Fuzzy Generalized Fractal Dimensions Using Inter-Heartbeat Interval Dynamics in ECG Signals for Age  Related Discrimination

Easwaramoorthy, D.; Jeevaraj, P. S. Eliahim; Gowrisankar, A.; Manimaran, A.; Nandhini, S.

doi:10.14419/ijet.v7i4.10.26784

Cited by 4 publications

(5 citation statements)

References 15 publications

(25 reference statements)

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“…This section concisely presents the necessary materials of the correlation dimension. Based on the Renyi entropy, the correlation dimension (correlation fractal dimension) of a fractal time series is defined by constructing the probability distribution as follows [ 19 – 28 ].…”

Section: Mathematical Methodsmentioning

confidence: 99%

“…As the fractal and multifractal analyses have been applied to time series and experimental signals, the dimensional measures have mainly been used to analyse the change in the chaotic nature in different physiopathological conditions and to estimate the complexity in forecasting process [ 14 – 18 ]. Among all the nonlinear schemes, the correlation fractal dimension measurement is more accessible in dealing with diffusion data with more complexity in nature for prediction [ 19 – 28 ]. Due to an uncertainty in the spread of coronavirus, the empirical data of the number of infected cases and death cases are being analysed based on the power law kinetics with fractal exponent, which gives a best fit to the contemporary data than the traditional epidemiology method.…”

Section: Introductionmentioning

confidence: 99%

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An exploration of fractal-based prognostic model and comparative analysis for second wave of COVID-19 diffusion

et al. 2021

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The coronavirus disease 2019 (COVID-19) pandemic has fatalized 216 countries across the world and has claimed the lives of millions of people globally. Researches are being carried out worldwide by scientists to understand the nature of this catastrophic virus and find a potential vaccine for it. The most possible efforts have been taken to present this paper as a form of contribution to the understanding of this lethal virus in the first and second wave. This paper presents a unique technique for the methodical comparison of disastrous virus dissemination in two waves amid five most infested countries and the death rate of the virus in order to attain a clear view on the behaviour of the spread of the disease. For this study, the data set of the number of deaths per day and the number of infected cases per day of the most affected countries, the USA, Brazil, Russia, India, and the UK, have been considered in the first and second waves. The correlation fractal dimension has been estimated for the prescribed data sets of COVID-19, and the rate of death has been compared based on the correlation fractal dimension estimate curve. The statistical tool, analysis of variance, has also been used to support the performance of the proposed method. Further, the prediction of the daily death rate has been demonstrated through the autoregressive moving average model. In addition, this study also emphasis a feasible reconstruction of the death rate based on the fractal interpolation function. Subsequently, the normal probability plot is portrayed for the original data and the predicted data, derived through the fractal interpolation function to estimate the accuracy of the prediction. Finally, this paper neatly summarized with the comparison and prediction of epidemic curve of the first and second waves of COVID-19 pandemic to visualize the transmission rate in the both times.

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Section: Mathematical Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An exploration of fractal-based prognostic model and comparative analysis for second wave of COVID-19 diffusion

et al. 2021

Self Cite

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“…Based on the Renyi entropy, the correlation dimension (correlation fractal dimension) of a fractal time series is defined by constructing the probability distribution as follows [19]- [28].…”

Section: Correlation Fractal Dimensionmentioning

confidence: 99%

“…As the fractal and multifractal analysis have been applied to time series and experimental signals, the dimensional measures have mainly been used to analyze the change in the chaotic nature in different physiopathological conditions and to estimate the complexity in forecasting process [14,15,16,17,18]. Among all the nonlinear schemes, the correlation fractal dimension measurement is more accessible in dealing with diffusion data with more complexity in nature for prediction [19]- [28]. Due to an uncertainty in the spread of coronavirus, the empirical data of the number of infected cases and death cases is being analyzed based on the power law kinetics with fractal exponent which gives a best fit to the contemporary data than the traditional epidemiology method.…”

