PD Dr. rer. nat. Niels Wessel

PD Dr. Niels Wessel erforscht die Analyse und Vorhersage von Herzrhythmus- und autonomen Regulationsstörungen durch nicht-invasive Biosignalmessungen, insbesondere mittels Magnetokardiographie und Herzratenvariabilität. Sein aktueller Fokus liegt auf der Anwendung von Deep Learning und selbstüberwachtem Lernen zur Erkennung von Herzkrankheiten (Koronararterienerkrankung, Herzinsuffizienz, Arrhythmien) sowie auf der Analyse zirkadianer Rhythmen bei Schlafstörungen und deren Auswirkungen auf kardiovaskuläre Risiken. Die Methoden ermöglichen Kliniken und Medizingeräteherstellern verbesserte, strahlungsfreie Diagnostik und Früherkennung von Herzerkrankungen sowie die Entwicklung präventiver Interventionen (z. B. akustische Stimulation bei Schlafapnoe). Relevant sind die Arbeiten für Kardiologie, Schlafmedizin, medizinische Geräteentwicklung und klinische Diagnostik.

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• Lichtabhängige kardiovaskuläre Regulation bei Konzentration und Schmerz

  Quelle ↗

  Förderer: Bundesministerium für Wirtschaft und Energie Zeitraum: 09/2009 - 08/2012 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

• 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

• 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 · 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 · 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.