Journal article
Adaptive Multiscale Entropy Analysis of Multivariate Neural Data
IEEE transactions on biomedical engineering, v 59(1), pp 12-15
Jan 2012
PMID: 21788182
Abstract
Multiscale entropy (MSE) has been widely used to quantify a system's complexity by taking into account the multiple time scales inherent in physiologic time series. The method, however, is biased toward the coarse scale, i.e., low-frequency components due to the progressive smoothing operations. In addition, the algorithm for extracting the different scales is not well adapted to nonlinear/nonstationary signals. In this letter, we introduce adaptive multiscale entropy (AME) measures in which the scales are adaptively derived directly from the data by virtue of recently developed multivariate empirical mode decomposition. Depending on the consecutive removal of low-frequency or high-frequency components, our AME can be estimated at either coarse-to-fine or fine-to-coarse scales over which the sample entropy is performed. Computer simulations are performed to verify the effectiveness of AME for analysis of the highly nonstationary data. Local field potentials collected from the visual cortex of macaque monkey while performing a generalized flash suppression task are used as an example to demonstrate the usefulness of our AME approach to reveal the underlying dynamics in complex neural data.
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Details
- Title
- Adaptive Multiscale Entropy Analysis of Multivariate Neural Data
- Creators
- Meng Hu - School of Biomedical Engineering, Science and Health Systems, Drexel University, Philadelphia, USAHualou Liang - School of Biomedical Engineering, Science and Health Systems, Drexel University, Philadelphia, USA
- Publication Details
- IEEE transactions on biomedical engineering, v 59(1), pp 12-15
- Publisher
- IEEE
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- School of Biomedical Engineering, Science, and Health Systems
- Web of Science ID
- WOS:000298327100004
- Scopus ID
- 2-s2.0-84555197047
- Other Identifier
- 991014878273404721
InCites Highlights
Data related to this publication, from InCites Benchmarking & Analytics tool:
- Web of Science research areas
- Engineering, Biomedical