Potentialities of the wavelet and multifractal techniques to evaluate changes in the functional state of the human brain

Dick, O. E.; Svyatogor, I. A.

doi:10.1016/j.neucom.2011.11.013

Cited by 30 publications

(19 citation statements)

References 22 publications

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“…Recent calculations of the degree of finite-size effects to be expected in multifractal spectrum calculations for non-multifractal processes also support these results, in that we see a larger degree of multifractality than expected by finite-size effects alone [46]. Taking into account the results presented here, results from previously published data [35][36][37], and results from numerical simulations [24,33,46], these data support the hypothesis that human EEG can be successfully modeled as a multifractal process, which may provide additional insight into changes in brain neuronal dynamics associated with pathological states. To our knowledge, these results are the first to formally assess the suitability of multifractal methods to human EEG using simulations of multifractal and monofractal data.…”

Section: Discussionsupporting

confidence: 89%

“…MF-DFA results from human EEG are comparable to those from known multifractal processes, and appear to have a larger degree of multifractality than known monofractal processes (Figure 1; also cf. [35][36][37]). Recent calculations of the degree of finite-size effects to be expected in multifractal spectrum calculations for non-multifractal processes also support these results, in that we see a larger degree of multifractality than expected by finite-size effects alone [46].…”

Section: Discussionmentioning

confidence: 99%

“…WTMM has been utilized in human EEG to assess differences among stages of sleep [35,36], and among different psychiatric conditions [37], therefore showing evidence of its utility in human EEG research.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Multifractal detrended fluctuation analysis of human EEG: preliminary investigation and comparison with the wavelet transform modulus maxima technique

Zorick¹,

Mandelkern²

2013

PsycEXTRA Dataset

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Section: Discussionsupporting

confidence: 89%

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Multifractal detrended fluctuation analysis of human EEG: preliminary investigation and comparison with the wavelet transform modulus maxima technique

Zorick¹,

Mandelkern²

2013

PsycEXTRA Dataset

View full text Add to dashboard Cite

show abstract

“…MF-DFA results from human EEG are comparable to those from known multifractal processes, and appear to have a larger degree of multifractality than known monofractal processes (Figure 1; also cf. [35]–[37]). Recent calculations of the degree of finite-size effects to be expected in multifractal spectrum calculations for non-multifractal processes also support these results, in that we see a larger degree of multifractality than expected by finite-size effects alone [46].…”

Section: Discussionmentioning

confidence: 99%

“…WTMM has been utilized in human EEG to assess differences among stages of sleep [35], [36], and among different psychiatric conditions [37], therefore showing evidence of its utility in human EEG research.…”

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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Classification of Mammographic ROI for Microcalcification Detection Using Multifractal Approach

2022

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Microcalcifications (MCs) are the main signs of precancerous cells. The development of aided-system for their detection has become a challenge for researchers in this field. In this paper, we propose a system for MCs detection based on the multifractal approach that classifies mammographic ROIs into normal (healthy) or abnormal ROIs containing MCs. The proposed method is divided into four main steps: a mammogram pre-processing step based on breast selection, breast density reduction using haze removal algorithm and contrast enhancement using multifractal measures. The second step consists of extracting the normal and abnormal ROIs and calculating the multifractal spectrum of each ROI. The next step represents the extraction of the multifractal features from the multifractal spectrum and the GLCM characteristics of each ROI. The last step is the classification of ROIs where three classifiers are tested (KNN, DT, and SVM). The system is evaluated on images from the INbreast database (308 images) with a total of 2688 extracted ROIs (1344 normal, 1344 with MC) from different BI-RADS classes. In this study, the SVM classifier gave the best classification results with a sensitivity, specificity, and precision of 98.66%, 97.77%, and 98.20% respectively. These results are very satisfactory and remarkable compared to the literature.

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Potentialities of the wavelet and multifractal techniques to evaluate changes in the functional state of the human brain

Cited by 30 publications

References 22 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

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

Classification of Mammographic ROI for Microcalcification Detection Using Multifractal Approach

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