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Applications of EEG Neuroimaging Data: Event-related Potentials, Spectral Power, and Multiscale Entropy
Published on: June 27, 2013
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Bi-dimensional multiscale entropy: Relation with discrete Fourier transform and biomedical application.
Anne Humeau-Heurtier1, Ana Carolina Mieko Omoto2, Luiz E V Silva3
1Univ Angers, LARIS - Laboratoire Angevin de Recherche en Ingénierie des Systèmes, 62 avenue Notre-Dame du Lac, 49000, Angers, France.
Computers in Biology and Medicine
|July 6, 2018
Summary
The bi-dimensional multiscale entropy (MSE2D) measure analyzes image complexity. This study reveals MSE2D is sensitive to image frequency content, suggesting new biomedical applications.
Area of Science:
- Image analysis
- Complexity science
- Biomedical imaging
Background:
- Multiscale entropy (MSE1D) is established for time series complexity.
- Developing complexity measures for images is an ongoing research area.
- Bi-dimensional multiscale entropy (MSE2D) has been proposed for image analysis.
Purpose of the Study:
- To investigate the relationship between MSE2D and the discrete Fourier transform (DFT).
- To explore the interpretability and potential applications of MSE2D in image analysis.
Main Methods:
- Analysis of synthetic and biomedical images using MSE2D.
- Evaluation of MSE2D's sensitivity to amplitude and phase changes in the DFT of images.
Main Results:
- The MSE2D profile demonstrates sensitivity to both amplitude and phase components of the DFT.
- Findings suggest that MSE2D can effectively characterize image complexity based on frequency content.
Conclusions:
- This research provides crucial insights into the interpretation of MSE2D.
- MSE2D shows promise for biomedical image analysis and opens new avenues for entropy-based image studies across spatial scales.
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