Computational methods of EEG signals analysis for Alzheimer’s disease classification

Computational analysis of electroencephalographic (EEG) signals have shown promising results in detecting brain disorders, such as Alzheimer’s disease (AD). AD is a progressive neurological illness that causes neuron cells degeneration, resulting in cognitive impairment. While there is no cure for A...

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Veröffentlicht in:Scientific reports Jg. 13; H. 1; S. 8184 - 14
Hauptverfasser: Vicchietti, Mário L., Ramos, Fernando M., Betting, Luiz E., Campanharo, Andriana S. L. O.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: London Nature Publishing Group UK 20.05.2023
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Aged
Algorithms
Alzheimer Disease - diagnosis
Alzheimer's disease
Biomarkers
Brain research
Cognitive ability
Cognitive Dysfunction - diagnosis
Computational neuroscience
Dementia
EEG
Electroencephalography
Electroencephalography - methods
Entropy
Fourier transforms
Humanities and Social Sciences
Humans
multidisciplinary
Neurodegeneration
Neurodegenerative diseases
Quality of Life
Science
Science (multidisciplinary)
Time series
ISSN:2045-2322, 2045-2322
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Abstract Computational analysis of electroencephalographic (EEG) signals have shown promising results in detecting brain disorders, such as Alzheimer’s disease (AD). AD is a progressive neurological illness that causes neuron cells degeneration, resulting in cognitive impairment. While there is no cure for AD, early diagnosis is critical to improving the quality of life of affected individuals. Here, we apply six computational time-series analysis methods (wavelet coherence, fractal dimension, quadratic entropy, wavelet energy, quantile graphs and visibility graphs) to EEG records from 160 AD patients and 24 healthy controls. Results from raw and wavelet-filtered (alpha, beta, theta and delta bands) EEG signals show that some of the time-series analysis methods tested here, such as wavelet coherence and quantile graphs, can robustly discriminate between AD patients from elderly healthy subjects. They represent a promising non-invasive and low-cost approach to the AD detection in elderly patients.
AbstractList Computational analysis of electroencephalographic (EEG) signals have shown promising results in detecting brain disorders, such as Alzheimer's disease (AD). AD is a progressive neurological illness that causes neuron cells degeneration, resulting in cognitive impairment. While there is no cure for AD, early diagnosis is critical to improving the quality of life of affected individuals. Here, we apply six computational time-series analysis methods (wavelet coherence, fractal dimension, quadratic entropy, wavelet energy, quantile graphs and visibility graphs) to EEG records from 160 AD patients and 24 healthy controls. Results from raw and wavelet-filtered (alpha, beta, theta and delta bands) EEG signals show that some of the time-series analysis methods tested here, such as wavelet coherence and quantile graphs, can robustly discriminate between AD patients from elderly healthy subjects. They represent a promising non-invasive and low-cost approach to the AD detection in elderly patients.Computational analysis of electroencephalographic (EEG) signals have shown promising results in detecting brain disorders, such as Alzheimer's disease (AD). AD is a progressive neurological illness that causes neuron cells degeneration, resulting in cognitive impairment. While there is no cure for AD, early diagnosis is critical to improving the quality of life of affected individuals. Here, we apply six computational time-series analysis methods (wavelet coherence, fractal dimension, quadratic entropy, wavelet energy, quantile graphs and visibility graphs) to EEG records from 160 AD patients and 24 healthy controls. Results from raw and wavelet-filtered (alpha, beta, theta and delta bands) EEG signals show that some of the time-series analysis methods tested here, such as wavelet coherence and quantile graphs, can robustly discriminate between AD patients from elderly healthy subjects. They represent a promising non-invasive and low-cost approach to the AD detection in elderly patients.
Computational analysis of electroencephalographic (EEG) signals have shown promising results in detecting brain disorders, such as Alzheimer’s disease (AD). AD is a progressive neurological illness that causes neuron cells degeneration, resulting in cognitive impairment. While there is no cure for AD, early diagnosis is critical to improving the quality of life of affected individuals. Here, we apply six computational time-series analysis methods (wavelet coherence, fractal dimension, quadratic entropy, wavelet energy, quantile graphs and visibility graphs) to EEG records from 160 AD patients and 24 healthy controls. Results from raw and wavelet-filtered (alpha, beta, theta and delta bands) EEG signals show that some of the time-series analysis methods tested here, such as wavelet coherence and quantile graphs, can robustly discriminate between AD patients from elderly healthy subjects. They represent a promising non-invasive and low-cost approach to the AD detection in elderly patients.
Abstract Computational analysis of electroencephalographic (EEG) signals have shown promising results in detecting brain disorders, such as Alzheimer’s disease (AD). AD is a progressive neurological illness that causes neuron cells degeneration, resulting in cognitive impairment. While there is no cure for AD, early diagnosis is critical to improving the quality of life of affected individuals. Here, we apply six computational time-series analysis methods (wavelet coherence, fractal dimension, quadratic entropy, wavelet energy, quantile graphs and visibility graphs) to EEG records from 160 AD patients and 24 healthy controls. Results from raw and wavelet-filtered (alpha, beta, theta and delta bands) EEG signals show that some of the time-series analysis methods tested here, such as wavelet coherence and quantile graphs, can robustly discriminate between AD patients from elderly healthy subjects. They represent a promising non-invasive and low-cost approach to the AD detection in elderly patients.
ArticleNumber 8184
Author Campanharo, Andriana S. L. O.
Ramos, Fernando M.
Betting, Luiz E.
Vicchietti, Mário L.
