×

EMD-based analysis of complexity with dissociated EEG amplitude and frequency information: a data-driven robust tool – for autism diagnosis – compared to multi-scale entropy approach. (English) Zbl 1501.92067

MSC:

92C55 Biomedical imaging and signal processing
PDFBibTeX XMLCite
Full Text: DOI

References:

[1] T, A systematic review of screening tools in non-young children and adults for autism spectrum disorder, Res. Dev. Disabil., 80, 1-12 (2018) · doi:10.1016/j.ridd.2018.05.017
[2] C, A. Pickles. Autism from 2 to 9 years of age., Arch. Gen. Psychiatry, 63, 694-701 (2006) · doi:10.1001/archpsyc.63.6.694
[3] B, Predictive validity of self-report questionnaires in the assessment of autism spectrum disorders in adults, Autism, 19, 842-849 (2015) · doi:10.1177/1362361315589869
[4] P, Autism Spectrum Disorder Screening Instruments for Very Young Children: A Systematic Review, Autism. Res. Treat., 2016, 4624829 (2016) · doi:10.1155/2016/4624829
[5] D, Use of machine learning to improve autism screening and diagnostic instruments: effectiveness. efficiency, and multi-Instrument Fusion, J. Child. Psychol. Psychiatry, 57, 927-937 (2017) · doi:10.1111/jcpp.12559
[6] J, Searching for a minimal set of behaviors for autism detection through feature selection-based machine learning, Transl. Psychiatry, 5, 514-517 (2015) · doi:10.1038/tp.2015.7
[7] F, Machine learning in autistic spectrum disorder behavioral research: A review and ways forward, Informatics Heal. Soc. Care, 44, 278-297 (2019) · doi:10.1080/17538157.2017.1399132
[8] D, Predicting autism spectrum disorder using blood-based gene expression signatures and machine learning, Clin. Psychopharmacol. Neurosci., 15, 47-52 (2017) · doi:10.9758/cpn.2017.15.1.47
[9] M, Use of machine learning for behavioral distinction of autism and ADHD, Transl. Psychiatry, 6, 732 (2016) · doi:10.1038/tp.2015.221
[10] G, High-efficiency classification of children with autism spectrum disorder, PLoS One, 13, 1-23 (2018) · doi:10.1371/journal.pone.0192867
[11] Q, Detecting Developmental Delay and Autism Through Machine Learning Models Using Home Videos of Bangladeshi Children: Development and Validation Study, J. Med. Internet Res., 21, 13822 (2019) · doi:10.2196/13822
[12] D. Eman, W. R. Emanuel, Machine Learning Classifiers for Autism Spectrum Disorder: A Review, <i>2019 4th Int. Conf. Inform. Technol. Inform. Syst. Electr. Eng. (ICITISEE)</i>, Yogyakarta, Indonesia, 2019. <a href=“https://doi.org/10.1109/ICITISEE48480.2019.9003807” target=“_blank”>https://doi.org/10.1109/ICITISEE48480.2019.9003807</a>
[13] X, Classification of autism spectrum disorder using random support vector machine cluster, Frontiers in Genetics, 6, 9-18 (2018) · doi:10.3389/fgene.2018.00018
[14] E, Diagnosis of autism through EEG processed by advanced computational algorithms: a pilot study, Comput. Methods Programs Biomed., 142, 73-79 (2017) · doi:10.1016/j.cmpb.2017.02.002
[15] M, Neural network based classification of EEG signals for diagnosis of autism spectrum disorder, Int. J. Pharm. Bio. Sci., 8, 1020-1026 (2017)
