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Niels Wessel

Researcher at Humboldt University of Berlin

Publications -  213
Citations -  7045

Niels Wessel is an academic researcher from Humboldt University of Berlin. The author has contributed to research in topics: Heart rate variability & Heart rate. The author has an hindex of 37, co-authored 210 publications receiving 6342 citations. Previous affiliations of Niels Wessel include Charité & Potsdam Institute for Climate Impact Research.

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Recurrence-plot-based measures of complexity and their application to heart-rate-variability data.

TL;DR: Applying measures of complexity based on vertical structures in recurrence plots and applying them to the logistic map as well as to heart-rate-variability data is able to detect and quantify the laminar phases before a life-threatening cardiac arrhythmia occurs thereby facilitating a prediction of such an event.
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The application of methods of non-linear dynamics for the improved and predictive recognition of patients threatened by sudden cardiac death

TL;DR: The methods of NLD describe complex rhythm fluctuations and separate structures of non-linear behavior in the heart rate time series more successfully than classical methods of time and frequency domains, leading to an improved discrimination between a normal (healthy persons) and an abnormal (high risk patients) type of heart beat generation.
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Quantitative analysis of heart rate variability.

TL;DR: Some complexity measures that are based on symbolic dynamics as well as a new measure, the renormalized entropy, detect some abnormalities in the HRV of several patients who have been classified in the low risk group by traditional methods.
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Practical considerations of permutation entropy - A tutorial review

TL;DR: A brief introduction topermutation entropy is given, the different fields of utilization where permutation entropy has been applied are explored, and a guide on how to choose appropriate parameters for different applications of permutations entropy is provided.
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Nonlinear analysis of complex phenomena in cardiological data

TL;DR: It is demonstrated that parameters from nonlinear dynamics are useful for risk stratification after myocardial infarction, for the prediction of life-threatening cardiac events even in short time series, and for modelling the relationship between heart rate and blood pressure regulation.