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  surname: Vicchietti
  fullname: Vicchietti, Mário L.
  organization: Department of Biodiversity and Biostatistics, Institute of Biosciences, São Paulo State University
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  givenname: Fernando M.
  surname: Ramos
  fullname: Ramos, Fernando M.
  organization: National Institute for Space Research, Earth System Science Center
– sequence: 3
  givenname: Luiz E.
  surname: Betting
  fullname: Betting, Luiz E.
  organization: Department of Neurology, Psychology and Psychiatry, Botucatu Medical School, São Paulo State University
– sequence: 4
  givenname: Andriana S. L. O.
  surname: Campanharo
  fullname: Campanharo, Andriana S. L. O.
  email: andriana.campanharo@unesp.br
  organization: Department of Biodiversity and Biostatistics, Institute of Biosciences, São Paulo State University
BackLink https://www.ncbi.nlm.nih.gov/pubmed/37210397$$D View this record in MEDLINE/PubMed
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Cites_doi 10.1016/0013-4694(94)90033-7
10.1016/S0140-6736(20)30367-6
10.1371/journal.pone.0231169
10.1159/000507078
10.1016/j.physa.2015.09.094
10.1016/j.neuroimage.2018.10.003
10.1371/journal.pone.0023378
10.1038/s41598-019-46633-7
10.1111/j.1365-2796.2004.01388.x
10.1097/WAD.0b013e3181ed1160
10.1073/pnas.0709247105
10.1016/S0987-7053(01)00254-4
10.1371/journal.pone.0193607
10.1016/j.medengphy.2008.06.010
10.1037/0735-7044.110.6.1235
10.21608/mjeer.2019.64927
10.1142/3823
10.1016/j.clinph.2004.01.001
10.1177/155005940503600303
10.1159/000107183
10.1109/TBME.2019.2898871
10.1016/0167-8760(84)90014-X
10.1016/j.neunet.2019.12.006
10.4097/kjae.2017.70.1.22
10.1016/j.patrec.2020.03.009
10.1016/j.nic.2005.09.005
10.1016/S0013-4694(97)00129-6
10.1016/j.procs.2016.09.236
10.1152/ajpheart.00561.2010
10.1007/s10959-005-7541-3
10.1212/WNL.65.6_suppl_3.S10
10.1007/s00702-010-0450-3
10.1016/j.cmpb.2017.02.002
10.1016/j.neurobiolaging.2006.06.006
10.1007/s11571-018-9501-5
10.1007/978-981-13-0908-3
10.1097/NEN.0b013e318232a379
10.1063/1.4927835
10.2174/156720510792231720
10.1016/j.neunet.2020.06.018
10.1016/0010-4825(88)90041-8
10.1049/htl.2014.0106
10.1007/s11571-014-9325-x
10.1088/0967-3334/27/3/003
10.3389/fneur.2015.00025
10.1038/nn.4497
10.3390/e18010008
10.1201/b16046
10.2741/4587
10.1016/j.jocn.2018.11.027
10.2214/ajr.184.2.01840364
10.1016/j.neuroimage.2019.05.048
10.1007/s11063-018-9936-z
10.1016/j.clinph.2020.03.003
10.1111/psyp.12283
10.1007/s13246-015-0333-x
10.1007/s10916-008-9219-8
10.1016/j.jneumeth.2010.01.029
10.1016/0167-2789(88)90081-4
10.1016/j.jneumeth.2005.10.009
10.1109/CCSP.2005.4977207
10.1145/1543834.1543860
10.1155/2018/8915079
10.11113/jt.v72.3886
10.1152/ajpheart.2000.278.6.H2039
10.1137/1.9781611970104
10.1007/978-3-319-59147-6_9
10.1155/2014/349249
10.1109/IEMBS.2010.5627621
10.1007/978-3-030-20521-8_10
10.21203/rs.3.rs-2371230/v1
10.1109/EMBC.2015.7320104
10.1007/978-981-13-0908-3_2
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References Braak, Thal, Ghebremedhin, Del Tredici (CR8) 2011; 70
Sharma, Patel, Acharya (CR12) 2020; 133
Diks (CR57) 1999
Cohen (CR49) 2019; 199
Chandrasekhar, Dimri, Gadre (CR55) 2013
Lopes, Perani, Yaakub, Richardson, Goodfellow, Terry (CR27) 2019; 9
Gómez, Mediavilla, Hornero, Abásolo, Fernández (CR58) 2009; 31
CR36
Jeong, Kim, Song, Chung, Jeong (CR56) 2016; 18
Chavan, Kolte (CR50) 2011; 3
CR76
CR74
Van der Hiele (CR15) 2007; 28
CR71
Ieracitano, Mammone, Hussain, Morabito (CR10) 2020; 123
Nasrolahzadeh, Mohammadpoory, Haddadnia (CR73) 2019; 13
Locatelli, Cursi, Liberati, Franceschi, Comi (CR78) 1998; 106
Jackson, Bolger (CR20) 2014; 51
Martin, Velayudhan (CR7) 2020; 49
Campanharo, Ramos (CR39) 2016; 444
Pritchard, Duke, Coburn, Moore, Tucker, Jann, Hostetler (CR41) 1994; 91
Abásolo, Hornero, Espino, Alvarez, Poza (CR31) 2006; 27
Pham, Elfiqi, Knecht, Wersching, Baune, Berger (CR62) 2010; 188
Feldman, Woodward (CR9) 2005; 65
CR3
Im (CR25) 2018
Lake, Moorman (CR66) 2011; 300
Shoka, Dessouky, El-Sherbeny, El-Sayed (CR43) 2019; 28
CR46
CR45
Jeong (CR75) 2004; 115
CR44
CR42
Breakspear (CR24) 2017; 20
Yang, Bornot, Wong-Lin, Prasad (CR13) 2019; 66
Horvath, Szucs, Csukly, Sakovics, Stefanics, Kamondi (CR22) 2018; 23
Ahmadlou, Adeli, Adeli (CR52) 2010; 117
Pascual-Marqui, Michel, Lehmann (CR81) 1994; 18
Obuchowski (CR53) 2005; 184
CR19
Rosso, Martin, Figliola, Keller, Plastino (CR69) 2006; 153
CR18
CR17
Adeli, Ghosh-Dastidar, Dadmehr (CR26) 2005; 36
CR14
Campanharo, Sirer, Malmgren, Ramos, Amaral (CR70) 2011; 6
Katz (CR60) 1988; 18
