Search Results - "преобразование Гильберта-Хуанга"

1

Academic Journal

Improvement of the ultrasonic testing method for materials with significant attenuaton

Authors: Derhunov, Oleksii, Kuts, Yurii, Monchenko, Olena, Shenhur, Svitlana, Oliinyk, Yurii

Source: Eastern-European Journal of Enterprise Technologies; Том 1, № 9 (91) (2018): Information and controlling system; 54-61
Восточно-Европейский журнал передовых технологий; Том 1, № 9 (91) (2018): Информационно-управляющие системы; 54-61
Східно-Європейський журнал передових технологій; Том 1, № 9 (91) (2018): Інформаційно-керуючі системи; 54-61

Subject Terms: UDC 621.391.83:004.94(045), ультразвуковая толщинометрия, фазовая характеристика сигнала, преобразование Гильберта, преобразование Гильберта-Хуанга, медианная фильтрация сигналов, 0203 mechanical engineering, 0103 physical sciences, 0211 other engineering and technologies, 0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology, ультразвукова товщинометрія, фазова характеристика сигналу, перетворення Гільберта, перетворення Гільберта-Хуанга, медіанна фільтрація сигналів, 01 natural sciences, ultrasonic thickness measurement, signal phase characteristic, Hilbert transform, Hilbert-Huang transform, median filtering of signals

File Description: application/pdf

Access URL: http://journals.uran.ua/eejet/article/download/122858/119044
http://journals.uran.ua/eejet/article/view/122858
http://journals.uran.ua/eejet/article/download/122858/119044
http://journals.uran.ua/eejet/article/view/122858

View record at OpenAIRE Full Text Finder

2

Academic Journal

Instantaneous EEG Signal Analysis Based on Empirical Mode Decomposition Applied to Burst-Suppression in Propofol Anaesthesia ; Анализ мгновенных параметров сигнала ЭЭГ на основании разложения по эмпирическим модам в применении к паттерну «вспышка-подавление» при анестезии пропофолом

Authors: G. Sobolova, M. S. Fabus, M. Fischer, M. Drobny, B. Drobna-Saniova, Г. Соболова, М. С. Фабус, М. Фишер, М. Дробны, Б. Дробна-Саниова

Contributors: This research was funded in whole, or in part, by the Wellcome Trust Grant number 203139/Z/16/Z . For the purpose of open access, the authors have applied a CC BY public copyright licence to any Author Accepted Manuscript version arising from this submission. The authors also give thanks to Andrew Quinn for discussions about EMD and the Python EMD toolbox., Это исследование полностью или частично финансировалось Wellcome Trust номер гранта 203139/Z/16/Z . В целях обеспечения открытого доступа авторы согласны с использованием любой версии данного документа и его производных согласно лицензии общественного авторского права CC. Авторы также благодарят Эндрю Куинна за обсуждение аспектов РЭМ и инструментария Python РЭМ.

Source: General Reanimatology; Том 17, № 5 (2021); 65-79 ; Общая реаниматология; Том 17, № 5 (2021); 65-79 ; 2411-7110 ; 1813-9779

Subject Terms: пропофол, EMD, HHT, Burst-Suppression, Intrinsic Mode Functions, Propofol, электроэнцефалография, разложение по эмпирическим модам, ННТ, преобразование Гильберта-Хуанга, паттерн «вспышка-подавление», внутренние модовые функции

