Wavelet entropy: A new tool for analysis of short duration brain electrical signals
Since traditional electrical brain signal analysis is mostly qualitative, the development of new quantitative methods is crucial for restricting the subjectivity in the study of brain signals. These methods are particularly fruitful when they are strongly correlated with intuitive physical concepts...
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| Formato: | Capítulo de libro |
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2001
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| Acceso en línea: | Registro en Scopus DOI Handle Registro en la Biblioteca Digital |
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| LEADER | 10995caa a22011417a 4500 | ||
|---|---|---|---|
| 001 | PAPER-2101 | ||
| 003 | AR-BaUEN | ||
| 005 | 20230518203129.0 | ||
| 008 | 190411s2001 xx ||||fo|||| 00| 0 eng|d | ||
| 024 | 7 | |2 scopus |a 2-s2.0-0035970026 | |
| 040 | |a Scopus |b spa |c AR-BaUEN |d AR-BaUEN | ||
| 030 | |a JNMED | ||
| 100 | 1 | |a Rosso, O.A. | |
| 245 | 1 | 0 | |a Wavelet entropy: A new tool for analysis of short duration brain electrical signals |
| 260 | |c 2001 | ||
| 270 | 1 | 0 | |m Rosso, O.A.; Facultad de Ciencias Exactas, Instituto de Cálculo, Universidad de Buenos Aires, 1428 Buenos Aires, Argentina; email: rosso@ulises.ic.fcen.uba.ar |
| 506 | |2 openaire |e Política editorial | ||
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| 504 | |a Casdagli, M.C., Iasemedis, L.D., Savit, R.S., Gilmore, R.L., Roper, S.N., Sackellares, J.C., Non-linearity in invasive EEG recordings from patients with temporal lobe epilepsy (1997) Electroenceph. Clin. Neurophysiol., 102, pp. 98-105 | ||
| 504 | |a Daubechies, I., (1992), Ten Lectures on Wavelets, Philadelphia: SIAM; Elbert, T., Ray, W.J., Kowalik, Z.J., Skinner, J.E., Graf, K.E., Birbaumer, N., Chaos and physiology: deterministic chaos in excitable cell assemblies (1994) Physiol. Rev., 74, pp. 1-47 | ||
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| 504 | |a Inouye, T., Shinosaki, K., Sakamoto, H., Toi, S., Ukai, S., Iyama, A., Katzuda, Y., Hirano, M., Quantification of EEG irregularity by use of the entropy of power spectrum (1991) Electroenceph. Clin. Neurophysiol., 79, pp. 204-210 | ||
| 504 | |a Inouye, T., Shinosaki, K., Imaya, A., Matsumoto, Y., Localization of activated areas and directional EEG patterns during mental arithmetic (1993) Electroenceph. Clin. Neurophysiol., 86, pp. 224-230 | ||
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| 504 | |a Mallat, S., (1999), A Wavelet Tour of Signal Processing, second ed., San Diego: Academic Press; (1987), Niedermeyer E., Lopes da Silva F.H. (Eds.), Electroencephalography, Basic Principles, Clinical Applications, and Related Field, Baltimore: Urban & Schwarzenberg; Nunez, P.L., (1981), Electric Fields of the Brain: The Neurophysics of EEG, New York/Oxford: Oxford University Press; Nunez PL. Toward a quantitative description of large scale neocortical dynamic function and EEG. Behav Brain Sci 2000, in press; Pjin, J.P., Van Neerven, J., Noestt, A., Lopes da Silva, F.H., Chaos or noise in EEG signals: dependence on state and brain site (1991) Electroenceph. Clin. Neurophysiol., 79, pp. 371-381 | ||