Kim (CR54) 2017; 70
Grossi, Olivieri, Buscema (CR28) 2017; 142
Davatzikos (CR11) 2019; 197
Rao (CR61) 2005; 18
Petersen (CR5) 2004; 256
Husain, Garrett (CR16) 2005; 15
Song (CR48) 2017
Campanharo, Doescher, Ramos (CR30) 2020; 52
Simons, Abasolo, Escudero (CR63) 2015; 2
Helkala, Hänninen, Hallikainen, Könönen, Laakso, Hartikainen, Soininen, Partanen, Partanen, Vainio (CR79) 1996; 110
Lan, Mo, Chen, Liu, Deng (CR38) 2015; 25
Kang, Chen, Li, Li (CR32) 2019; 62
Sanei, Chambers (CR21) 2013
Rodrigues, Teixeira, Garrett, Alves, Freitas (CR1) 2016; 100
Rossini (CR6) 2020; 131
Shah, Zhang, Bais (CR29) 2020; 130
Neto, Allen, Aurlien, Nordby, Eichele (CR34) 2015; 6
Pritchard, Duke, Coburn (CR40) 1991; 2
CR68
Ahmadlou, Adeli, Adeli (CR80) 2011; 25
CR23
CR67
Wang, Wang, Yu, Wei, Yang, Deng (CR33) 2015; 9
CR65
CR64
Bennys, Rondouin, Vergnes, Touchon (CR77) 2001; 31
Houmani, Vialatte, Gallego-Jutglà, Dreyfus, Nguyen-Michel, Mariani, Kinugawa (CR2) 2018; 13
Pineda, Ramos, Betting, Campanharo (CR37) 2020; 15
Higuchi (CR59) 1988; 31
Livingston, Huntley, Sommerlad, Ames, Ballard, Banerjee, Brayne, Burns, Cohen-Mansfield, Cooper (CR4) 2020; 396
Canal (CR47) 2010; 34
Lacasa, Luque, Ballesteros, Luque, Nuno (CR72) 2008; 105
Dauwels, Vialatte, Cichocki (CR51) 2010; 7
Amin, Malik, Ahmad, Badruddin, Kamel, Hussain, Chooi (CR35) 2015; 38
MA Lopes (32664_CR27) 2019; 9
X Lan (32664_CR38) 2015; 25
32664_CR19
32664_CR18
H Song (32664_CR48) 2017
C Gómez (32664_CR58) 2009; 31
M Ahmadlou (32664_CR52) 2010; 117
A Horvath (32664_CR22) 2018; 23
E Martin (32664_CR7) 2020; 49
C Ieracitano (32664_CR10) 2020; 123
DE Lake (32664_CR66) 2011; 300
G Livingston (32664_CR4) 2020; 396
R Wang (32664_CR33) 2015; 9
32664_CR64
D-H Jeong (32664_CR56) 2016; 18
ASLO Campanharo (32664_CR39) 2016; 444
E Chandrasekhar (32664_CR55) 2013
32664_CR65
32664_CR68
32664_CR23
32664_CR67
L Lacasa (32664_CR72) 2008; 105
AS Chavan (32664_CR50) 2011; 3
S Yang (32664_CR13) 2019; 66
MR Canal (32664_CR47) 2010; 34
PM Rodrigues (32664_CR1) 2016; 100
RD Pascual-Marqui (32664_CR81) 1994; 18
H Braak (32664_CR8) 2011; 70
M Nasrolahzadeh (32664_CR73) 2019; 13
N Houmani (32664_CR2) 2018; 13
WS Pritchard (32664_CR40) 1991; 2
PM Rossini (32664_CR6) 2020; 131
32664_CR14
MJ Katz (32664_CR60) 1988; 18
32664_CR17
J Dauwels (32664_CR51) 2010; 7
E-L Helkala (32664_CR79) 1996; 110
M Rao (32664_CR61) 2005; 18
T Higuchi (32664_CR59) 1988; 31
NA Obuchowski (32664_CR53) 2005; 184
T Locatelli (32664_CR78) 1998; 106
AF Jackson (32664_CR20) 2014; 51
32664_CR3
M Breakspear (32664_CR24) 2017; 20
CH Im (32664_CR25) 2018
ASLO Campanharo (32664_CR70) 2011; 6
S Simons (32664_CR63) 2015; 2
32664_CR42
MM Husain (32664_CR16) 2005; 15
SAA Shah (32664_CR29) 2020; 130
ASLO Campanharo (32664_CR30) 2020; 52
MX Cohen (32664_CR49) 2019; 199
TK Kim (32664_CR54) 2017; 70
D Abásolo (32664_CR31) 2006; 27
O Rosso (32664_CR69) 2006; 153
M Ahmadlou (32664_CR80) 2011; 25
32664_CR44
32664_CR46
32664_CR45
TD Pham (32664_CR62) 2010; 188
K Van der Hiele (32664_CR15) 2007; 28
M Sharma (32664_CR12) 2020; 133
WS Pritchard (32664_CR41) 1994; 91
H Adeli (32664_CR26) 2005; 36
C Diks (32664_CR57) 1999
S Sanei (32664_CR21) 2013
K Bennys (32664_CR77) 2001; 31
AM Pineda (32664_CR37) 2020; 15
J Jeong (32664_CR75) 2004; 115
C Davatzikos (32664_CR11) 2019; 197
HU Amin (32664_CR35) 2015; 38
32664_CR74
J Kang (32664_CR32) 2019; 62
E Grossi (32664_CR28) 2017; 142
32664_CR71
H Feldman (32664_CR9) 2005; 65
RC Petersen (32664_CR5) 2004; 256
32664_CR36
E Neto (32664_CR34) 2015; 6
A Shoka (32664_CR43) 2019; 28
32664_CR76
References_xml – volume: 91
  start-page: 118
  issue: 2
  year: 1994
  end-page: 130
  ident: CR41
  article-title: EEG-based, neural-net predictive classification of Alzheimer’s disease versus control subjects is augmented by non-linear EEG measures
  publication-title: Electroencephalogr. Clin. Neurophysiol.
  doi: 10.1016/0013-4694(94)90033-7
– ident: CR45
– volume: 396
  start-page: 413
  issue: 10248
  year: 2020
  end-page: 446
  ident: CR4
  article-title: Dementia prevention, intervention, and care: 2020 Report of the Lancet Commission
  publication-title: Lancet
  doi: 10.1016/S0140-6736(20)30367-6
– volume: 15
  issue: 6
  year: 2020
  ident: CR37
  article-title: Quantile graphs for EEG-based diagnosis of Alzheimer’s disease
  publication-title: PLoS ONE
  doi: 10.1371/journal.pone.0231169
– volume: 49
  start-page: 146
  issue: 2
  year: 2020
  end-page: 155
  ident: CR7
  article-title: Neuropsychiatric symptoms in mild cognitive impairment: A literature review
  publication-title: Dement. Geriatr. Cogn. Disord.