File Description: application/pdf

Relation: https://www.reanimatology.com/rmt/article/view/2136/1556; https://www.reanimatology.com/rmt/article/view/2136/1557; Sanders R.D., Tononi G, Laureys S., Sleigh J.W. Unresponsiveness # Unconsciousness. Anesthesiology. 2012; 116 (4): 946-959. DOI:10.1097/ALN.0b013e318249d0a7.; Ni Mhuircheartaigh R., Warnaby C., Rogers R., Jbabdi S., Tracey I. Slow-Wave Activity Saturation and Thalamocortical Isolation During Propofol Anesthesia in Humans. Sci Transl Med. 2013; 23; 5 (208): 208ra148. DOI:10.1126/scitranslmed.3006007.; Warnaby C.E., Sleigh J.W., Hight D., Jbabdi S., Tracey I. Investigation of Slow-wave Activity Saturation during Surgical Anesthesia Reveals a Signature of Neural Inertia in Humans. Anesthesiology. 2017; 127 (4): 645-657. DOI:10.1097/ALN.0000000000001759.; Marchant N., Sanders R., Sleigh J., Vanhaudenhuyse A., Bruno A.U., Brichant J.F., Steven Laureys S., Bonhomme V. How Electroencephalography Serves the Anesthesiologist. Clin EEG Neurosci. 2014; 45 (1): 22-32. DOI:10.1177/1550059413509801.; Purdon P.L., Sampson A., Pavone K.J., Brown E.N. Clinical Electroencephalography for Anesthesiologists: Part I: Background and Basic Signatures. Anesthesiology. 2015; 123 (4): 937-960. DOI:10.1097/ALN.0000000000000841.; Marcuse L.V., Fields M.C., Jenna J. Y. The EEG in other neurological and medical conditions and in status epilepticus. In: Rowan's Primer of EEG. 2nd ed. London: Elsevier, 2016: 157-173.; Gropper M. A. Miller's Anesthesia, 2-Volume Set E-Book [electronic resource], 9th ed. 2019.; Amzica F. What does burst suppression really mean? Epilepsy Behav. 2015; 49: 234-237. DOI:10.1016/j.yebeh.2015.06.012.; Hogan J., Sun H., Aboul Nour H., Jing J., Tabaeizadeh M., Shoukat M., Javed F., Kassa S., Edhi., Bordbar E., Gallagher J., Moura V. J., Ghanta M., Shao Y-P., Akeju O., Cole A.J., Rosenthal E.S., Zafar S., Westover M.B. Burst Suppression: Causes and Effects on Mortality in Critical Illnes. Neurocrit Care 2020; 33: 565-574. DOI:10.1007/s12028-020-00932-4; Pontificia Universidad Catolica de Chile,. Study of the Association Between Burst Suppression During Anesthetic Induction With Propofol in Cardiac Surgery in Patients Over 65 Years of Age With Postoperative Delirium. Clinical trials.gov. Clinical trial registration NCT04713644, Mar. 2021. Accessed: Apr. 14, 2021. [Online]. Available: https: //clinicaltrials.gov/ct2/show/NCT04713644.; Soehle M., Dittmann A., Ellerkmann R.K., Baumgarten G., Putensen C., Guenther U. Intraoperative burst suppression is associated with postoperative delirium following cardiac surgery: a prospective, observational study. BMC Anesthesiol. 2015; 15 (61): 1-8. DOI:10.1186/s12871-015-0051-7.; Huang N.E., Shen Z., Long S., Wu M.C., Shih H.H., Zheng Q., Yen N. C., Tung C.C., Liu H.H. The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis', Proc. R. Soc. Lond. Ser. Math. Phys. Eng. Sci. 1998; 454 (1971): 903995. DOI:10.1098/rspa.1998.0193.; Huang N.E., Wu Z. A review on Hilbert-Huang transform: Method and its applications to geophysical studies. Rev. Geophys. 2008; 46 (2): 1-23. DOI:10.1029/2007RG000228.; Kortelainen J., Vayrynen E. Assessing EEG slow wave activity during anesthesia using Hilbert-Huang Transform. Annu Int Conf IEEE Eng Med Biol Soc. 2015; 2015: 117-120. DOI:10.1109/EMBC.2015.7318314.; Barbosh M., Singh P., Sadhu A. Empirical mode decomposition and its variants: a review with applications in structural health monitoring. Smart Mater. Struct. 2020; 29 (9): 093001. DOI:10.1088/1361-665X/aba539.; Bueno-Lopez M., Giraldo E., Molinas M., Fosso O.B. The Mode Mixing Problem and its Influence in the Neural Activity Reconstruction. IAENG International Journal of Computer Science. 2019; 46 (3): 11.; Yang Y., DengJ., Wu C. Analysis of Mode Mixing Phenomenon in the Empirical Mode Decomposition Method. In 2009 Second International Symposium on Information Science and Engineering, Shanghai, China; 2009: 553-556. DOI:10.1109/ISISE.2009.19.; Wu Z., Huang N.E. Ensemble empirical mode decomposition: a noise-assisted data analysis method. Adv. Adapt. Data Anal. 2009; 01 (01): 1-41. DOI:10.1142/S1793536909000047.; Deering R., Kaiser J.F. The use of a masking signal to improve empirical mode decomposition. In: Proceedings. (ICASSP '05). IEEE International Conference on Acoustics, Speech, and Signal Processing. 