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| 504 | |a Quian Quiroga R., Rosso O.A., Başar E., Schürmann M. Wavelet-entropy in event-related potentials: A new method shows ordering of EEG-oscillations. Biol Cyber 2000a, in press; Quian Quiroga R., Arnhold J., Lehnertz K., Grassberger P. Kullback-Leibler and renormalised entropy: applications to EEG of epilepsy patients. Phys Rev E 2000b, in press; Rockstroh, B., Elbert, T., Canavan, A., Lutzenberger, W., Birbaumer, N., (1989), Slow Cortical Potentials and Behaviour, Baltimore: Urban & Schwarzenberg; Rosso OA, Blanco S. Characterization of dynamical evolution of electroencephalogram time series, 1999, unpublished; Sayers, B., Beagley, H.A., Riha, J., The mechanism of auditory evoked EEG response (1974) Nature, 247, pp. 481-483 | ||
| 504 | |a Shannon CE. A mathematical theory of communication. Bell Syst Technol J 1948;27:379-23, 623-56 | ||
| 520 | 3 | |a Since traditional electrical brain signal analysis is mostly qualitative, the development of new quantitative methods is crucial for restricting the subjectivity in the study of brain signals. These methods are particularly fruitful when they are strongly correlated with intuitive physical concepts that allow a better understanding of brain dynamics. Here, new method based on orthogonal discrete wavelet transform (ODWT) is applied. It takes as a basic element the ODWT of the EEG signal, and defines the relative wavelet energy, the wavelet entropy (WE) and the relative wavelet entropy (RWE). The relative wavelet energy provides information about the relative energy associated with different frequency bands present in the EEG and their corresponding degree of importance. The WE carries information about the degree of order/disorder associated with a multi-frequency signal response, and the RWE measures the degree of similarity between different segments of the signal. In addition, the time evolution of the WE is calculated to give information about the dynamics in the EEG records. Within this framework, the major objective of the present work was to characterize in a quantitative way functional dynamics of order/disorder microstates in short duration EEG signals. For that aim, spontaneous EEG signals under different physiological conditions were analyzed. Further, specific quantifiers were derived to characterize how stimulus affects electrical events in terms of frequency synchronization (tuning) in the event related potentials. Copyright © 2001 Elsevier Science B.V. |l eng | |
| 536 | |a Detalles de la financiación: James S. McDonnell Foundation, 98-66 EE-GLO-04 | ||
| 536 | |a Detalles de la financiación: Fundación Alberto J. Roemmers | ||
| 536 | |a Detalles de la financiación: Deutsche Forschungsgemeinschaft, 436-BUL-113/105 | ||
| 536 | |a Detalles de la financiación: ARG-4-G0A-6A | ||
| 536 | |a Detalles de la financiación: Consejo Nacional de Investigaciones Científicas y Técnicas | ||
| 536 | |a Detalles de la financiación: Consejo Nacional de Investigaciones Científicas y Técnicas, PIP 0029/98 | ||
