Multifractal Detrended Fluctuation Analysis of Human EEG: Preliminary Investigation and Comparison with the Wavelet Transform Modulus Maxima Technique
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{"title"=>"Multifractal Detrended Fluctuation Analysis of Human EEG: Preliminary Investigation and Comparison with the Wavelet Transform Modulus Maxima Technique", "type"=>"journal", "authors"=>[{"first_name"=>"Todd", "last_name"=>"Zorick", "scopus_author_id"=>"35276750800"}, {"first_name"=>"Mark A.", "last_name"=>"Mandelkern", "scopus_author_id"=>"35227606200"}], "year"=>2013, "source"=>"PLoS ONE", "identifiers"=>{"pmid"=>"23844189", "doi"=>"10.1371/journal.pone.0068360", "sgr"=>"84879748080", "scopus"=>"2-s2.0-84879748080", "issn"=>"19326203", "pui"=>"369253454"}, "id"=>"6f6eba98-0346-322d-a8e2-530aadddd2b1", "abstract"=>"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.", "link"=>"http://www.mendeley.com/research/multifractal-detrended-fluctuation-analysis-human-eeg-preliminary-investigation-comparison-wavelet-t", "reader_count"=>47, "reader_count_by_academic_status"=>{"Unspecified"=>3, "Professor > Associate Professor"=>3, "Researcher"=>4, "Student > Doctoral Student"=>3, "Student > Ph. D. Student"=>7, "Student > Postgraduate"=>1, "Student > Master"=>14, "Other"=>1, "Student > Bachelor"=>4, "Lecturer"=>1, "Lecturer > Senior Lecturer"=>2, "Professor"=>3}, "reader_count_by_user_role"=>{"Unspecified"=>3, "Professor > Associate Professor"=>3, "Researcher"=>4, "Student > Doctoral Student"=>3, "Student > Ph. D. Student"=>7, "Student > Postgraduate"=>1, "Student > Master"=>14, "Other"=>1, "Student > Bachelor"=>4, "Lecturer"=>1, "Lecturer > Senior Lecturer"=>2, "Professor"=>3}, "reader_count_by_subject_area"=>{"Unspecified"=>8, "Engineering"=>8, "Biochemistry, Genetics and Molecular Biology"=>2, "Mathematics"=>1, "Agricultural and Biological Sciences"=>6, "Medicine and Dentistry"=>5, "Neuroscience"=>2, "Physics and Astronomy"=>2, "Psychology"=>5, "Chemistry"=>1, "Social Sciences"=>1, "Computer Science"=>5}, "reader_count_by_subdiscipline"=>{"Engineering"=>{"Engineering"=>8}, "Medicine and Dentistry"=>{"Medicine and Dentistry"=>5}, "Neuroscience"=>{"Neuroscience"=>2}, "Chemistry"=>{"Chemistry"=>1}, "Social Sciences"=>{"Social Sciences"=>1}, "Physics and Astronomy"=>{"Physics and Astronomy"=>2}, "Psychology"=>{"Psychology"=>5}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>6}, "Computer Science"=>{"Computer Science"=>5}, "Biochemistry, Genetics and Molecular Biology"=>{"Biochemistry, Genetics and Molecular Biology"=>2}, "Mathematics"=>{"Mathematics"=>1}, "Unspecified"=>{"Unspecified"=>8}}, "reader_count_by_country"=>{"Netherlands"=>1, "United States"=>2, "China"=>1, "Mexico"=>1, "India"=>1}, "group_count"=>3}

