HRV) in epileptic patients to reveal premonitory information regarding an imminent seizure and to provide more information on the(More)
A processing scheme for the investigation of neonatal electroencephalographic burst oscillations that is composed of time-variant methods for linear and nonlinear phase analysis is introduced. Starting from a time-frequency analysis of oscillations' amplitudes, time-variant approaches for quantification of phase locking, n:m phase synchronization, and(More)
Quantification of functional connectivity in physiological networks is frequently performed by means of time-variant partial directed coherence (tvPDC), based on time-variant multivariate autoregressive models. The principle advantage of tvPDC lies in the combination of directionality, time variance and frequency selectivity simultaneously, offering a more(More)
Major aim of our study is to demonstrate that signal-adaptive approaches improve the nonlinear and time-variant analysis of heart rate variability (HRV) of children with temporal lobe epilepsy (TLE). Nonlinear HRV analyses are frequently applied in epileptic patients. As HRV is characterized by components with oscillatory properties frequency-selective(More)
OBJECTIVE Principle aim of this study is to investigate the performance of a matching pursuit (MP)-based bispectral analysis in the detection and quantification of quadratic phase couplings (QPC) in biomedical signals. Nonlinear approaches such as time-variant bispectral analysis are able to provide information about phase relations between oscillatory(More)
This article is part of a For-Discussion-Section of Methods of Information in Medicine about the paper "Computational Electrocardiography: Revisiting Holter ECG Monitoring" written by Thomas M. Deserno and Nikolaus Marx. It is introduced by an editorial. This article contains the combined commentaries invited to independently comment on the paper of Deserno(More)
OBJECTIVES Empirical mode decomposition (EMD) is a frequently used signal processing approach which adaptively decomposes a signal into a set of narrow-band components known as intrinsic mode functions (IMFs). For multi-trial, multivariate (multiple simultaneous recordings), and multi-subject analyses the number and signal properties of the IMFs can deviate(More)
In neuroscience, data are typically generated from neural network activity. Complex interactions between measured time series are involved, and nothing or only little is known about the underlying dynamic system. Convergent Cross Mapping (CCM) provides the possibility to investigate nonlinear causal interactions between time series by using nonlinear state(More)