Multifractal properties of heart rate by multifractal detrended fluctuation analysis and wavelet transform modulus maxima analysis—are both approaches equivalent?

Gałąska, Rafał; Makowiec, Danuta; Dudkowska, A; Koprowski, Andrzej; Fijałkowski, Marcin; Wdowczyk-Szulc, Joanna; Rynkiewicz, Andrzej

doi:10.1016/j.jelectrocard.2007.03.404

Cited by 5 publications

(2 citation statements)

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“…There have been successful applications of MF-DFA to geophysics and to the biology of ion current fluctuations [38]. In several head-to-head comparisons of MF-DFA with WTMM on the same datasets, both methods have been found to be reliable, though MF-DFA has tended to produce more consistent results [33], [39].…”

Section: Introductionmentioning

confidence: 99%

Multifractal Detrended Fluctuation Analysis of Human EEG: Preliminary Investigation and Comparison with the Wavelet Transform Modulus Maxima Technique

2013

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Recently, many lines of investigation in neuroscience and statistical physics have converged to raise the hypothesis that the underlying pattern of neuronal activation which results in electroencephalography (EEG) signals is nonlinear, with self-affine dynamics, while scalp-recorded EEG signals themselves are nonstationary. Therefore, traditional methods of EEG analysis may miss many properties inherent in such signals. Similarly, fractal analysis of EEG signals has shown scaling behaviors that may not be consistent with pure monofractal processes. In this study, we hypothesized that scalp-recorded human EEG signals may be better modeled as an underlying multifractal process. We utilized the Physionet online database, a publicly available database of human EEG signals as a standardized reference database for this study. Herein, we report the use of multifractal detrended fluctuation analysis on human EEG signals derived from waking and different sleep stages, and show evidence that supports the use of multifractal methods. Next, we compare multifractal detrended fluctuation analysis to a previously published multifractal technique, wavelet transform modulus maxima, using EEG signals from waking and sleep, and demonstrate that multifractal detrended fluctuation analysis has lower indices of variability. Finally, we report a preliminary investigation into the use of multifractal detrended fluctuation analysis as a pattern classification technique on human EEG signals from waking and different sleep stages, and demonstrate its potential utility for automatic classification of different states of consciousness. Therefore, multifractal detrended fluctuation analysis may be a useful pattern classification technique to distinguish among different states of brain function.

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Section: Introductionmentioning

confidence: 99%

Multifractal Detrended Fluctuation Analysis of Human EEG: Preliminary Investigation and Comparison with the Wavelet Transform Modulus Maxima Technique

2013

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“…The obtained exponent is similar to the Hurst exponent [ 16 ], except that DFA may also be applied to signals whose underlying statistics (such as mean and variance) or dynamics are non-stationary [ 17 – 20 ]. See the papers [ 19 , 21 – 24 ], for which DFA and EEG were applied.…”

Section: Methodsmentioning

confidence: 99%

Auto-correlation in the motor/imaginary human EEG signals: A vision about the FDFA fluctuations

2017

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In this paper we analyzed, by the FDFA root mean square fluctuation (rms) function, the motor/imaginary human activity produced by a 64-channel electroencephalography (EEG). We utilized the Physionet on-line databank, a publicly available database of human EEG signals, as a standardized reference database for this study. Herein, we report the use of detrended fluctuation analysis (DFA) method for EEG analysis. We show that the complex time series of the EEG exhibits characteristic fluctuations depending on the analyzed channel in the scalp-recorded EEG. In order to demonstrate the effectiveness of the proposed technique, we analyzed four distinct channels represented here by F332, F637 (frontal region of the head) and P349, P654 (parietal region of the head). We verified that the amplitude of the FDFA rms function is greater for the frontal channels than for the parietal. To tabulate this information in a better way, we define and calculate the difference between FDFA (in log scale) for the channels, thus defining a new path for analysis of EEG signals. Finally, related to the studied EEG signals, we obtain the auto-correlation exponent, αDFA by DFA method, that reveals self-affinity at specific time scale. Our results shows that this strategy can be applied to study the human brain activity in EEG processing.

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Multifractal Approach for Quantification of Autonomic Deregulation Due to Epileptic Seizure with ECG Data

Ghosh

Samanta²,

Chakraborty

2019

Multifractals and Chronic Diseases of the Central Nervous System

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Multifractal properties of heart rate by multifractal detrended fluctuation analysis and wavelet transform modulus maxima analysis—are both approaches equivalent?

Cited by 5 publications

References 0 publications

Multifractal Detrended Fluctuation Analysis of Human EEG: Preliminary Investigation and Comparison with the Wavelet Transform Modulus Maxima Technique

Multifractal Detrended Fluctuation Analysis of Human EEG: Preliminary Investigation and Comparison with the Wavelet Transform Modulus Maxima Technique

Auto-correlation in the motor/imaginary human EEG signals: A vision about the FDFA fluctuations

Multifractal Approach for Quantification of Autonomic Deregulation Due to Epileptic Seizure with ECG Data

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