ArticleScientific reports2020
New insights and best practices for the successful use of Empirical Mode Decomposition, Iterative Filtering and derived algorithms.
Article in Scientific reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed.
- Oracle Upper Bounds on Clean-EEG Recoverability from Single-Channel Decompositions Under EOG/EMG Contamination.Sensors (Basel, Switzerland) · 2026Article
- Applications of Fast Iterative Filtering in NMR Spectroscopy: Baseline Correction.Magnetic resonance in chemistry : MRC · 2025Article
- Ocean's largest chlorophyll-rich tongue is extending westward (2002-2022).Nature communications · 2025Article
- Tropical cyclone activity over western North Pacific favors Arctic sea ice increase.Nature communications · 2024Article
- Estimation of Tissue Permeability in MRI Images by Empirical Mode Decomposition Method.Journal of biomedical physics & engineering · 2024Article
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- High resolution seafloor thermometry for internal wave and upwelling monitoring using Distributed Acoustic Sensing.Scientific reports · 2023Article
- An Evaluation of Non-Contact Photoplethysmography-Based Methods for Remote Respiratory Rate Estimation.Sensors (Basel, Switzerland) · 2023Article
- PN Codes Estimation of Binary Phase Shift Keying Signal Based on Sparse Recovery for Radar Jammer.Sensors (Basel, Switzerland) · 2023Article
- Calculation of an Improved Stiffness Index Using Decomposed Radial Pulse and Digital Volume Pulse Signals.Journal of personalized medicine · 2022Article
- Tool Condition Monitoring for High-Performance Machining Systems-A Review.Sensors (Basel, Switzerland) · 2022Review
- Assessment of time irreversibility in a time series using visibility graphs.Frontiers in network physiology · 2022Article
- Enhanced Partial Discharge Signal Denoising Using Dispersion Entropy Optimized Variational Mode Decomposition.Entropy (Basel, Switzerland) · 2021Article
- Application of Empirical Mode Decomposition for Decoding Perception of Faces Using Magnetoencephalography.Sensors (Basel, Switzerland) · 2021Article
- Comparison of Empirical Mode Decomposition, Wavelets, and Different Machine Learning Approaches for Patient-Specific Seizure Detection Using Signal-Derived Empirical Dictionary Approach.Frontiers in digital health · 2021Article
- New insights and best practices for the successful use of Empirical Mode Decomposition, Iterative Filtering and derived algorithms.Scientific reports · 2020Article
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Algorithms based on Empirical Mode Decomposition (EMD) and Iterative Filtering (IF) are largely implemented for representing a signal as superposition of simpler well-behaved components called Intrinsic Mode Functions (IMFs). Although they are more suitable than traditional methods for the analysis of nonlinear and nonstationary signals, they could be easily misused if their known limitations, together with the assumptions they rely on, are not carefully considered. In this work, we examine the main pitfalls and provide caveats for the proper use of the EMD- and IF-based algorithms. Specifically, we address the problems related to boundary errors, to the presence of spikes or jumps in the signal and to the decomposition of highly-stochastic signals. The consequences of an improper usage of these techniques are discussed and clarified also by analysing real data and performing numerical simulations. Finally, we provide the reader with the best practices to maximize the quality and meaningfulness of the decomposition produced by these techniques. In particular, a technique for the extension of signal to reduce the boundary effects is proposed; a careful handling of spikes and jumps in the signal is suggested; the concept of multi-scale statistical analysis is presented to treat highly stochastic signals.
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Registered trials
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