Results 31 to 40 of about 44,498 (172)
Performance evaluation of the Hilbert–Huang transform for respiratory sound analysis and its application to continuous adventitious sound characterization [PDF]
, 2016 © 2016. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/The use of the Hilbert–Huang transform in the analysis of biomedical signals has increased during the past few years, but
Fiz Fernández, José Antonio +2 more
core +2 more sources
Myoelectric Pattern Identification of Stroke Survivors Using Multivariate Empirical Mode Decomposition [PDF]
Journal of Healthcare Engineering, 2014 This study presents a novel feature extraction method for myoelectric pattern recognition using a multivariate extension of empirical mode decomposition (EMD), namely multivariate EMD (MEMD). The method processes multiple surface electromyogram (EMG) channels simultaneously rather than in a channel‐by‐channel manner.
Xu, Zhang, Ping, Zhou
openaire +2 more sources
GPU-Accelerated Multivariate Empirical Mode Decomposition for Massive Neural Data Processing
IEEE Access, 2017 This paper presents an efficient implementation of multivariate empirical mode decomposition (MEMD) algorithm, a multivariate extension of EMD algorithm.
Taha Mujahid +2 more
doaj +1 more source
IEEE Access, 2020 This paper describes a novel multichannel signal denoising approach based on multivariate variational mode decomposition (MVMD). MVMD is the extended version of the variational mode decomposition (VMD) algorithm for multichannel data sets.
Peipei Cao, Huali Wang, Kaijie Zhou
doaj +1 more source
Elastic Net Regression and Empirical Mode Decomposition for Enhancing the Accuracy of the Model Selection [PDF]
International Journal of Mathematical, Engineering and Management Sciences, 2021 Elastic net (ELNET) regression is a hybrid statistical technique used for regularizing and selecting necessary predictor variables that have a strong effect on the response variable and deal with multicollinearity problem when it exists between the ...
Abdullah S. Al-Jawarneh +2 more
doaj +1 more source
Sample entropy analysis of EEG signals via artificial neural networks to model patients' consciousness level based on anesthesiologists experience. [PDF]
, 2015 Electroencephalogram (EEG) signals, as it can express the human brain's activities and reflect awareness, have been widely used in many research and medical equipment to build a noninvasive monitoring index to the depth of anesthesia (DOA).
Abbod, MF +12 more
core +4 more sources
On the behavior of EMD and MEMD in presence of symmetric alpha-stable noise [PDF]
, 2014 EmpiricalMode Decomposition (EMD) and its extended versions such as Multivariate EMD (MEMD) are data-driven techniques that represent nonlinear and non-stationary data as a sum of a finite zero-mean AM-FM components referred to as Intrinsic Mode ...
BOUDRAA, Abdel-Ouahab +3 more
core +7 more sources
A novel method to identify the flow pattern of oil–water two-phase flow
Journal of Petroleum Exploration and Production Technology, 2020 This paper presents a novel method combining extreme learning machine (ELM) and multiple empirical mode decomposition (MEMD) to identify flow patterns of oil–water two-phase flow. The proposed method can recognize accurately five typical flow patterns of
Zhong-Cheng Li, Chun-Ling Fan
doaj +1 more source
Frontiers in Physics, 2023 Microseismic monitoring data may be seriously contaminated by complex and nonstationary interference noises produced by mechanical vibration, which significantly impact the data quality and subsequent data-processing procedure.
Zhichao Yu +5 more
doaj +1 more source
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 +1 more source