HYBRID EVENT: You can participate in person at Tokyo, Japan or Virtually from your home or work.

International Heart Congress

May 24-25 | Hybrid Event

May 24 -25, 2023 | Tokyo, Japan
Heart Congress 2023

Measuring fractal dynamics of FECG signals to determine the complexity of fetal heart rate

Tahmineh Azizi, Speaker at Cardiovascular Diseases Events
University of Wisconsin, United States
Title : Measuring fractal dynamics of FECG signals to determine the complexity of fetal heart rate

Abstract:

In this research, we study the fetal heart rate from abdominal signals using multi-fractal spectra and fractal analysis. We use the Abdominal and Direct Fetal Electrocardiogram Database contains multichannel fetal electrocardiogram (FECG) recordings obtained from 5 different women in labor, between 38 and 41 weeks of gestation. We apply autocorrelation or power spectral densities (PSD) analysis on these five FECG recordings to estimate the exponent from realizations of these processes and to find out if the signal of interest exhibits a power-law PSD. We perform multifractal analysis to discover whether some type of power-law scaling exists for various statistical moments at different scales of these FECG signals. We plot the multi-fractal spectra of this database to compare the width of the scaling exponent for each spectrum. A quantitative analysis commonly known as the Fractal Dimension (FD) using the Higuchi algorithm has been carried out to illustrate the fractal complexity of input signals. Our finding shows that the fractal geometry can be used as a mathematical model and computational framework to further analysis and classification of clinical database. Moreover, it can be considered as a framework to compare the complexity of FECG signals and a useful tool to differentiate between their patterns.

Biography:

As someone who is highly inquisitive and analytical, I am skilled in computational mathematics and mathematical modeling for biological systems and also at developing appropriate model and implementing methodology using data collection and analyzing the results of research. I am currently a Research Associate at University of Wisconsin-Madison. My current work on developing new innovative methods in topological data analysis, computational anatomy and dynamic modelling of brain network using multiple time scale approaches is a neuroscience program in department of biostatistics and medical informatics at University of Wisconsin-Madison.As someone who is highly inquisitive and analytical, I am skilled in computational mathematics and mathematical modeling for biological systems and also at developing appropriate model and implementing methodology using data collection and analyzing the results of research. I am currently a Research Associate at University of Wisconsin-Madison. My current work on developing new innovative methods in topological data analysis, computational anatomy and dynamic modelling of brain network using multiple time scale approaches is a neuroscience program in department of biostatistics and medical informatics at University of Wisconsin-Madison.

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