2005; 4: iv/485-iv/488. DOI:10.1109/ICASSP.2005.1416051.; Wang Y.H., Hu K., Lo M.T. Uniform Phase Empirical Mode Decomposition: An Optimal Hybridization of Masking Signal and Ensemble Approaches'. IEEE Access. 2018; 6: 34819-34833. DOI:10.1109/ACCESS.2018.2847634.IEEE Access 2018; 6: 34819-34833.; Gramfort A., Luessi M., Larson E., Engemann D.A., Strohmeier D., Brodbeck C.H., Goj R., Brooks T., Parkkonen L., Hamalainen M. MEG and EEG data analysis with MNE-Python. Front Neurosci. 2013; 26 (7): 1-13. DOI: 1O.3389/fnins.2O13.00267.; Quinn A.J., Lopes dos Santos V., Dupret D., Nobre A.CH., Mark W. Woolrich M.W. EMD: Empirical Mode Decomposition and Hilbert-Huang Spectral Analyses in Python. J Open Source Softw. 2021; 6 (59): 1-8. DOI:10.21105/joss.02977.; Quinn A.J, Lopes-dos-Santos V., Huang N., Liang W.K., Juan Ch.H., Yeh J-R., Nobre A.C., Dupret D., Woolrich M.W. Within-cycle instantaneous frequency profiles report oscillatory waveform dynamics. bio-Rxiv. 2021.04.12.439547. DOI:10.1101/2021.04.12.439547.; Kenny J.D., Westover M.B., Ching S., Brown E.N., Solt K. Propofol and sevoflurane induce distinct burst suppression patterns in rats. Front. Syst. Neurosci. 2014; 8 (237): 1-13. DOI:10.3389/fnsys.2014.00237.; Vijn P.C., Sneyd J.R. I.v. anaesthesia and EEG burst suppression in rats: bolus injections and closed-loop infusions. Br. J. Anaesth. 1998; 81 (3): 415-421. DOI:10.1093/bja/81.3.415.; Freye E., Levy J.V. Cerebral Monitoring in the Operating Room and the Intensive Care Unit: An Introductory for the Clinician and a Guide for the Novice Wanting to Open a Window to the Brain. J. Clin. Monit. Comput. 2005; 19 (1): 1-76. DOI:10.1007/s10877-005-0712-z.; Hesse S., Kreuzer M., Hight D., Gaskell A., Devari P., Singh D., Taylor N.B., Whalin M.K., Lee S., Sleigh J.W., Garcia P.S.Association of electroencephalogram trajectories during emergence from anaesthesia with delirium in the postanaesthesia care unit: an early sign of postoperative complications'. Br. J. Anaesth. 2019; 122 (5): 622-634. doi:10.1016/j.bja.2018.09.016.; Hight D., L. J. Voss L.J., Garcia P.S., J. Sleigh J.W. Changes in Alpha Frequency and Power of the Electroencephalogram during VolatileBased General Anesthesia. Front. Syst. Neurosci. 2017; 11 (36): DOI:10.1016/j.bja.2018.09.016.10 doi:10.3389/fnsys.2017.00036.; Sleigh J., Pullon R.M., Vlisides P.E., Warnaby C.E. Electroencephalographic slow wave dynamics and loss of behavioural responsiveness induced by ketamine in human volunteers. Br. J. Anaesth. 2019; 123 (5): 592-600. DOI:10.1016/j.bja.2019.07.021.; Massimini M., Huber R., Ferrarelli F., Hill S., Giulio T. The Sleep Slow Oscillation as a Traveling Wave. J. Neurosci. 2004; 24 (31): 6862-6870. DOI:10.1523/JNEUROSCI.1318-04.2004.; Murphy M., Bruno M-A., Riedner B.A., Boveroux P., Noirhomme Q., Landsness E., Brichant J-F., Phillips Ch., Massimini M., Laureys S., Tononi G., Boly M. Propofol Anesthesia and Sleep: A High-Density EEG Study. Sleep. 2011; 34 (3): 283-291A: DOI:10.1093/sleep/34.3.283.; Lewis L.D., Ching Sh., Weiner V.S., Peterfreund R.A., Eskandar E.N., Cash S.S., Brown E.N., Purdon P.L. Local cortical dynamics of burst suppression in the anaesthetized brain. Brain. 2013; 136 (9) 27272737. DOI:10.1093/brain/awt174.; Ming Q., Liou J-Y., Yang F., Li J., Chu Ch., Zhou Q., Wu D., Xu S., Luo P. Liang J., Li D., Pryor K.O., Lin W., Schwartz T., Ma H. Isoflurane-In-duced Burst Suppression Is a Thalamus-Modulated, Focal-Onset Rhythm With Persistent Local Asynchrony and Variable Propagation Patterns in Rats. Front. Syst. Neurosci. 2021; 14: 1-11. DOI:10.3389/fnsys.2020.599781.; Lang X., Zheng Q., Zhang Z., Lu S., Xie L., Horch A., Su H. Fast Multivariate Empirical Mode Decomposition. IEEE Access PP; 2018; 99: 1-18. DOI:10.1109/ACCESS.2018.2877150.; Rehman N., Mandic D.P. Multivariate empirical mode decomposition. Proc. R. Soc. Math. Phys. Eng. Sci. 2010; 466 (2117): 1291-1302.DOI:10.1098/rspa.2009.0502.; Wu Z., Feng J., Qiao F., Tan Z.