Scopus | Further Information

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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/1109686"], "description"=>"<p>For each stage from each subject, 1 min of EEG data was used to calculate MF-DFA spectra. Average MF-DFA spectra for each consciousness state shown here were calculated by averaging across individual spectrum values for each subject. Mean h values were then calculated for the h range, and differences between sleep stages compared by paired t testing. <b>*</b>p<0.05; <b>**</b>p<0.01. Significant differences were found for the waking-REM, Sleep 1-Sleep 2, and Sleep 2-Sleep 3 comparisons.</p>", "links"=>[], "tags"=>["Anatomy and physiology", "Physiological processes", "sleep", "biophysics", "Biophysics simulations", "Computational biology", "computational neuroscience", "signal processing", "Statistical signal processing", "electrophysiology", "Diagnostic medicine", "Clinical neurophysiology", "electroencephalography", "Medical physics", "Statistical mechanics", "stages", "eeg"], "article_id"=>738267, "categories"=>["Physics", "Medicine", "Engineering", "Biological Sciences"], "users"=>["Todd Zorick", "Mark A. Mandelkern"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0068360.g005", "stats"=>{"downloads"=>0, "page_views"=>8, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Comparison_among_stages_of_sleep_for_1_minute_of_EEG_data_/738267", "title"=>"Comparison among stages of sleep for 1 minute of EEG data.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-07-03 01:59:14"}
  • {"files"=>["https://ndownloader.figshare.com/files/1109685"], "description"=>"<p>For 14 subjects with 8 m of EEG from both waking and sleep stage 2 per subject, EEG was divided into 16 segments of 30 s each, and MF-DFA spectra were calculated for each segment (224 segments for each state of consciousness). Average MF-DFA spectra for each consciousness state shown here were calculated by averaging across individual spectrum values for each subject. **: p<0.001 for effect of state of consciousness by general linear modeling based on mean_h.</p>", "links"=>[], "tags"=>["Anatomy and physiology", "Physiological processes", "sleep", "biophysics", "Biophysics simulations", "Computational biology", "computational neuroscience", "signal processing", "Statistical signal processing", "electrophysiology", "Diagnostic medicine", "Clinical neurophysiology", "electroencephalography", "Medical physics", "Statistical mechanics", "mf-dfa", "spectra", "waking"], "article_id"=>738266, "categories"=>["Physics", "Medicine", "Engineering", "Biological Sciences"], "users"=>["Todd Zorick", "Mark A. Mandelkern"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0068360.g004", "stats"=>{"downloads"=>0, "page_views"=>14, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Comparison_between_MF_DFA_spectra_of_from_waking_and_sleep_stage_2_/738266", "title"=>"Comparison between MF-DFA spectra of from waking and sleep stage 2.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-07-03 01:59:14"}
  • {"files"=>["https://ndownloader.figshare.com/files/1109688"], "description"=>"<p>Individual subject data for 8 min waking EEG MF-DFA spectra.</p>", "links"=>[], "tags"=>["Anatomy and physiology", "Physiological processes", "sleep", "biophysics", "Biophysics simulations", "Computational biology", "computational neuroscience", "signal processing", "Statistical signal processing", "electrophysiology", "Diagnostic medicine", "Clinical neurophysiology", "electroencephalography", "Medical physics", "Statistical mechanics", "min", "waking", "eeg", "mf-dfa"], "article_id"=>738269, "categories"=>["Physics", "Medicine", "Engineering", "Biological Sciences"], "users"=>["Todd Zorick", "Mark A. Mandelkern"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0068360.t002", "stats"=>{"downloads"=>0, "page_views"=>15, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Individual_subject_data_for_8_min_waking_EEG_MF_DFA_spectra_/738269", "title"=>"Individual subject data for 8 min waking EEG MF-DFA spectra.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2013-07-03 01:59:14"}
  • {"files"=>["https://ndownloader.figshare.com/files/1109687"], "description"=>"<p>List of subject numbers and data utilized.</p>", "links"=>[], "tags"=>["Anatomy and physiology", "Physiological processes", "sleep", "biophysics", "Biophysics simulations", "Computational biology", "computational neuroscience", "signal processing", "Statistical signal processing", "electrophysiology", "Diagnostic medicine", "Clinical neurophysiology", "electroencephalography", "Medical physics", "Statistical mechanics", "numbers"], "article_id"=>738268, "categories"=>["Physics", "Medicine", "Engineering", "Biological Sciences"], "users"=>["Todd Zorick", "Mark A. Mandelkern"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0068360.t001", "stats"=>{"downloads"=>0, "page_views"=>3, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_List_of_subject_numbers_and_data_utilized_/738268", "title"=>"List of subject numbers and data utilized.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2013-07-03 01:59:14"}
  • {"files"=>["https://ndownloader.figshare.com/files/1109684"], "description"=>"<p>For 14 subjects with 8 m of EEG from waking data per subject, MF-DFA and WTMM spectra were calculated for each 8 m EEG series. Average multifractal spectra for each technique shown here were calculated by averaging individual spectra across subjects: mean_h±s.d. is 0.12±0.03 for MF-DFA, and 0.17±0.04 for WTMM.</p>", "links"=>[], "tags"=>["Anatomy and physiology", "Physiological processes", "sleep", "biophysics", "Biophysics simulations", "Computational biology", "computational neuroscience", "signal processing", "Statistical signal processing", "electrophysiology", "Diagnostic medicine", "Clinical neurophysiology", "electroencephalography", "Medical physics", "Statistical mechanics", "waking", "eeg", "mf-dfa"], "article_id"=>738265, "categories"=>["Physics", "Medicine", "Engineering", "Biological Sciences"], "users"=>["Todd Zorick", "Mark A. Mandelkern"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0068360.g003", "stats"=>{"downloads"=>0, "page_views"=>5, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Comparison_of_waking_EEG_between_MF_DFA_and_WTMM_/738265", "title"=>"Comparison of waking EEG between MF-DFA and WTMM.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-07-03 01:59:14"}
  • {"files"=>["https://ndownloader.figshare.com/files/1109682"], "description"=>"<p>Data points represent individual D(h) and h values from MF-DFA from a single time series of each type. <i>waking</i>: waking EEG (8 m, n = 120,000) from a single subject; <i>shuffled</i>: waking EEG with values shuffled prior to MF-DFA analysis; <i>BMS</i>: binomial multifractal series model with a = 0.6 (n = 120,000; Kantelhardt et al, 2002 <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0068360#pone.0068360-Kantelhardt2\" target=\"_blank\">[33]</a>); <i>LNS1</i>: log normal sigma 0.1 multifractal model data (n = 32,768; Arneodo et al 1998 <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0068360#pone.0068360-Arneodo2\" target=\"_blank\">[50]</a>; <a href=\"http://www.physionet.org/physiotools/multifractal/\" target=\"_blank\">http://www.physionet.org/physiotools/multifractal/</a>); <i>fbm2, 5, 7</i>; fractional Brownian motion monofractal models with Hb values of 0.2, 0.5, 0.7 as indicated (n = 120,000 each; dvfBm 1.0 R package).</p>", "links"=>[], "tags"=>["Anatomy and physiology", "Physiological processes", "sleep", "biophysics", "Biophysics simulations", "Computational biology", "computational neuroscience", "signal processing", "Statistical signal processing", "electrophysiology", "Diagnostic medicine", "Clinical neurophysiology", "electroencephalography", "Medical physics", "Statistical mechanics", "mf-dfa", "waking", "eeg", "numerical", "mono-", "multifractal"], "article_id"=>738263, "categories"=>["Physics", "Medicine", "Engineering", "Biological Sciences"], "users"=>["Todd Zorick", "Mark A. Mandelkern"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0068360.g001", "stats"=>{"downloads"=>0, "page_views"=>16, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Comparison_of_MF_DFA_spectrum_from_waking_EEG_to_numerical_models_of_mono_and_multifractal_processes_/738263", "title"=>"Comparison of MF-DFA spectrum from waking EEG to numerical models of mono- and multifractal processes.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-07-03 01:59:14"}
  • {"files"=>["https://ndownloader.figshare.com/files/1109683"], "description"=>"<p><b>A</b>. For 14 subjects with 8 m of waking EEG each, divided into 30 s segments (16 segments of n = 7500 data points each per subject), multifractal spectra were calculated (total of 224 segments). Mean Hölder exponent value (mean_h), width of the Hölder exponents (width_h), mean fractal dimension (mean_D(h)) value, and height of the multifractal singularity spectrum (height_D(h)) were calculated for each segment. <b>B</b>. The 14 subjects’ 8 m of waking EEG were analyzed whole, and multifractal specta were calculated. Note the trend to reduced variance with increasing length of EEG tracing.</p>", "links"=>[], "tags"=>["Anatomy and physiology", "Physiological processes", "sleep", "biophysics", "Biophysics simulations", "Computational biology", "computational neuroscience", "signal processing", "Statistical signal processing", "electrophysiology", "Diagnostic medicine", "Clinical neurophysiology", "electroencephalography", "Medical physics", "Statistical mechanics", "mf-dfa", "wtmm"], "article_id"=>738264, "categories"=>["Physics", "Medicine", "Engineering", "Biological Sciences"], "users"=>["Todd Zorick", "Mark A. Mandelkern"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0068360.g002", "stats"=>{"downloads"=>1, "page_views"=>9, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Variance_comparison_between_MF_DFA_and_WTMM_techniques_/738264", "title"=>"Variance comparison between MF-DFA and WTMM techniques.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-07-03 01:59:14"}