  doi: 10.1159/000507078
– ident: CR68
– ident: CR74
– volume: 444
  start-page: 43
  year: 2016
  end-page: 48
  ident: CR39
  article-title: Hurst exponent estimation of self-affine time series using quantile graphs
  publication-title: Physica A
  doi: 10.1016/j.physa.2015.09.094
– volume: 197
  start-page: 652
  year: 2019
  ident: CR11
  article-title: Machine learning in neuroimaging: Progress and challenges
  publication-title: Neuroimage
  doi: 10.1016/j.neuroimage.2018.10.003
– volume: 6
  issue: 8
  year: 2011
  ident: CR70
  article-title: Duality between time series and networks
  publication-title: PLoS ONE
  doi: 10.1371/journal.pone.0023378
– volume: 9
  start-page: 1
  issue: 1
  year: 2019
  end-page: 10
  ident: CR27
  article-title: Revealing epilepsy type using a computational analysis of interictal EEG
  publication-title: Sci. Rep.
  doi: 10.1038/s41598-019-46633-7
– volume: 256
  start-page: 183
  issue: 3
  year: 2004
  end-page: 194
  ident: CR5
  article-title: Mild cognitive impairment as a diagnostic entity
  publication-title: J. Intern. Med.
  doi: 10.1111/j.1365-2796.2004.01388.x
– ident: CR42
– volume: 25
  start-page: 85
  issue: 1
  year: 2011
  end-page: 92
  ident: CR80
  article-title: Fractality and a wavelet-chaos-methodology for EEG-based diagnosis of Alzheimer’s disease
  publication-title: Alzheimer Disease Assoc. Disorders
  doi: 10.1097/WAD.0b013e3181ed1160
– volume: 105
  start-page: 4972
  issue: 13
  year: 2008
  end-page: 4975
  ident: CR72
  article-title: From time series to complex networks: The visibility graph
  publication-title: Proc. Natl. Acad. Sci.
  doi: 10.1073/pnas.0709247105
– volume: 31
  start-page: 153
  issue: 3
  year: 2001
  end-page: 160
  ident: CR77
  article-title: Diagnostic value of quantitative eeg in Alzheimer’s disease
  publication-title: Neurophysiologie Clinique/Clin. Neurophysiol.
  doi: 10.1016/S0987-7053(01)00254-4
– ident: CR46
– ident: CR71
– ident: CR19
– volume: 13
  issue: 3
  year: 2018
  ident: CR2
  article-title: Diagnosis of Alzheimer’s disease with electroencephalography in a differential framework
  publication-title: PLoS ONE
  doi: 10.1371/journal.pone.0193607
– volume: 31
  start-page: 306
  issue: 3
  year: 2009
  end-page: 313
  ident: CR58
  article-title: Use of the Higuchi’s fractal dimension for the analysis of MEG recordings from Alzheimer’s disease patients
  publication-title: Med. Eng. Phys.
  doi: 10.1016/j.medengphy.2008.06.010
– year: 2013
  ident: CR21
  publication-title: EEG Signal Processing
– volume: 110
  start-page: 1235
  issue: 6
  year: 1996
  ident: CR79
  article-title: Slow-wave activity in the spectral analysis of the electroencephalogram and volumes of hippocampus in subgroups of Alzheimer’s disease patients
  publication-title: Behav. Neurosci.
  doi: 10.1037/0735-7044.110.6.1235
– volume: 28
  start-page: 292
  issue: 1
  year: 2019
  end-page: 299
  ident: CR43
  article-title: Literature review on EEG preprocessing, feature extraction, and classifications techniques
  publication-title: Menoufia J. Electron. Eng. Res
  doi: 10.21608/mjeer.2019.64927
– ident: CR67
– year: 1999
  ident: CR57
  publication-title: Nonlinear Time Series Analysis: Methods and Applications
  doi: 10.1142/3823
– volume: 115
  start-page: 1490
  issue: 7
  year: 2004
  end-page: 1505
  ident: CR75
  article-title: EEG dynamics in patients with Alzheimer’s disease
  publication-title: Clin. Neurophysiol.
  doi: 10.1016/j.clinph.2004.01.001
– volume: 36
  start-page: 131
  issue: 3
  year: 2005
  end-page: 140
  ident: CR26
  article-title: Alzheimer’s disease: Models of computation and analysis of EEGs
  publication-title: Clin. EEG Neurosci.
  doi: 10.1177/155005940503600303
– volume: 2
  start-page: 102
  issue: 2
  year: 1991
  end-page: 105
  ident: CR40
  article-title: Altered EEG dynamical responsivity associated with normal aging and probable Alzheimer’s disease
  publication-title: Dement. Geriatr. Cogn. Disord.
  doi: 10.1159/000107183
– volume: 66
  start-page: 2924
  issue: 10
  year: 2019
  end-page: 2935
  ident: CR13
  article-title: M/EEG-based bio-markers to predict the MCI and Alzheimer’s disease: A review from the ml perspective
  publication-title: IEEE Trans. Biomed. Eng.
  doi: 10.1109/TBME.2019.2898871
– ident: CR36
– volume: 18
  start-page: 49
  issue: 1
  year: 1994
  end-page: 65
  ident: CR81
  article-title: Low resolution electromagnetic tomography: A new method for localizing electrical activity in the brain
  publication-title: Int. J. Psychophysiol.
  doi: 10.1016/0167-8760(84)90014-X
– volume: 123
  start-page: 176
  year: 2020
  end-page: 190
  ident: CR10
  article-title: A novel multi-modal machine learning based approach for automatic classification of EEG recordings in dementia
  publication-title: Neural Netw.
  doi: 10.1016/j.neunet.2019.12.006
– ident: CR64
– volume: 70
  start-page: 22
  issue: 1
  year: 2017
  ident: CR54
  article-title: Understanding one-way ANOVA using conceptual figures
  publication-title: Korean J. Anesthesiol.
  doi: 10.4097/kjae.2017.70.1.22
– volume: 133
  start-page: 188
  year: 2020
  end-page: 194
  ident: CR12
  article-title: Automated detection of abnormal EEG signals using localized wavelet filter banks
  publication-title: Pattern Recogn. Lett.
