Selection of Phase Space Reconstruction Parameters for EMG Signals of the Uterus

Brzozowska, Ewelina; Borowska, Marta

doi:10.1515/slgr-2016-0046

Cited by 6 publications

(4 citation statements)

References 32 publications

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“…In literature, there are several studies have been conducted in various fields of study using chaotic approaches. For example, Brzozowska and Borowska (2016) have reconstructed phase space by using the Mutual Information Function method, False Nearest Neighbours method, and Cao's method for EHG signals of Uterus. In addition, B.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Wind Speed by Using Chaotic Approach: A Case Study in Istanbul

KARA

AYDIN

KARANFİL

et al. 2022

International Journal of Environment and Geoinformatics

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Renewable energy sources have gained great popularity due to the increasing importance given to a sustainable environment and economic development. Because of the environmental friendliness of renewable energy compared to fossil fuels, the tendency and investments in this field have increased. Wind energy comes into prominence among renewable energy sources because of its potential power that is used in various areas currently. Wind energy having stochastic nature is more sensitive to the extreme values of wind speed. Therefore, in order to create wind energy effectively, an accurate wind speed forecast is needed. In this study, nonlinear dynamical system approaches have been implemented by using reconstructing of phase space based on specifying minimum embedded dimension and delay time. In order to find out performance, different error metrics (MSE, RMSE, MAE, and MAPE) have been implemented. According to results, RMSE has been found 0.47 and 0.85 in hourly and daily dataset, respectively. Also, the correlation coefficient between the measurement and the obtained data set was as high as 0.92 in the hourly wind variable. In addition, a lesser correlation coefficient of 0.62 was found in the daily wind speed.

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Section: Introductionmentioning

confidence: 99%

Prediction of Wind Speed by Using Chaotic Approach: A Case Study in Istanbul

KARA

AYDIN

KARANFİL

et al. 2022

International Journal of Environment and Geoinformatics

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“…Methods from chaos theory and nonlinear dynamics give us the opportunity to study these complex biological signals, including entropy 6 , complexity indexes 7 and dimensional analysis 8 . Phase space reconstruction (PSR) 9,10 and dimensional analysis has been implemented to characterize the intrinsic nonlinear complexity within the time series signals 11–13 . Specifically, the phase space consists of a set of typical trajectories of the system, in which each point corresponds to one system state.…”

Section: Introductionmentioning

confidence: 99%

Quantification of Contractile Dynamic Complexities Exhibited by Human Stem Cell-Derived Cardiomyocytes Using Nonlinear Dimensional Analysis

Hoang

Jacquir

Lemus

et al. 2019

Sci Rep

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Understanding the complexity of biological signals has been gaining widespread attention due to increasing knowledge on the nonlinearity that exists in these systems. Cardiac signals are known to exhibit highly complex dynamics, consisting of high degrees of interdependency that regulate the cardiac contractile functions. These regulatory mechanisms are important to understand for the development of novel in vitro cardiac systems, especially with the exponential growth in deriving cardiac tissue directly from human induced pluripotent stem cells (hiPSCs). This work describes a unique analytical approach that integrates linear amplitude and frequency analysis of physical cardiac contraction, with nonlinear analysis of the contraction signals to measure the signals’ complexity. We generated contraction motion waveforms reflecting the physical contraction of hiPSC-derived cardiomyocytes (hiPSC-CMs) and implemented these signals to nonlinear analysis to compute the capacity and correlation dimensions. These parameters allowed us to characterize the dynamics of the cardiac signals when reconstructed into a phase space and provided a measure of signal complexity to supplement contractile physiology data. Thus, we applied this approach to evaluate drug response and observed that relationships between contractile physiology and dynamic complexity were unique to each tested drug. This illustrated the applicability of this approach in not only characterization of cardiac signals, but also monitoring and diagnostics of cardiac health in response to external stress.

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“…More specifically, a phase space reconstruction ( PSR ) method (Brzozowska & Borowska, 2016; Huffaker, 1997; Kliková & Raidl, 2011; Richter & Schreiber, 1998) was used to convert our biological dynamic system into a phase space consisting of a set of typical trajectories, in which each point is corresponding to a system state. PSR has previously been applied to analyze electrophysiological signals to identify arrhythmia both in vitro and in vivo .…”

Section: Introductionmentioning

confidence: 99%

“…To address this limitation, we took advantage of mathematical chaos theory to identify the nonlinear dynamic characteristics of hiPSC-CMs contractile motions recorded using optical flow and block-matching methods. More specifically, a phase space reconstruction (PSR) method (Brzozowska & Borowska, 2016;Huffaker, 2010;Kliková & Raidl, 2011;Richter & Schreiber, 1998) was used to convert our biological dynamic system into a phase space consisting of a set of typical trajectories, in which each point is corresponding to a system state. PSR has previously been applied to analyze electrophysiological signals to identify arrhythmia both in vitro and in vivo.…”

Section: Introductionmentioning

confidence: 99%

Quantitatively characterizing drug‐induced arrhythmic contractile motions of human stem cell‐derived cardiomyocytes

Hoang

Huebsch

Bang

et al. 2018

Biotech & Bioengineering

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Quantification of abnormal contractile motions of cardiac tissue has been a noteworthy challenge and significant limitation in assessing and classifying the drug-induced arrhythmias (i.e. Torsades de pointes). To overcome these challenges, researchers have taken advantage of computational image processing tools to measure contractile motion from cardiomyocytes derived from human induced pluripotent stem cells (hiPSC-CMs). However, the amplitude and frequency analysis of contractile motion waveforms doesn’t produce sufficient information to objectively classify the degree of variations between two or more sets of cardiac contractile motions. In this paper, we generated contractile motion data from beating hiPSC-CMs using motion tracking software based on optical flow analysis, and then implemented a computational algorithm, phase space reconstruction (PSR), to derive parameters (embedding, regularity, and fractal dimensions) to further characterize the dynamic nature of the cardiac contractile motions. Application of drugs known to cause cardiac arrhythmia induced significant changes to these resultant dimensional parameters calculated from PSR analysis. Integrating this new computational algorithm with the existing analytical toolbox of cardiac contractile motions will allow us to expand current assessments of cardiac tissue physiology into an automated, high-throughput, and quantifiable manner which will allow more objective assessments of drug-induced proarrhythmias.

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Selection of Phase Space Reconstruction Parameters for EMG Signals of the Uterus

Cited by 6 publications

References 32 publications

Prediction of Wind Speed by Using Chaotic Approach: A Case Study in Istanbul

Prediction of Wind Speed by Using Chaotic Approach: A Case Study in Istanbul

Quantification of Contractile Dynamic Complexities Exhibited by Human Stem Cell-Derived Cardiomyocytes Using Nonlinear Dimensional Analysis

Quantitatively characterizing drug‐induced arrhythmic contractile motions of human stem cell‐derived cardiomyocytes

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