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  • {"unique-ip"=>"11", "full-text"=>"10", "pdf"=>"2", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"1", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2018", "month"=>"12"}
  • {"unique-ip"=>"10", "full-text"=>"10", "pdf"=>"4", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2019", "month"=>"2"}
  • {"unique-ip"=>"10", "full-text"=>"10", "pdf"=>"5", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2019", "month"=>"3"}
  • {"unique-ip"=>"13", "full-text"=>"10", "pdf"=>"7", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2019", "month"=>"4"}
  • {"unique-ip"=>"12", "full-text"=>"11", "pdf"=>"1", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2019", "month"=>"5"}

Relative Metric

{"start_date"=>"2013-01-01T00:00:00Z", "end_date"=>"2013-12-31T00:00:00Z", "subject_areas"=>[{"subject_area"=>"/Biology and life sciences", "average_usage"=>[269, 466, 588, 697, 800, 896, 988, 1076, 1165, 1254, 1340, 1417]}, {"subject_area"=>"/Biology and life sciences/Anatomy and physiology", "average_usage"=>[256, 428]}, {"subject_area"=>"/Computer and information sciences/Information technology", "average_usage"=>[265, 446, 547, 656, 751, 809, 898, 978, 1077, 1216, 1295, 1357, 1403, 1469]}, {"subject_area"=>"/Engineering and technology", "average_usage"=>[254, 440, 558, 675, 785, 883, 972, 1061, 1154, 1248, 1336, 1414, 1489]}, {"subject_area"=>"/Engineering and technology/Signal processing", "average_usage"=>[248, 406, 514, 603, 689, 787, 859, 930, 1027, 1083, 1131, 1201, 1270]}, {"subject_area"=>"/Medicine and health sciences/Physiology", "average_usage"=>[258, 449, 572, 679, 775, 866, 956, 1041, 1124, 1211, 1291, 1371, 1437]}]}
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