  doi: 10.1016/j.patrec.2020.03.009
– volume: 15
  start-page: 767
  issue: 4
  year: 2005
  end-page: 77
  ident: CR16
  article-title: Clinical diagnosis and management of Alzheimer’s disease
  publication-title: Neuroimaging Clin. N. Am.
  doi: 10.1016/j.nic.2005.09.005
– ident: CR18
– volume: 106
  start-page: 229
  issue: 3
  year: 1998
  end-page: 237
  ident: CR78
  article-title: EEG coherence in Alzheimer’s disease
  publication-title: Electroencephalogr. Clin. Neurophysiol.
  doi: 10.1016/S0013-4694(97)00129-6
– volume: 100
  start-page: 865
  year: 2016
  end-page: 871
  ident: CR1
  article-title: Alzheimer’s early prediction with electroencephalogram
  publication-title: Procedia Comput. Sci.
  doi: 10.1016/j.procs.2016.09.236
– ident: CR14
– volume: 300
  start-page: H319
  issue: 1
  year: 2011
  end-page: H325
  ident: CR66
  article-title: Accurate estimation of entropy in very short physiological time series: The problem of atrial fibrillation detection in implanted ventricular devices
  publication-title: Am. J. Physiol.-Heart Circ. Physiol.
  doi: 10.1152/ajpheart.00561.2010
– start-page: 596
  year: 2017
  ident: CR48
  publication-title: Review of Time Series Analysis and Its Applications With R Examples, by Robert H. Shumway & David S. Stoffer
– volume: 18
  start-page: 967
  issue: 4
  year: 2005
  end-page: 981
  ident: CR61
  article-title: More on a new concept of entropy and information
  publication-title: J. Theor. Probab.
  doi: 10.1007/s10959-005-7541-3
– volume: 65
  start-page: S10
  issue: 6 suppl 3
  year: 2005
  end-page: S17
  ident: CR9
  article-title: The staging and assessment of moderate to severe Alzheimer’s disease
  publication-title: Neurology
  doi: 10.1212/WNL.65.6_suppl_3.S10
– volume: 117
  start-page: 1099
  issue: 9
  year: 2010
  end-page: 1109
  ident: CR52
  article-title: New diagnostic EEG markers of the Alzheimer’s disease using visibility graph
  publication-title: J. Neural Transm.
  doi: 10.1007/s00702-010-0450-3
– volume: 142
  start-page: 73
  year: 2017
  end-page: 79
  ident: CR28
  article-title: Diagnosis of autism through EEG processed by advanced computational algorithms: A pilot study
  publication-title: Comput. Methods Programs Biomed.
  doi: 10.1016/j.cmpb.2017.02.002
– volume: 28
  start-page: 1322
  issue: 9
  year: 2007
  end-page: 1329
  ident: CR15
  article-title: EEG and MRI correlates of mild cognitive impairment and Alzheimer’s disease
  publication-title: Neurobiol. Aging
  doi: 10.1016/j.neurobiolaging.2006.06.006
– volume: 13
  start-page: 45
  issue: 1
  year: 2019
  end-page: 52
  ident: CR73
  article-title: Analysis of heart rate signals during meditation using visibility graph complexity
  publication-title: Cogn. Neurodyn.
  doi: 10.1007/s11571-018-9501-5
– ident: CR23
– year: 2018
  ident: CR25
  publication-title: Computational EEG Analysis
  doi: 10.1007/978-981-13-0908-3
– volume: 70
  start-page: 960
  issue: 11
  year: 2011
  end-page: 969
  ident: CR8
  article-title: Stages of the pathologic process in Alzheimer’s disease: Age categories from 1 to 100 years
  publication-title: J. Neuropathol. Exp. Neurol.
  doi: 10.1097/NEN.0b013e318232a379
– volume: 25
  issue: 8
  year: 2015
  ident: CR38
  article-title: Fast transformation from time series to visibility graphs
  publication-title: Chaos: Interdiscip. J. Nonlinear Sci.
  doi: 10.1063/1.4927835
– ident: CR44
– volume: 7
  start-page: 487
  issue: 6
  year: 2010
  end-page: 505
  ident: CR51
  article-title: Diagnosis of Alzheimer’s disease from EEG signals: where are we standing?
  publication-title: Curr. Alzheimer Res.
  doi: 10.2174/156720510792231720
– volume: 130
  start-page: 75
  year: 2020
  end-page: 84
  ident: CR29
  article-title: Dynamical system based compact deep hybrid network for classification of Parkinson’s disease related EEG signals
  publication-title: Neural Netw.
  doi: 10.1016/j.neunet.2020.06.018
– volume: 18
  start-page: 145
  issue: 3
  year: 1988
  end-page: 156
  ident: CR60
  article-title: Fractals and the analysis of waveforms
  publication-title: Comput. Biol. Med.
  doi: 10.1016/0010-4825(88)90041-8
– ident: CR65
– volume: 2
  start-page: 70
  issue: 3
  year: 2015
  end-page: 73
  ident: CR63
  article-title: Classification of Alzheimer’s disease from quadratic sample entropy of electroencephalogram
  publication-title: Healthc. Technol. Lett.
  doi: 10.1049/htl.2014.0106
– volume: 9
  start-page: 291
  issue: 3
  year: 2015
  end-page: 304
  ident: CR33
  article-title: Power spectral density and coherence analysis of Alzheimer’s EEG
  publication-title: Cogn. Neurodyn.
  doi: 10.1007/s11571-014-9325-x
– ident: CR3
– volume: 27
  start-page: 241
  issue: 3
  year: 2006
  ident: CR31
  article-title: Entropy analysis of the EEG background activity in Alzheimer’s disease patients
  publication-title: Physiol. Meas.
  doi: 10.1088/0967-3334/27/3/003
– volume: 6
  start-page: 25
  year: 2015
  ident: CR34
  article-title: EEG spectral features discriminate between Alzheimer’s and vascular dementia
  publication-title: Front. Neurol.
  doi: 10.3389/fneur.2015.00025
– volume: 20
  start-page: 340
  issue: 3
  year: 2017
  end-page: 352
  ident: CR24
  article-title: Dynamic models of large-scale brain activity
  publication-title: Nat. Neurosci.