[16] L, EEG based ASD diagnosis for children using auto-regressive features and FFNN, Int. J. Control Theo. App., 10, 27-32 (2017)
[17] L, EEG based diagnosis of autism spectrum disorder using static and dynamic neural networks, ARPN J. Eng. Appl. Sci., 12, 4653787 (2017)
[18] R, EEG-based computer aided diagnosis of autism spectrum disorder using wavelet. entropy, and ANN, BioMed. Res. Int., 2017, 1-9 (2017) · doi:10.1155/2017/9816591
[19] T, Recent Advances in Resting-State Electroencephalography Biomarkers for Autism Spectrum Disorder-A Review of Methodological and Clinical Challenges, Rev. Pediatr. Neurol., 61, 28-37 (2016) · doi:10.1016/j.pediatrneurol.2016.03.010
[20] N, Spectrum-weighted EEG frequency (“brain-rate”) as a quantitative indicator of mental arousal., Prilozi., 26, 35-42 (2005)
[21] E, Classification of Normal, Ictal and Inter-ictal EEG via Direct Quadrature and Random Forest Tree, J. Med. Biol. Eng., 37, 843-857 (2017) · doi:10.1007/s40846-017-0239-z
[22] Z. Dandan, D. Haiyan, H. Xinlin, L. Yunfeng, Z. Congle, Y. Datian, The Combination of Amplitude and Sample Entropy in EEG and its Application to Assessment of Cerebral Injuries in Piglets, <i>2008 Int. Conf. BioMed. Eng. Informatics</i>, Sanya, China, 2008. <a href=“https://doi.org/10.1109/BMEI.2008.12” target=“_blank”>https://doi.org/10.1109/BMEI.2008.12</a>
[23] E, Computer-aided autism diagnosis via second-order difference plot area applied to EEG empirical mode decomposition, Neural Comput. Appl., 32, 10947-10956 (2020) · doi:10.1007/s00521-018-3738-0
[24] R, Seizure classification in EEG signals utilizing Hilbert-Huang transform, Biomed. Eng. Online, 10, 38 (2011) · doi:10.1186/1475-925X-10-38
[25] E. Abdulhay, M. Alafeef, H. Hadoush, N. Alomari, M. Bashayreh, Frequency 3D Mapping and Inter-Channel Stability of EEG Intrinsic Function Pulsation: Indicators Towards Autism Spectrum Diagnosis, <i>2017 10th Jordanian Int. Electric. Electron. Eng. Conf. (JIEEEC)</i>, Amman, Jordan, 2017. <a href=“https://doi.org/10.1109/JIEEEC.2017.8051416” target=“_blank”>https://doi.org/10.1109/JIEEEC.2017.8051416</a>
[26] H, Automated identification for autism severity level: EEG analysis using empirical mode decomposition and second order difference plot, Behavioural Brain Res., 362, 240-248 (2019) · doi:10.1016/j.bbr.2019.01.018
[27] E, Automated diagnosis of epilepsy from EEG signals using ensemble learning approach, Pattern Recognition Letters, 139, 174-181 (2020) · doi:10.1016/j.patrec.2017.05.021
[28] T, Autism spectrum disorder diagnostic system using HOS bispectrum with EEG signals, Int. J. Environ. Res. Public Health, 17, 1-14 (2020) · doi:10.3390/ijerph17030971
[29] W, EEG complexity as a biomarker for autism spectrum disorder risk, BMC Med., 9, 18 (2011) · doi:10.1186/1741-7015-9-18
[30] F, Autism, spectrum or clusters? An EEG coherence study, BMC Neurol., 19, 27 (2019) · doi:10.1186/s12883-019-1254-1
[31] A. Sheikhani, H. Behnam, M. R. Mohammadi, M. Noroozian, Analysis of EEG background activity in Autsim disease patients with bispectrum and STFT measure, <i>Proceedings of the 11th WSEAS Int. Conf. Commun.</i>, Agios Nikolaos, Greece, 2007.