-M. Fast multidimensional ensemble empirical mode decomposition for the analysis of big spatio-temporal datasets. Philos. Trans. R. Soc. Math. Phys. Eng. Sci. 2016; 374 (2065): 20150197. DOI:10.1098/rsta.2015.0197.; Amzica F., Steriade M. Electrophysiological correlates of sleep delta waves. Electroencephalogr. Clin. Neurophysiol. 1998; 107 (2): 69-83. DOI:10.1016/S0013-4694 (98)00051-0.; Chen S-J., Peng Ch-J., Chen Y-Ch., Hwang Y-R., Lai Y-S., Fan S-Z., Jen K-K. Comparison of FFT and marginal spectra of EEG using empirical mode decomposition to monitor anesthesia. Comput. Methods Programs Biomed. 2016; 137: 77-85. DOI:10.1016/j.cmpb.2016.08.024.; Li Ch., Li D., Liang Z., Voss L.J., Sleigh J.W. Analysis of depth of anesthesia with Hilbert-Huang spectral entropy. Clin. Neurophysiol. 2008; 119 (11): 2465-2475. DOI:10.1016/j.clinph.2008.08.006.; Cole S. R., Voytek B. Brain Oscillations and the Importance of Waveform Shape. Trends Cogn. Sci. 2017; 21 (2): 137-149. DOI:10.1016/j.tics.2016.12.008.; van Ede F., Quinn A.J., Woolrich M.W., Nobre A.C. Neural Oscillations: Sustained Rhythms or Transient Burst-Events? Trends Neurosci. 2018; 41 (7): 415-417. DOI:10.1016/j.tins.2018.04.004.; Vidaurre D., Quinn A.J., Baker A.P., Dupret D., Tejero-Cantero A.,Wo-olrich M.W. Spectrally resolved fast transient brain states in electrophysiological data. NeuroImage. 2016; 126: 81-95. DOI:10.1016/j.ne-uroimage.2015.11.047.; Bartz S., Avarvand F.S., Leicht G., Nolte G. Analyzing the waveshape of brain oscillations with bicoherence. NeuroImage. 2019; 188: 145-160. DOI:10.1016/j.neuroimage.2018.11.045.; Quinn A.J. Within-cycle instantaneous frequency profiles report oscillatory waveform dynamics. bioRxiv, 2012: 2021.04.12.439547. DOI:10.1101/2021.04.12.439547.; Soler A., Munoz-Gutierrez PA., Bueno-Lopez M., Giraldo E., Molinas M. Low-Density EEG for Neural Activity Reconstruction Using Multivariate Empirical Mode Decomposition. Front. Neurosci; 2020: 14 DOI:10.3389/fnins.2020.00175.; Kortelainen J., Vayrynen E. Assessing EEG slow wave activity during anesthesia using Hilbert-Huang Transform. in 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). 2015: 117-120. doi:10.1109/EMBC.2015.7318314.; Cole S.R., van der Meij R., Peterson E.J., de Hemptinne C., Starr P.A., Voytek B. Nonsinusoidal Beta Oscillations Reflect Cortical Pathophysiology in Parkinson's Disease. J. Neurosci. 2017; 37 (18): 4830-4840. DOI:10.1523/JNEUROSCI.2208-16.2017.; Amzica F., Steriade M. ‘Electrophysiological correlates of sleep delta waves', Electroencephalogr. Clin. Neurophysiol. 1998; 107 (2): 69-83. DOI:10.1016/S0013-4694(98)00051-0.; Huang N.E., Shen Zh., Long S.R., Wu M.L.C. The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-statio-nary time series analysis. Lond. Ser. Math. Phys. Eng. Sci. 1998; 454 (1971): 903-995. DOI:10.1098/rspa.1998.0193.; Wang Y.-H, Hu K., Lo M.-T. Uniform Phase Empirical Mode Decomposition: An Optimal Hybridization of Masking Signal and Ensemble Approaches', IEEE Access. 2018; 6: 34819-34833. DOI:10.1109/ACCESS.2018.2847634.; Yang Y., Deng J., Wu C. Analysis of Mode Mixing Phenomenon in the Empirical Mode Decomposition Method', in 2009 Second International Symposium on Information Science and Engineering, Shanghai, China. 2009: 553-556. DOI:10.1109/ISISE.2009.19.; Deering R., Kaiser J.F. The use of a masking signal to improve empirical mode decomposition', in Proceedings. (ICASSP '05). IEEE International Conference on Acoustics, Speech, and Signal Processing. 2005; 4: iv/485-iv/488. DOI:10.1109/ICASSP.2005.1416051.; Wu Z., Huang N.E. Ensemble empirical mode decomposition: a noise-assisted data analysis method. Adv. Adapt. Data Anal. 2009: 01 (01): 1-41. DOI:10.1142/S1793536909000047.; Fabus M.S., Quinn A.J., Warnaby C.E., Woolrich M.W. Automatic decomposition of electrophysiological data into distinct non-sinusoidal oscillatory modes. bioRxiv, 2021: 2021.07.06.451245. DOI:10.1101/2021.07.06.451245.; Lo M.-T., Novak V., Peng C.-K., Liu Y., Hu K. Nonlinear phase interaction between nonstationary signals: A comparison study of methods based on Hilbert-Huang and Fourier transforms. Phys. Rev. E Stat. Nonlin. Soft Matter Phys. 2009; 79 (6 Pt 1): 061924 DOI:10.1103/Phys-RevE.79.061924.; https://www.reanimatology.com/rmt/article/view/2136