  doi: 10.1038/nn.4497
– volume: 18
  start-page: 8
  issue: 1
  year: 2016
  ident: CR56
  article-title: Wavelet energy and wavelet coherence as EEG biomarkers for the diagnosis of Parkinson’s disease-related dementia and Alzheimer’s disease
  publication-title: Entropy
  doi: 10.3390/e18010008
– ident: CR17
– year: 2013
  ident: CR55
  publication-title: Wavelets and Fractals in Earth System Sciences
  doi: 10.1201/b16046
– volume: 23
  start-page: 183
  year: 2018
  end-page: 220
  ident: CR22
  article-title: EEG and ERP biomarkers of Alzheimer’s disease: A critical review
  publication-title: Front. Biosci. (Landmark edn.)
  doi: 10.2741/4587
– volume: 62
  start-page: 199
  year: 2019
  end-page: 206
  ident: CR32
  article-title: EEG entropy analysis in autistic children
  publication-title: J. Clin. Neurosci.
  doi: 10.1016/j.jocn.2018.11.027
– volume: 184
  start-page: 364
  issue: 2
  year: 2005
  end-page: 372
  ident: CR53
  article-title: ROC analysis
  publication-title: Am. J. Roentgenol.
  doi: 10.2214/ajr.184.2.01840364
– volume: 199
  start-page: 81
  year: 2019
  end-page: 86
  ident: CR49
  article-title: A better way to define and describe Morlet wavelets for time-frequency analysis
  publication-title: Neuroimage
  doi: 10.1016/j.neuroimage.2019.05.048
– volume: 52
  start-page: 5
  issue: 1
  year: 2020
  end-page: 20
  ident: CR30
  article-title: Application of quantile graphs to the automated analysis of EEG signals
  publication-title: Neural Process. Lett.
  doi: 10.1007/s11063-018-9936-z
– volume: 3
  start-page: 5
  issue: 1
  year: 2011
  end-page: 10
  ident: CR50
  article-title: EEG signal preprocessing using Wavelet Transform
  publication-title: Int. J. Electron. Eng.
– volume: 131
  start-page: 1287
  issue: 6
  year: 2020
  end-page: 1310
  ident: CR6
  article-title: Early diagnosis of Alzheimer’s disease: The role of biomarkers including advanced EEG signal analysis. Report from the IFCN-sponsored panel of experts
  publication-title: Clin. Neurophysiol.
  doi: 10.1016/j.clinph.2020.03.003
– volume: 51
  start-page: 1061
  issue: 11
  year: 2014
  end-page: 1071
  ident: CR20
  article-title: The neurophysiological bases of EEG and EEG measurement: A review for the rest of us
  publication-title: Psychophysiology
  doi: 10.1111/psyp.12283
– volume: 38
  start-page: 139
  issue: 1
  year: 2015
  end-page: 149
  ident: CR35
  article-title: Feature extraction and classification for EEG signals using Wavelet Transform and machine learning techniques
  publication-title: Austral. Phys. Eng. Sci. Med.
  doi: 10.1007/s13246-015-0333-x
– ident: CR76
– volume: 34
  start-page: 91
  issue: 1
  year: 2010
  end-page: 94
  ident: CR47
  article-title: Comparison of wavelet and short time Fourier transform methods in the analysis of EMG signals
  publication-title: J. Med. Syst.
  doi: 10.1007/s10916-008-9219-8
– volume: 188
  start-page: 113
  issue: 1
  year: 2010
  end-page: 126
  ident: CR62
  article-title: Structural simplexity of the brain
  publication-title: J. Neurosci. Methods
  doi: 10.1016/j.jneumeth.2010.01.029
– volume: 31
  start-page: 277
  issue: 2
  year: 1988
  end-page: 283
  ident: CR59
  article-title: Approach to an irregular time series on the basis of the fractal theory
  publication-title: Physica D
  doi: 10.1016/0167-2789(88)90081-4
– volume: 153
  start-page: 163
  issue: 2
  year: 2006
  end-page: 182
  ident: CR69
  article-title: EEG analysis using wavelet-based information tools
  publication-title: J. Neurosci. Methods
  doi: 10.1016/j.jneumeth.2005.10.009
– volume: 15
  start-page: 767
  issue: 4
  year: 2005
  ident: 32664_CR16
  publication-title: Neuroimaging Clin. N. Am.
  doi: 10.1016/j.nic.2005.09.005
– ident: 32664_CR68
  doi: 10.1109/CCSP.2005.4977207
– ident: 32664_CR3
– volume: 110
  start-page: 1235
  issue: 6
  year: 1996
  ident: 32664_CR79
  publication-title: Behav. Neurosci.
  doi: 10.1037/0735-7044.110.6.1235
– volume: 49
  start-page: 146
  issue: 2
  year: 2020
  ident: 32664_CR7
  publication-title: Dement. Geriatr. Cogn. Disord.
  doi: 10.1159/000507078
– ident: 32664_CR67
  doi: 10.1145/1543834.1543860
– volume: 65
  start-page: S10
  issue: 6 suppl 3
  year: 2005
  ident: 32664_CR9
  publication-title: Neurology
  doi: 10.1212/WNL.65.6_suppl_3.S10
– volume-title: Wavelets and Fractals in Earth System Sciences
  year: 2013
  ident: 32664_CR55
  doi: 10.1201/b16046
– ident: 32664_CR76
  doi: 10.1155/2018/8915079
– ident: 32664_CR46
– volume: 184
  start-page: 364
  issue: 2
  year: 2005
  ident: 32664_CR53
  publication-title: Am. J. Roentgenol.
  doi: 10.2214/ajr.184.2.01840364
– ident: 32664_CR18
  doi: 10.11113/jt.v72.3886
– volume: 9
  start-page: 1
  issue: 1
  year: 2019
  ident: 32664_CR27
  publication-title: Sci. Rep.
  doi: 10.1038/s41598-019-46633-7
– volume-title: Nonlinear Time Series Analysis: Methods and Applications
  year: 1999
  ident: 32664_CR57
  doi: 10.1142/3823
– volume: 105
  start-page: 4972
  issue: 13
  year: 2008
  ident: 32664_CR72
  publication-title: Proc. Natl. Acad. Sci.