[32] J, EEG entropy analysis in autistic children, J. Clin. Neurosci., 62, 199-206 (2019) · doi:10.1016/j.jocn.2018.11.027
[33] L, On the application of quantitative EEG for characterizing autistic brain: a systematic review, Front. Hum. Neurosci., 7, 442 (2013) · doi:10.3389/fnhum.2013.00442
[34] M, Fractality and a wavelet-chaos-neural network methodology for EEG-based diagnosis of autistic spectrum disorder, J. Clin. Neurophysiol., 27, 328-333 (2010) · doi:10.1097/WNP.0b013e3181f40dc8
[35] B. B. Mandelbrot, The Fractal Geometry of Nature. New York: Freeman and Company (1977), 1-468. · Zbl 0504.28001
[36] M, Multiscale entropy analysis of biological signals., Phys. Rev. E., 71, 021906 (2005) · doi:10.1103/PhysRevE.71.021906
[37] A, A review of entropy measures for uncertainty quantification of stochastic processes, Adv. Mechanical Eng., 11, 1-14 (2019) · doi:10.1177/1687814019857350
[38] H, Brain complexity in children with mild and severe autism spectrum disorders: analysis of multiscale entropy in EEG, Brain Topography, 32, 914-921 (2019) · doi:10.1007/s10548-019-00711-1
[39] Y, Joint analysis of band-specific functional connectivity and signal complexity in autism, J. Autism Dev. Disord., 45, 444-460 (2015) · doi:10.1007/s10803-013-1915-7
[40] T, Altered electroencephalogram complexity in autistic children shown by the multiscale entropy approach, Neuro. Report, 28, 169-173 (2017) · doi:10.1097/WNR.0000000000000724
[41] J. O. Maximo, D. L. Murdaugh, R. K. Kana, Alterations in Brain Entropy in Autism Spectrum Disorders, <i>2017 Int. Meet. Autism Res.</i>, Birmingham, USA, 2017.
[42] J, Standard multiscale entropy reflects neural dynamics at mismatched temporal scales: What’s signal irregularity got to do with it?, PLOS Comput. Biol., 16, e1007885 (2020) · doi:10.1371/journal.pcbi.1007885
[43] A, Atypical EEG complexity in autism spectrum conditions: a multiscale. entropy analysis, Clin. Neurophysiol., 122, 2375-2383 (2011) · doi:10.1016/j.clinph.2011.05.004
[44] J, Physiological time-series analysis using approximate entropy and sample entropy, Am. J. Physiol. Heart Circ. Physiol., 278, H2039-49 (2000) · doi:10.1152/ajpheart.2000.278.6.H2039
[45] R, Comparison of entropy and complexity measures for the assessment of depth of sedation, IEEE Trans. Biomed. Eng., 53, 1067-1077 (2006) · doi:10.1109/TBME.2006.873543
[46] A, The Multiscale Entropy Algorithm and Its Variants: A Review, Entropy, 17, 3110-3123 (2015) · doi:10.3390/e17053110
[47] H, Amplitude- and Fluctuation-Based Dispersion Entropy, Entropy, 20, 210 (2018) · doi:10.3390/e20030210
[48] J, Refined multiscale entropy: Application to 24-h Holter recordings of heart period variability in healthy and aortic stenosis subjects, IEEE Trans. Biomed., 56, 2202-2213 (2009) · doi:10.1109/TBME.2009.2021986
[49] J, Heart rate variability characterized by refined multiscale entropy applied to cardiac death in ischemic cardiomyopathy patients, Comput. Cardiol., 37, 65-68 (2010)
[50] W, Nonlinear EEG biomarker profiles for autism and absence epilepsy, Neuropsychiatric Electrophysiology, 3, 1 (2017) · doi:10.1186/s40810-017-0023-x
[51] W, EEG Analytics for Early Detection of Autism Spectrum Disorder: A data-driven approach, Sci. Rep., 8, 6828 (2018) · doi:10.1038/s41598-018-24318-x
[52] S, Modified multiscale entropy for short-term time series analysis, Physica A, 392, 15865-5873 (2013) · doi:10.1016/j.physa.2013.07.075
