Glossary entry (derived from question below)
English term or phrase:
wavelet-chaos-neural network
Polish translation:
metodologia analizy falkowej i ilościowego oznaczania chaotycznej dynamiki sieci neuronowych
Added to glossary by
Frank Szmulowicz, Ph. D.
Aug 29, 2020 04:49
3 yrs ago
4 viewers *
English term
wavelet-chaos-neural network
English to Polish
Science
Biology (-tech,-chem,micro-)
Some electrophysiological metrics are emerging as potential discriminators between brain signal from individuals with ASCs and those who are neurotypical, such as a wavelet-chaos-neural network methodology applied to EEG signal and reduced cross-frequency coupling.
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Proposed translations
(Polish)
| 3 | metodologia analizy falkowej i ilościowego oznaczania chaotycznej dynamiki sieci neuronowych |
Frank Szmulowicz, Ph. D.
|
Change log
Aug 29, 2020 04:49: changed "Kudoz queue" from "In queue" to "Public"
Oct 17, 2020 12:02: Frank Szmulowicz, Ph. D. Created KOG entry
Proposed translations
7 hrs
Frank Szmulowicz, Ph. D.
United States
United States
Native in English
&
Polish
Works in field
United States
On time, on target, on budget.
Native in: English 
, Polish
Works in: Polish to English, English to Polish, Russian to Polish, and 1 more.
Related KudoZ points
:
578
Selected
metodologia analizy falkowej i ilościowego oznaczania chaotycznej dynamiki sieci neuronowych
metodologia analizy falkowej i ilościowego oznaczania chaotycznej dynamiki sieci neuronowych (ang. wavelet-chaos-neural network)
cccccc
A novel wavelet-chaos-neural network methodology is presented for classification of electroencephalograms (EEGs) into healthy, ictal, and interictal EEGs. Wavelet analysis is used to decompose the EEG into delta, theta, alpha, beta, and gamma sub-bands.
Three parameters are employed for EEG representation: standard deviation (quantifying the signal variance), correlation dimension, and largest Lyapunov exponent (quantifying the non-linear chaotic dynamics of the signal). The classification accuracies of the following techniques are compared: 1) unsupervised-means clustering; 2) linear and quadratic discriminant analysis; 3) radial basis function neural network; 4) Levenberg-Marquardt backpropagation neural network (LMBPNN). To reduce the computing time and output analysis, the research was performed in two phases: band-specific analysis and mixed-band analysis. In phase two, over 500 different
https://scholar.google.com/citations?user=TQMpoh8AAAAJ&hl=en...
cccccc
A novel wavelet-chaos-neural network methodology is presented for classification of electroencephalograms (EEGs) into healthy, ictal, and interictal EEGs. Wavelet analysis is used to decompose the EEG into delta, theta, alpha, beta, and gamma sub-bands.
Three parameters are employed for EEG representation: standard deviation (quantifying the signal variance), correlation dimension, and largest Lyapunov exponent (quantifying the non-linear chaotic dynamics of the signal). The classification accuracies of the following techniques are compared: 1) unsupervised-means clustering; 2) linear and quadratic discriminant analysis; 3) radial basis function neural network; 4) Levenberg-Marquardt backpropagation neural network (LMBPNN). To reduce the computing time and output analysis, the research was performed in two phases: band-specific analysis and mixed-band analysis. In phase two, over 500 different
https://scholar.google.com/citations?user=TQMpoh8AAAAJ&hl=en...
4 KudoZ points awarded for this answer.
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