Availability: https://www.reanimatology.com/rmt/article/view/2136
https://doi.org/10.15360/1813-9779-2021-5-65-79

View record from BASE

3

Academic Journal

MAXIMUM WATER RUNOFF FORECAST OF THE DNIEPER RIVER AT THE RECHITSA STATION

Source: Vestnik of Brest State Technical University; No. 1(124) (2021): Vestnik of Brest State Technical University; 69-76
Вестник Брестского государственного технического университета; № 1(124) (2021): Вестник Брестского государственного технического университета; 69-76

Subject Terms: empirical mode decomposition, Hilbert-Huang transform, hybrid model, EMD, ARIMA, river runoff forecast estimates, эмпирическая модовая декомпозиция, преобразование Гильберта-Хуанга, гибридная модель, EMD, ARIMA, прогнозные оценки речного стока

File Description: application/pdf

Access URL: https://journal.bstu.by/index.php/bstu_herald/article/view/492

View record at OpenAIRE

4

Academic Journal

Застосування часо-частотних перетворень акустичних сигналів генерованих багатофазним потоком для прогнозування корозійно-ерозійних пошкоджень трубопроводів

Subject Terms: багатофазний потік, скорость фазы потока, phase flow velocity, коррозия-эрозия, Hilbert-Huang transform, многофазный поток, швидкість фази потоку, преобразование Гильберта-Хуанга, корозія-ерозія, Multiphase flow, 6. Clean water, перетворення Гілберта-Хуанга, corrosion-erosion

Access URL: http://elar.nung.edu.ua/handle/123456789/2957

5

Academic Journal

Декомпозиция сигнала на внутренние модовые функции с использованием гетерогенных вычислительных систем

Subject Terms: материалы конференций, нелинейные системы, преобразование Гильберта-Хуанга

Access URL: https://openrepository.ru/article?id=212474

6

Academic Journal

Програмний комплекс оброблення циклічних сигналів неруйнівного контролю ; Программный комплекс обработки циклических сигналов неразрушающего контроля ; Software for cyclic signal processing in non-destructive testing

Authors: О.В. Дергунов, А.В. Дергунов, O.V. Dergunov

Source: Системи обробки інформації. — 2015. — № 9(134). 19-23 ; Системы обработки информации. — 2015. — № 9(134). 19-23 ; Information Processing Systems. — 2015. — № 9(134). 19-23 ; 1681-7710

Subject Terms: Обробка інформації в складних технічних системах, УДК 620.19: 004.942(043.2), програмний комплекс, обробка сигналів, фазова характеристика, перетворення Гільберта-Хуанга, моделювання, программный комплекс, обработка сигналов, анализ фазовой характеристики сигнала, преобразование Гильберта-Хуанга, моделирование, software package, signal processing, phase characteristic analysis, Hibert-Huang transform, signal modeling