[53] S, Time series analysis using composite multiscale entropy, Entropy, 15, 1069-1084 (2013) · Zbl 1298.65020 · doi:10.3390/e15031069
[54] S, Analysis of complex time series using refined composite multiscale entropy, Phys. Lett. A, 378, 1369-1374 (2014) · Zbl 1323.94061 · doi:10.1016/j.physleta.2014.03.034
[55] S, Modified multiscale entropy for short-term time series analysis, Phys. A, 392, 5865-5873 (2013) · doi:10.1016/j.physa.2013.07.075
[56] Y, Application of a modified entropy computational method in assessing the complexity of pulse wave velocity signals in healthy and diabetic subjects, Entropy, 16, 4032-4043 (2014) · doi:10.3390/e16074032
[57] Y, Hierarchical entropy analysis for biological signals, J. Comput. Appl. Math., 236, 728-742 (2011) · Zbl 1230.92025 · doi:10.1016/j.cam.2011.06.007
[58] H, Measuring time series regularity using nonlinear similarity-based sample entropy, Phys. Lett. A, 372, 7140-7146 (2008) · Zbl 1227.37009 · doi:10.1016/j.physleta.2008.10.049
[59] M, Multivariate multiscale entropy analysis, IEEE Signal Process. Lett., 19, 91-94 (2012) · doi:10.1109/LSP.2011.2180713
[60] M, Generalized multiscale entropy analysis: Application to quantifying the complex volatility of human heartbeat time series, Entropy, 17, 1197-1203 (2015) · doi:10.3390/e17031197
[61] L, Efficient Computation of Multiscale Entropy over Short Biomedical Time Series Based on Linear State-Space Models, Complexity, 2017, 1768264 (2017) · Zbl 1380.93228 · doi:10.1155/2017/1768264
[62] T, Complexity of spontaneous brain activity in mental disorders, Prog. Neuropsychopharmacol. Biol. Psychiatry, 45, 258-266 (2013) · doi:10.1016/j.pnpbp.2012.05.001
[63] N, The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis, Proc. Math. Phys. Eng. Sci., 454, 903-995 (1998) · Zbl 0945.62093 · doi:10.1098/rspa.1998.0193
[64] N, A review on Hilbert-Huang transform: Method and its applications to geophysical studies, Rev. Geophys., 46, 228-251 (2008) · doi:10.1029/2007RG000228
[65] F. R. Kschischang, The Hilbert Transform. Toronto: University of Toronto, 2006.
[66] E. Abdulhay, P.Y. Guméry, J. Fontecave, P. Baconnier, Cardiogenic oscillations extraction in inductive plethysmography: Ensemble empirical mode decomposition, <i>Annu. Int. Conf. IEEE Eng. Med. Biol. Soc.</i>, Minnesota, USA, 2009, 2240-2243. <a href=“https://doi.org/10.1109/IEMBS.2009.5335004” target=“_blank”>https://doi.org/10.1109/IEMBS.2009.5335004</a>
[67] X, Sparse Principal Component Analysis via Fractional Function Regularity, Math. Probl. Eng., 2020, 7874140 (2020) · Zbl 1459.62098 · doi:10.1155/2020/7874140
[68] C, Performance Evaluation of Training Algorithms in Backpropagation Neural Network Approach to Blast-Induced Ground Vibration Prediction, Ghana Mining J., 20, 20-33 (2020) · doi:10.4314/gm.v20i1.3
[69] K, Reduced visual evoked potential amplitude in autism spectrum disorder. a variability effect?, Translational Psychiatry, 9, 341 (2019) · doi:10.1038/s41398-019-0672-6
[70] S, Connectivity in the human brain dissociates entropy and complexity of auditory inputs, NeuroImage, 31, 292-300 (2015) · doi:10.1016/j.neuroimage.2014.12.048
[71] P, A big-world network in ASD: dynamical connectivity analysis reflects a deficit in longrange connections and an excess of short-range connections, Neuropsychologia, 49, 254-263 (2015) · doi:10.1016/j.neuropsychologia.2010.11.024