Section: Introductionmentioning

confidence: 99%

An Exploration of Fractal Based Prognostic Model and Comparative Analysis for Second Wave of COVID-19 Diffusion

Easwaramoorthy

Gowrisankar

Manimaran

et al. 2021

Preprint

View full text Add to dashboard Cite

The coronavirus disease 2019 (COVID-19) pandemic has fatalized 216 countries across the world and has claimed the lives of millions of people globally. Researches are being carried out worldwide by scientists to understand the nature of this catastrophic virus and find a potential vaccine for it. The most possible efforts have been taken to present this paper as a form of contribution to the understanding of this lethal virus in the first and second wave. This paper presents a unique technique for the methodical comparison of disastrous virus dissemination in two waves amid five most infested countries and the death rate of the virus in order to attain a clear view on the behaviour of the spread of the disease. For this study, the dataset of the number of deaths per day and the number of infected cases per day of the most affected countries, The United States of America, Brazil, Russia, India, and The United Kingdom have been considered in first and second wave. The correlation fractal dimension has been estimated for the prescribed datasets of COVID-19 and the rate of death has been compared based on the correlation fractal dimension estimate curve. The statistical tool, analysis of variance has also been used to support the performance of the proposed method. Further, the prediction of the daily death rate has been demonstrated through the autoregressive moving average model. In addition, this study also emphasis a feasible reconstruction of the death rate based on the fractal interpolation function. Subsequently, the normal probability plot is portrayed for the original data and the predicted data, derived through the fractal interpolation function to estimate the accuracy of the prediction. Finally, this paper neatly summarized with the comparison and prediction of epidemic curve of the first and second waves of COVID-19 pandemic to picturize the transmission rate in the both times.

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“…Such an inhomogeneous set is called a Multifractal and is characterized by the Generalized Fractal Dimensions (GFD) or Renyi Fractal Dimensions. Therefore, the generalized fractal dimension is used to analyze, characterize and estimate the dynamics of complex signals and images [7][8][9][10][11][12][13][14][15][16][17][18][19][20][21][22][23][24][25].…”

Section: Introductionmentioning

confidence: 99%

Multifractal based image processing for estimating the complexity of COVID-19 dynamics

Rong

Thangaraj

Easwaramoorthy

et al. 2021

Eur. Phys. J. Spec. Top.

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The COVID-19 pandemic creates a worldwide threat to human health, medical practitioners, social structures, and finance sectors. The coronavirus epidemic has a significant impact on people's health, survival, employment, and financial crises; while also having noticeable harmful effects on our environment in a short span of time. In this context, the complexity of the Corona Virus transmission is estimated and analyzed by the measure of non-linearity called the Generalized Fractal Dimensions (GFD) on the chest X-Ray images. Grayscale image is considered as the most important suitable tool in the medical image processing. Particularly, COVID-19 affects the human lungs vigorously within a few days. It is a very challenging task to differentiate the COVID-19 infections from the various respiratory diseases represented in this study. The multifractal dimension measure is calculated for the original, noisy and denoised images to estimate the robustness of COVID-19 and other noticeable diseases. Also the comparison of COVID-19 X-Ray images is performed graphically with the images of healthy and other diseases to state the level of complexity of diseases in terms of GFD curves. In addition, the Mean Absolute Error (MAE) and the Peak Signal-to-Noise Ratio (PSNR) are used to evaluate the performance of the denoising process involved in the proposed comparative analysis of the representative grayscale images.

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Fuzzy Generalized Fractal Dimensions Using Inter-Heartbeat Interval Dynamics in ECG Signals for Age Related Discrimination

Cited by 4 publications

References 15 publications

An exploration of fractal-based prognostic model and comparative analysis for second wave of COVID-19 diffusion

An exploration of fractal-based prognostic model and comparative analysis for second wave of COVID-19 diffusion

An Exploration of Fractal Based Prognostic Model and Comparative Analysis for Second Wave of COVID-19 Diffusion

Multifractal based image processing for estimating the complexity of COVID-19 dynamics

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