  doi: 10.1073/pnas.0709247105
– volume: 13
  issue: 3
  year: 2018
  ident: 32664_CR2
  publication-title: PLoS ONE
  doi: 10.1371/journal.pone.0193607
– volume: 28
  start-page: 1322
  issue: 9
  year: 2007
  ident: 32664_CR15
  publication-title: Neurobiol. Aging
  doi: 10.1016/j.neurobiolaging.2006.06.006
– volume: 31
  start-page: 277
  issue: 2
  year: 1988
  ident: 32664_CR59
  publication-title: Physica D
  doi: 10.1016/0167-2789(88)90081-4
– volume-title: EEG Signal Processing
  year: 2013
  ident: 32664_CR21
– volume: 6
  issue: 8
  year: 2011
  ident: 32664_CR70
  publication-title: PLoS ONE
  doi: 10.1371/journal.pone.0023378
– volume: 133
  start-page: 188
  year: 2020
  ident: 32664_CR12
  publication-title: Pattern Recogn. Lett.
  doi: 10.1016/j.patrec.2020.03.009
– volume: 18
  start-page: 8
  issue: 1
  year: 2016
  ident: 32664_CR56
  publication-title: Entropy
  doi: 10.3390/e18010008
– volume: 18
  start-page: 49
  issue: 1
  year: 1994
  ident: 32664_CR81
  publication-title: Int. J. Psychophysiol.
  doi: 10.1016/0167-8760(84)90014-X
– volume: 28
  start-page: 292
  issue: 1
  year: 2019
  ident: 32664_CR43
  publication-title: Menoufia J. Electron. Eng. Res
  doi: 10.21608/mjeer.2019.64927
– volume: 153
  start-page: 163
  issue: 2
  year: 2006
  ident: 32664_CR69
  publication-title: J. Neurosci. Methods
  doi: 10.1016/j.jneumeth.2005.10.009
– volume: 18
  start-page: 967
  issue: 4
  year: 2005
  ident: 32664_CR61
  publication-title: J. Theor. Probab.
  doi: 10.1007/s10959-005-7541-3
– volume: 188
  start-page: 113
  issue: 1
  year: 2010
  ident: 32664_CR62
  publication-title: J. Neurosci. Methods
  doi: 10.1016/j.jneumeth.2010.01.029
– volume: 396
  start-page: 413
  issue: 10248
  year: 2020
  ident: 32664_CR4
  publication-title: Lancet
  doi: 10.1016/S0140-6736(20)30367-6
– ident: 32664_CR65
  doi: 10.1152/ajpheart.2000.278.6.H2039
– volume: 20
  start-page: 340
  issue: 3
  year: 2017
  ident: 32664_CR24
  publication-title: Nat. Neurosci.
  doi: 10.1038/nn.4497
– ident: 32664_CR74
  doi: 10.1137/1.9781611970104
– volume: 2
  start-page: 70
  issue: 3
  year: 2015
  ident: 32664_CR63
  publication-title: Healthc. Technol. Lett.
  doi: 10.1049/htl.2014.0106
– ident: 32664_CR45
– volume-title: Computational EEG Analysis
  year: 2018
  ident: 32664_CR25
  doi: 10.1007/978-981-13-0908-3
– volume: 62
  start-page: 199
  year: 2019
  ident: 32664_CR32
  publication-title: J. Clin. Neurosci.
  doi: 10.1016/j.jocn.2018.11.027
– volume: 106
  start-page: 229
  issue: 3
  year: 1998
  ident: 32664_CR78
  publication-title: Electroencephalogr. Clin. Neurophysiol.
  doi: 10.1016/S0013-4694(97)00129-6
– volume: 18
  start-page: 145
  issue: 3
  year: 1988
  ident: 32664_CR60
  publication-title: Comput. Biol. Med.
  doi: 10.1016/0010-4825(88)90041-8
– volume: 142
  start-page: 73
  year: 2017
  ident: 32664_CR28
  publication-title: Comput. Methods Programs Biomed.
  doi: 10.1016/j.cmpb.2017.02.002
– volume: 3
  start-page: 5
  issue: 1
  year: 2011
  ident: 32664_CR50
  publication-title: Int. J. Electron. Eng.
– volume: 23
  start-page: 183
  year: 2018
  ident: 32664_CR22
  publication-title: Front. Biosci. (Landmark edn.)
  doi: 10.2741/4587
– volume: 70
  start-page: 960
  issue: 11
  year: 2011
  ident: 32664_CR8
  publication-title: J. Neuropathol. Exp. Neurol.
  doi: 10.1097/NEN.0b013e318232a379
– volume: 444
  start-page: 43
  year: 2016
  ident: 32664_CR39
  publication-title: Physica A
  doi: 10.1016/j.physa.2015.09.094
– ident: 32664_CR23
  doi: 10.1007/978-3-319-59147-6_9
– volume: 131
  start-page: 1287
  issue: 6
  year: 2020
  ident: 32664_CR6
  publication-title: Clin. Neurophysiol.
  doi: 10.1016/j.clinph.2020.03.003
– ident: 32664_CR17
  doi: 10.1155/2014/349249
– volume: 199
  start-page: 81
  year: 2019
  ident: 32664_CR49
  publication-title: Neuroimage
  doi: 10.1016/j.neuroimage.2019.05.048
– ident: 32664_CR64
– ident: 32664_CR14
  doi: 10.1109/IEMBS.2010.5627621
– ident: 32664_CR71
  doi: 10.1007/978-3-030-20521-8_10
– volume: 66
  start-page: 2924
  issue: 10
  year: 2019
  ident: 32664_CR13
  publication-title: IEEE Trans. Biomed. Eng.
  doi: 10.1109/TBME.2019.2898871
– volume: 38
  start-page: 139
  issue: 1
  year: 2015
  ident: 32664_CR35
  publication-title: Austral. Phys. Eng. Sci. Med.
  doi: 10.1007/s13246-015-0333-x
– volume: 25
  issue: 8
  year: 2015
  ident: 32664_CR38
  publication-title: Chaos: Interdiscip. J. Nonlinear Sci.
  doi: 10.1063/1.4927835
– volume: 70
  start-page: 22
  issue: 1
  year: 2017
  ident: 32664_CR54
  publication-title: Korean J. Anesthesiol.