[72] H, Altered Time-Frequency Feature in Default Mode Network of Autism Based on Improved Hilbert-Huang Transform, IEEE J. Biomed. Health Informatics, 25, 485-492 (2021) · doi:10.1109/JBHI.2020.2993109
[73] T, Conditional entropy approach to analyze cognitive dynamics in autism spectrum disorder, Neurol. Res., 42, 869-878 (2020) · doi:10.1080/01616412.2020.1788844
[74] E, Brainwaves Analysis Using Spectral Entropy in Children with Autism Spectrum Disorders (ASD), J. phys. Conf. ser., 1505, 012070 (2020) · doi:10.1088/1742-6596/1505/1/012070
[75] E, Entropy of Fourier coefficients of periodic musical objects, J. Math. Music, 15, 235-246 (2021) · Zbl 1481.00009 · doi:10.1080/17459737.2020.1777592
[76] D, Entropy analysis of the EEG background activity in Alzheimer’s disease patients, Physiol. Meas., 27, 241-253 (2006) · doi:10.1088/0967-3334/27/3/003
[77] J, Global Synchronization of Multichannel EEG Based on Rényi Entropy in Children with Autism Spectrum Disorder, Appl. Sci., 7, 257 (2017) · doi:10.3390/app7030257
[78] E, Resting State EEG-based Diagnosis of Autism via Elliptic Area of Continuous Wavelet Transform Complex Plot, J. Intell. fuzzy syst., 39, 8599-8607 (2020) · doi:10.3233/JIFS-189176
[79] R, Changes in EEG complexity with electroconvulsive therapy in a patient with autism spectrum disorders: a multiscale entropy approach, Front. Hum. Neurosci., 9, 25767444 (2015) · doi:10.3389/fnhum.2015.00106
[80] S. Thapaliya, S. Jayarathna, M. Jaime, Evaluating the EEG and eye movements for autism spectrum disorder, <i>2018 IEEE Int. Conf. Big Data</i>, Seattle, WA, USA, 2018. <a href=“https://doi.org/10.1109/BigData.2018.8622501” target=“_blank”>https://doi.org/10.1109/BigData.2018.8622501</a>
[81] J, Robust features for the automatic identification of autism spectrum disorder in children, J. Neurodev. Disord., 6, 1-12 (2014) · doi:10.1186/1866-1955-6-1
[82] H, Intrinsic mode entropy for nonlinear discriminant analysis, IEEE Signal Process. Lett., 14, 297-300 (2007) · doi:10.1109/LSP.2006.888089
[83] M, Adaptive multiscale entropy analysis of multivariate neural data, IEEE Trans. Biomed. Eng., 59, 12-15 (2012) · doi:10.1109/TBME.2011.2162511
[84] O, A Personalized Autism Diagnosis CAD System Using a Fusion of Structural MRI and Resting-State Functional MRI Data., Front. Psychiatry, 10, 1-16 (2021) · doi:10.3389/fpsyt.2019.00392
[85] O, et.al. A Comprehensive Framework for Differentiating Autism Spectrum Disorder From Neurotypicals by Fusing Structural MRI and Resting State Functional MRI, Seminars in Pediatric Neurology., 34, 100805 (2020) · doi:10.1016/j.spen.2020.100805
[86] K. Barik, K. Watanabe, J. Bhattacharya, G. Saha, Classification of Autism in Young Children by Phase Angle Clustering in Magnetoencephalogram Signals, <i>2020 National Conf. Commun. (NCC)</i>, Kharagpur, India, 2020, 1-6. <a href=“https://doi.org/10.1109/NCC48643.2020.9056022” target=“_blank”>https://doi.org/10.1109/NCC48643.2020.9056022</a>
This reference list is based on information provided by the publisher or from digital mathematics libraries. Its items are heuristically matched to zbMATH identifiers and may contain data conversion errors. In some cases that data have been complemented/enhanced by data from zbMATH Open. This attempts to reflect the references listed in the original paper as accurately as possible without claiming completeness or a perfect matching.