PD Dr. rer. nat. Niels Wessel

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Zusammenfassung

PD Dr. Niels Wessel analysiert Herzrhythmen und autonome kardiovaskuläre Regulation mittels nichtlinearer Methoden, um Risiken wie plötzlichen Herztod oder Schwangerschaftskomplikationen frühzeitig zu erkennen. Seine Expertise liegt in der Verarbeitung von Biosignalen (EKG, Herzratenvariabilität) durch komplexe mathematische Verfahren, um klinisch relevante Vorhersagen zu treffen. Diese Methoden ermöglichen es, subtile Veränderungen in Herzfunktion und autonomer Regulation zu erfassen, die mit konventionellen Verfahren übersehen werden.

Skills

Name

PD Dr. rer. nat. Niels Wessel

Titel

PD Dr. rer. nat.

Fakultät

Mathematisch-Naturwissenschaftliche Fakultät

Institut

Institut für Physik

Arbeitsgruppe

Nichtlineare Dynamik (S)

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HU-FIS-Profil

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Zuletzt gescrapt

27.6.2026, 01:16:30

• Biosignal 2010 (Veranstaltung: 14.-16.07.2010, Berlin)

  Quelle ↗

  Förderer: DFG sonstige Programme Zeitraum: 04/2010 - 10/2010 Projektleitung: PD Dr. rer. nat. Niels Wessel

• CAT-Kongress Biosignal 2010 (Veranstaltung: 14.-16.07.2010, Berlin)

  Quelle ↗

  Zeitraum: 04/2010 - 10/2010 Projektleitung: PD Dr. rer. nat. Niels Wessel

• Entwicklung einer Biofeedback-Software zur Beeinflussung der Herzratenvariabilität

  Quelle ↗

  Zeitraum: 12/2012 - 04/2013 Projektleitung: PD Dr. rer. nat. Niels Wessel

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

  2002

  Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics · 982 Zitationen · DOI

  The knowledge of transitions between regular, laminar or chaotic behaviors is essential to understand the underlying mechanisms behind complex systems. While several linear approaches are often insufficient to describe such processes, there are several nonlinear methods that, however, require rather long time observations. To overcome these difficulties, we propose measures of complexity based on vertical structures in recurrence plots and apply them to the logistic map as well as to heart-rate-variability data. For the logistic map these measures enable us not only to detect transitions between chaotic and periodic states, but also to identify laminar states, i.e., chaos-chaos transitions. The traditional recurrence quantification analysis fails to detect the latter transitions. Applying our measures to the heart-rate-variability data, we are able to detect and quantify the laminar phases before a life-threatening cardiac arrhythmia occurs thereby facilitating a prediction of such an event. Our findings could be of importance for the therapy of malignant cardiac arrhythmias.

• Quantitative analysis of heart rate variability

  1995

  Chaos An Interdisciplinary Journal of Nonlinear Science · 477 Zitationen · DOI

  In the modern industrialized countries every year several hundred thousands of people die due to sudden cardiac death. The individual risk for this sudden cardiac death cannot be defined precisely by common available, noninvasive diagnostic tools like Holter monitoring, highly amplified ECG and traditional linear analysis of heart rate variability (HRV). Therefore, we apply some rather unconventional methods of nonlinear dynamics to analyze the HRV. Especially, 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. A combination of these complexity measures with the parameters in the frequency domain seems to be a promising way to get a more precise definition of the individual risk. These findings have to be validated by a representative number of patients. (c) 1995 American Institute of Physics.

• The application of methods of non-linear dynamics for the improved and predictive recognition of patients threatened by sudden cardiac death

  1996

  Cardiovascular Research · 446 Zitationen · DOI

  OBJECTIVES: This study introduces new methods of non-linear dynamics (NLD) and compares these with traditional methods of heart rate variability (HRV) and high resolution ECG (HRECG) analysis in order to improve the reliability of high risk stratification. METHODS: Simultaneous 30 min high resolution ECG's and long-term ECG's were recorded from 26 cardiac patients after myocardial infarction (MI). They were divided into two groups depending upon the electrical risk, a low risk group (group 2, n = 10) and a high risk group (group 3, n = 16). The control group consisted of 35 healthy persons (group 1). From these electrocardiograms we extracted standard measures in time and frequency domain as well as measures from the new non-linear methods of symbolic dynamics and renormalized entropy. RESULTS: Applying discriminant function techniques on HRV analysis the parameters of non-linear dynamics led to an acceptable differentiation between healthy persons and high risk patients of 96%. The time domain and frequency domain parameters were successful in less than 90%. The combination of parameters from all domains and a stepwise discriminant function separated these groups completely (100%). Use of this discriminant function classified three patients with apparently low (no) risk into the same cluster as high risk patients. The combination of the HRECG and HRV analysis showed the same individual clustering but increased the positive value of separation. CONCLUSIONS: 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. This leads to an improved discrimination between a normal (healthy persons) and an abnormal (high risk patients) type of heart beat generation. Some patients with an unknown risk exhibit similar patterns to high risk patients and this suggests a hidden high risk. The methods of symbolic dynamics and renormalized entropy were particularly useful measures for classifying the dynamics of HRV.

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