Data driven filtering of bowel sounds using multivariate empirical mode decomposition [PDF]
BioMedical Engineering OnLine, 2019 Background The analysis of abdominal sounds can help to diagnose gastro-intestinal diseases. Sounds originating from the stomach and the intestine, the so-called bowel sounds, occur in various forms. They are described as loose successions or clusters of
Konstanze Kölle +4 more
doaj +6 more sources
Multi-Scale Pixel-Based Image Fusion Using Multivariate Empirical Mode Decomposition [PDF]
Sensors, 2015 A novel scheme to perform the fusion of multiple images using the multivariate empirical mode decomposition (MEMD) algorithm is proposed. Standard multi-scale fusion techniques make a priori assumptions regarding input data, whereas standard univariate ...
Naveed ur Rehman +5 more
doaj +6 more sources
Multivariate Empirical Mode Decomposition for Quantifying Multivariate Phase Synchronization [PDF]
EURASIP Journal on Advances in Signal Processing, 2011 Quantifying the phase synchrony between signals is important in many different applications, including the study of the chaotic oscillators in physics and the modeling of the joint dynamics between channels of brain activity recorded by electroencephalogram (EEG).
Ali Yener Mutlu, Selin Aviyente
doaj +2 more sources
Low-Density EEG for Neural Activity Reconstruction Using Multivariate Empirical Mode Decomposition [PDF]
Frontiers in Neuroscience, 2020 Several approaches can be used to estimate neural activity. The main differences between them concern the a priori information used and its sensitivity to high noise levels.
Andres Soler +5 more
doaj +2 more sources
Fast Multivariate Empirical Mode Decomposition
IEEE Access, 2018 The multivariate empirical mode decomposition (MEMD) has been pioneered recently for adaptively processing of multichannel data. Despite its high efficiency on time-frequency analysis of nonlinear and nonstationary signals, high computational load and ...
Xun Lang +6 more
doaj +2 more sources
Noise-assisted multivariate empirical mode decomposition for multichannel EMG signals. [PDF]
Biomed Eng Online, 2017 Ensemble Empirical Mode Decomposition (EEMD) has been popularised for single-channel Electromyography (EMG) signal processing as it can effectively extract the temporal information of the EMG time series. However, few papers examine the temporal and spatial characteristics across multiple muscle groups in relation to multichannel EMG signals.The ...
Zhang Y +7 more
europepmc +4 more sources
Matlab Open Source Code: Noise-Assisted Multivariate Empirical Mode Decomposition Based Causal Decomposition for Causality Inference of Bivariate Time Series [PDF]
Frontiers in Neuroinformatics, 2022 Causality inference has arrested much attention in academic studies. Currently, multiple methods such as Granger causality, Convergent Cross Mapping (CCM), and Noise-assisted Multivariate Empirical Mode Decomposition (NA-MEMD) are introduced to solve the
Yi Zhang +11 more
doaj +2 more sources
Early Fault Detection Method for Rotating Machinery Based on Harmonic-Assisted Multivariate Empirical Mode Decomposition and Transfer Entropy [PDF]
Entropy, 2018 It is a difficult task to analyze the coupling characteristics of rotating machinery fault signals under the influence of complex and nonlinear interference signals.
Zhe Wu +4 more
doaj +2 more sources
Bidimensional Multivariate Empirical Mode Decomposition With Applications in Multi-Scale Image Fusion [PDF]
IEEE Access, 2019 Empirical mode decomposition (EMD) is a fully data-driven technique designed for multi-scale decomposition of signals into their natural scale components, called intrinsic mode functions (IMFs).
Yili Xia +3 more
doaj +2 more sources
Multi-Fault Diagnosis of Rolling Bearings via Adaptive Projection Intrinsically Transformed Multivariate Empirical Mode Decomposition and High Order Singular Value Decomposition [PDF]
Sensors, 2018 Rolling bearings are important components in rotary machinery systems. In the field of multi-fault diagnosis of rolling bearings, the vibration signal collected from single channels tends to miss some fault characteristic information.
Rui Yuan, Yong Lv, Gangbing Song
doaj +2 more sources