ReviewProceedings. Mathematical, physical, and engineering sciences2015
Intrinsic multi-scale analysis: a multi-variate empirical mode decomposition framework.
Review in Proceedings. Mathematical, physical, and engineering sciences, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Empirical mode decomposition of local field potential data from optogenetic experiments.Frontiers in computational neuroscience · 2023Article
- A Pilot Study of Heart Rate Variability Synchrony as a Marker of Intraoperative Surgical Teamwork and Its Correlation to the Length of Procedure.Sensors (Basel, Switzerland) · 2022Article
- Loosening Identification of Multi-Bolt Connections Based on Wavelet Transform and ResNet-50 Convolutional Neural Network.Sensors (Basel, Switzerland) · 2022Article
- Structural Health Monitoring in Composite Structures: A Comprehensive Review.Sensors (Basel, Switzerland) · 2021Review
- Cognitive Outcome Prediction in Infants With Neonatal Hypoxic-Ischemic Encephalopathy Based on Functional Connectivity and Complexity of the Electroencephalography Signal.Frontiers in human neuroscience · 2021Article
- Health Degradation Monitoring and Early Fault Diagnosis of a Rolling Bearing Based on CEEMDAN and Improved MMSE.Materials (Basel, Switzerland) · 2018Article
- Resolving Ambiguities in the LF/HF Ratio: LF-HF Scatter Plots for the Categorization of Mental and Physical Stress from HRV.Frontiers in physiology · 2017Article
- Adaptive-projection intrinsically transformed multivariate empirical mode decomposition in cooperative brain-computer interface applications.Philosophical transactions. Series A, Mathematical, physical, and engineering sciences · 2016Article
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3 authors.
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Abstract
A novel multi-scale approach for quantifying both inter- and intra-component dependence of a complex system is introduced. This is achieved using empirical mode decomposition (EMD), which, unlike conventional scale-estimation methods, obtains a set of scales reflecting the underlying oscillations at the intrinsic scale level. This enables the data-driven operation of several standard data-association measures (intrinsic correlation, intrinsic sample entropy (SE), intrinsic phase synchrony) and, at the same time, preserves the physical meaning of the analysis. The utility of multi-variate extensions of EMD is highlighted, both in terms of robust scale alignment between system components, a pre-requisite for inter-component measures, and in the estimation of feature relevance. We also illuminate that the properties of EMD scales can be used to decouple amplitude and phase information, a necessary step in order to accurately quantify signal dynamics through correlation and SE analysis which are otherwise not possible. Finally, the proposed multi-scale framework is applied to detect directionality, and higher order features such as coupling and regularity, in both synthetic and biological systems.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.