| 536 | |a Detalles de la financiación: This work was supported by the Consejo Nacional de Investigaciones Cientificas y Técnicas (CONICET), Argentina (PIP 0029/98), Fundación Alberto J. Roemmers, Argentina, the International Office of BMBF, Germany (ARG-4-G0A-6A), the Deutsche Forschungsgemeinschaft, Germany (436-BUL-113/105), and James McDonnell Foundation, USA (98-66 EE-GLO-04). | ||
| 593 | |a Facultad de Ciencias Exactas y Naturales, Instituto de Cálculo, Universidad de Buenos Aires, 1428 Buenos Aires, Argentina | ||
| 593 | |a Institute of Physiology, Bulgarian Academy of Sciences, Acad. G. Bonchev Str., bl. 23, 1113 Sofia, Bulgaria | ||
| 593 | |a Institute of Physiology, Medical University Lübeck, Ratzeburger Alle 160, D-23538 Lübeck, Germany | ||
| 593 | |a TÜBITAK Brain Dynamics Research Unit, Ankara, Turkey | ||
| 690 | 1 | 0 | |a EEG, EVENT-RELATED POTENTIALS (ERP) |
| 690 | 1 | 0 | |a SIGNAL ENTROPY |
| 690 | 1 | 0 | |a TIME-FREQUENCY SIGNAL ANALYSIS |
| 690 | 1 | 0 | |a VISUAL EVOKED POTENTIAL |
| 690 | 1 | 0 | |a WAVELET ANALYSIS |
| 690 | 1 | 0 | |a ADULT |
| 690 | 1 | 0 | |a ARTICLE |
| 690 | 1 | 0 | |a AUDITORY STIMULATION |
| 690 | 1 | 0 | |a CONTROLLED STUDY |
| 690 | 1 | 0 | |a CORTICAL SYNCHRONIZATION |
| 690 | 1 | 0 | |a ELECTROENCEPHALOGRAM |
| 690 | 1 | 0 | |a ELECTROENCEPHALOGRAPHY |
| 690 | 1 | 0 | |a ENERGY |
| 690 | 1 | 0 | |a ENTROPY |
| 690 | 1 | 0 | |a EVENT RELATED POTENTIAL |
| 690 | 1 | 0 | |a EVOKED VISUAL RESPONSE |
| 690 | 1 | 0 | |a FREQUENCY ANALYSIS |
| 690 | 1 | 0 | |a HUMAN |
| 690 | 1 | 0 | |a HUMAN EXPERIMENT |
| 690 | 1 | 0 | |a MATHEMATICAL ANALYSIS |
| 690 | 1 | 0 | |a NORMAL HUMAN |
| 690 | 1 | 0 | |a OSCILLATION |
| 690 | 1 | 0 | |a PRIORITY JOURNAL |
| 690 | 1 | 0 | |a QUANTITATIVE ASSAY |
| 690 | 1 | 0 | |a SIGNAL PROCESSING |
| 690 | 1 | 0 | |a TECHNIQUE |
| 690 | 1 | 0 | |a TIME |
| 690 | 1 | 0 | |a VOLUNTEER |
| 690 | 1 | 0 | |a WAVEFORM |
| 690 | 1 | 0 | |a WAVELET |
| 690 | 1 | 0 | |a ADULT |
| 690 | 1 | 0 | |a BIOLOGICAL CLOCKS |
| 690 | 1 | 0 | |a BRAIN |
| 690 | 1 | 0 | |a CORTICAL SYNCHRONIZATION |
| 690 | 1 | 0 | |a ELECTROENCEPHALOGRAPHY |
| 690 | 1 | 0 | |a ENTROPY |
| 690 | 1 | 0 | |a EVOKED POTENTIALS |
| 690 | 1 | 0 | |a HUMANS |
| 690 | 1 | 0 | |a MODELS, NEUROLOGICAL |
| 690 | 1 | 0 | |a SIGNAL PROCESSING, COMPUTER-ASSISTED |
| 690 | 1 | 0 | |a TIME FACTORS |
| 700 | 1 | |a Blanco, S. | |
| 700 | 1 | |a Yordanova, J. | |
| 700 | 1 | |a Kolev, V. | |
| 700 | 1 | |a Figliola, A. | |
| 700 | 1 | |a Schürmann, M. | |
| 700 | 1 | |a Ba ar, E. | |
| 773 | 0 | |d 2001 |g v. 105 |h pp. 65-75 |k n. 1 |p J. Neurosci. Methods |x 01650270 |w (AR-BaUEN)CENRE-5712 |t Journal of Neuroscience Methods | |
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| 856 | 4 | 0 | |u https://doi.org/10.1016/S0165-0270(00)00356-3 |y DOI |
| 856 | 4 | 0 | |u https://hdl.handle.net/20.500.12110/paper_01650270_v105_n1_p65_Rosso |y Handle |
| 856 | 4 | 0 | |u https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_01650270_v105_n1_p65_Rosso |y Registro en la Biblioteca Digital |
| 961 | |a paper_01650270_v105_n1_p65_Rosso |b paper |c PE | ||
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| 999 | |c 63054 | ||