  doi: 10.4097/kjae.2017.70.1.22
– volume: 123
  start-page: 176
  year: 2020
  ident: 32664_CR10
  publication-title: Neural Netw.
  doi: 10.1016/j.neunet.2019.12.006
– volume: 13
  start-page: 45
  issue: 1
  year: 2019
  ident: 32664_CR73
  publication-title: Cogn. Neurodyn.
  doi: 10.1007/s11571-018-9501-5
– volume: 130
  start-page: 75
  year: 2020
  ident: 32664_CR29
  publication-title: Neural Netw.
  doi: 10.1016/j.neunet.2020.06.018
– volume: 36
  start-page: 131
  issue: 3
  year: 2005
  ident: 32664_CR26
  publication-title: Clin. EEG Neurosci.
  doi: 10.1177/155005940503600303
– volume: 300
  start-page: H319
  issue: 1
  year: 2011
  ident: 32664_CR66
  publication-title: Am. J. Physiol.-Heart Circ. Physiol.
  doi: 10.1152/ajpheart.00561.2010
– volume: 100
  start-page: 865
  year: 2016
  ident: 32664_CR1
  publication-title: Procedia Comput. Sci.
  doi: 10.1016/j.procs.2016.09.236
– volume: 6
  start-page: 25
  year: 2015
  ident: 32664_CR34
  publication-title: Front. Neurol.
  doi: 10.3389/fneur.2015.00025
– volume: 256
  start-page: 183
  issue: 3
  year: 2004
  ident: 32664_CR5
  publication-title: J. Intern. Med.
  doi: 10.1111/j.1365-2796.2004.01388.x
– volume: 15
  issue: 6
  year: 2020
  ident: 32664_CR37
  publication-title: PLoS ONE
  doi: 10.1371/journal.pone.0231169
– volume: 91
  start-page: 118
  issue: 2
  year: 1994
  ident: 32664_CR41
  publication-title: Electroencephalogr. Clin. Neurophysiol.
  doi: 10.1016/0013-4694(94)90033-7
– volume: 51
  start-page: 1061
  issue: 11
  year: 2014
  ident: 32664_CR20
  publication-title: Psychophysiology
  doi: 10.1111/psyp.12283
– volume: 52
  start-page: 5
  issue: 1
  year: 2020
  ident: 32664_CR30
  publication-title: Neural Process. Lett.
  doi: 10.1007/s11063-018-9936-z
– ident: 32664_CR42
  doi: 10.21203/rs.3.rs-2371230/v1
– start-page: 596
  volume-title: Review of Time Series Analysis and Its Applications With R Examples, by Robert H. Shumway & David S. Stoffer
  year: 2017
  ident: 32664_CR48
– volume: 27
  start-page: 241
  issue: 3
  year: 2006
  ident: 32664_CR31
  publication-title: Physiol. Meas.
  doi: 10.1088/0967-3334/27/3/003
– volume: 117
  start-page: 1099
  issue: 9
  year: 2010
  ident: 32664_CR52
  publication-title: J. Neural Transm.
  doi: 10.1007/s00702-010-0450-3
– volume: 197
  start-page: 652
  year: 2019
  ident: 32664_CR11
  publication-title: Neuroimage
  doi: 10.1016/j.neuroimage.2018.10.003
– ident: 32664_CR36
  doi: 10.1109/EMBC.2015.7320104
– volume: 25
  start-page: 85
  issue: 1
  year: 2011
  ident: 32664_CR80
  publication-title: Alzheimer Disease Assoc. Disorders
  doi: 10.1097/WAD.0b013e3181ed1160
– volume: 115
  start-page: 1490
  issue: 7
  year: 2004
  ident: 32664_CR75
  publication-title: Clin. Neurophysiol.
  doi: 10.1016/j.clinph.2004.01.001
– volume: 31
  start-page: 306
  issue: 3
  year: 2009
  ident: 32664_CR58
  publication-title: Med. Eng. Phys.
  doi: 10.1016/j.medengphy.2008.06.010
– ident: 32664_CR44
  doi: 10.1007/978-981-13-0908-3_2
– volume: 34
  start-page: 91
  issue: 1
  year: 2010
  ident: 32664_CR47
  publication-title: J. Med. Syst.
  doi: 10.1007/s10916-008-9219-8
– volume: 2
  start-page: 102
  issue: 2
  year: 1991
  ident: 32664_CR40
  publication-title: Dement. Geriatr. Cogn. Disord.
  doi: 10.1159/000107183
– ident: 32664_CR19
– volume: 9
  start-page: 291
  issue: 3
  year: 2015
  ident: 32664_CR33
  publication-title: Cogn. Neurodyn.
  doi: 10.1007/s11571-014-9325-x
– volume: 31
  start-page: 153
  issue: 3
  year: 2001
  ident: 32664_CR77
  publication-title: Neurophysiologie Clinique/Clin. Neurophysiol.
  doi: 10.1016/S0987-7053(01)00254-4
– volume: 7
  start-page: 487
  issue: 6
  year: 2010
  ident: 32664_CR51
  publication-title: Curr. Alzheimer Res.
  doi: 10.2174/156720510792231720
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Snippet Computational analysis of electroencephalographic (EEG) signals have shown promising results in detecting brain disorders, such as Alzheimer’s disease (AD). AD...
Computational analysis of electroencephalographic (EEG) signals have shown promising results in detecting brain disorders, such as Alzheimer's disease (AD). AD...
Abstract Computational analysis of electroencephalographic (EEG) signals have shown promising results in detecting brain disorders, such as Alzheimer’s disease...
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SubjectTerms 631/378/1689/1283
639/166/985
639/766/530/2801
Aged
Algorithms
Alzheimer Disease - diagnosis
Alzheimer's disease
Biomarkers
Brain research
Cognitive ability
Cognitive Dysfunction - diagnosis
Computational neuroscience
Dementia
EEG
Electroencephalography
Electroencephalography - methods
Entropy
Fourier transforms
Humanities and Social Sciences
Humans
multidisciplinary
Neurodegeneration
Neurodegenerative diseases
Quality of Life
Science
Science (multidisciplinary)
Time series
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Title Computational methods of EEG signals analysis for Alzheimer’s disease classification
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