Characterization and classification of intracardiac atrial fibrillation signals using the time-singularity multifractal spectrum distribution

Urda-Benitez, Robert D.; Castro-Ospina, Andrés Eduardo; Duque, Andrés

doi:10.1016/j.cnsns.2020.105675

Cited by 3 publications

(1 citation statement)

References 20 publications

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“…As well, MF-DFA can realize the effective analysis of social phenomena and events [17,18]. In addition, MF-DFA has been widely used in other sequence processing [19][20][21][22]. Wang et al [23] applied MF-DFA to the classification of ECG signals.…”

Section: Introductionmentioning

confidence: 99%

Modified MF-DFA Model Based on LSSVM Fitting

Wang,

Zhong,

Yue

et al. 2024

Fractal Fract

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This paper proposes a multifractal least squares support vector machine detrended fluctuation analysis (MF-LSSVM-DFA) model. The system is an extension of the traditional MF-DFA model. To address potential overfitting or underfitting caused by the fixed-order polynomial fitting in MF-DFA, LSSVM is employed as a superior alternative for fitting. This approach enhances model accuracy and adaptability, ensuring more reliable analysis results. We utilize the p model to construct a multiplicative cascade time series to evaluate the performance of MF-LSSVM-DFA, MF-DFA, and two other models that improve upon MF-DFA from recent studies. The results demonstrate that our proposed modified model yields generalized Hurst exponents h(q) and scaling exponents τ(q) that align more closely with the analytical solutions, indicating superior correction effectiveness. In addition, we explore the sensitivity of MF-LSSVM-DFA to the overlapping window size s. We find that the sensitivity of our proposed model is less than that of MF-DFA. We find that when s exceeds the limited range of the traditional MF-DFA, h(q) and τ(q) are closer than those obtained in MF-DFA when s is in a limited range. Meanwhile, we analyze the performances of the fitting of the two models and the results imply that MF-LSSVM-DFA achieves a better outstanding performance. In addition, we put the proposed MF-LSSVM-DFA into practice for applications in the medical field, and we found that MF-LSSVM-DFA improves the accuracy of ECG signal classification and the stability and robustness of the algorithm compared with MF-DFA. Finally, numerous image segmentation experiments are adopted to verify the effectiveness and robustness of our proposed method.

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Section: Introductionmentioning

confidence: 99%

Modified MF-DFA Model Based on LSSVM Fitting

Wang,

Zhong,

Yue

et al. 2024

Fractal Fract

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Vibration discrimination based upon multifractal spectrum and improved probabilistic neural network in the dual Mach–Zehnder interferometric perimeter system

Xiong

et al. 2022

Opt Rev

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Testing for Intrinsic Multifractality in the Global Grain Spot Market Indices: A Multifractal Detrended Fluctuation Analysis

WANG,

GAO,

ZHOU

2023

Fractals

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Grains account for more than 50% of the calories consumed by people worldwide, and military conflicts, pandemics, climate change, and soaring grain prices all have vital impacts on food security. However, the complex price behavior of the global grain spot markets has not been well understood. A recent study performed multifractal moving average analysis (MF-DMA) of the Grains & Oilseeds Index (GOI) and its sub-indices of wheat, maize, soybeans, rice, and barley and it was found that only the maize and barley sub-indices exhibit an intrinsic multifractal nature with convincing evidence. Here, we utilize multifractal fluctuation analysis (MF-DFA) to investigate the same problem. Extensive statistical tests confirm the presence of intrinsic multifractality in the maize and barley sub-indices and the absence of intrinsic multifractality in the wheat and rice sub-indices. Different from the MF-DMA results, the MF-DFA results suggest that there is also intrinsic multifractality in the GOI and soybeans sub-indices. Our comparative analysis does not provide conclusive information about the GOI and soybeans and highlights the high complexity of the global grain spot markets.

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Characterization and classification of intracardiac atrial fibrillation signals using the time-singularity multifractal spectrum distribution

Cited by 3 publications

References 20 publications

Modified MF-DFA Model Based on LSSVM Fitting

Modified MF-DFA Model Based on LSSVM Fitting

Vibration discrimination based upon multifractal spectrum and improved probabilistic neural network in the dual Mach–Zehnder interferometric perimeter system

Testing for Intrinsic Multifractality in the Global Grain Spot Market Indices: A Multifractal Detrended Fluctuation Analysis

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