Classification and regression of stenosis using an in-vitro pulse wave data set: Dependence on heart rate, waveform and location.

Journal: Computers in biology and medicine

PMID: 36327886

Abstract

BACKGROUND: Data-based approaches promise to use the information in cardiovascular signals to diagnose cardiovascular diseases. Considerable effort has been undertaken in the field of pulse-wave analysis to harness this information. However, the inverse problem, inferring arterial properties from waveform measurements, is not well understood today. Consequently, uncertainties within the estimation hinder the diagnostic application of such methods.

Authors

Alexander Mair

Technische Hochschule Mittelhessen, Department Life Science Engineering, Wiesenstrasse 14, 35390 Gießen, Germany.
Michelle Wisotzki

Technische Hochschule Mittelhessen, Department Life Science Engineering, Wiesenstrasse 14, 35390 Gießen, Germany.
Stefan Bernhard

Technische Hochschule Mittelhessen, Department Life Science Engineering, Wiesenstrasse 14, 35390 Gießen, Germany; Freie Universität Berlin, Institute of Mathematics, Berlin, Germany. Electronic address: stefan.bernhard@lse.thm.de.

Keywords

Arteries Blood Pressure Constriction, Pathologic Heart Rate Humans Neural Networks, Computer Pulse Wave Analysis

External Resources

View on PubMed Access via DOI PubMed (36327886)

Classification and regression of stenosis using an in-vitro pulse wave data set: Dependence on heart rate, waveform and location.

Abstract

Authors

Keywords

External Resources

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