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Ensemble empirical mode decomposition based feature enhancement of cardio signals
Medical Engineering & Physics, 2012This paper presents an application of ensemble empirical mode decomposition method for enhancement of specific biological signal features. The application for two types of cardiological signals is presented in this article. Detection of fiducial points is a routine task for analyzing these signals.
Artūras, Janušauskas +2 more
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2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2009
The purpose of this study is to investigate the potential of the ensemble empirical mode decomposition (EEMD) to extract cardiogenic oscillations from inductive plethysmography signals in order to measure cardiac stroke volume. First, a simple cardio-respiratory model is used to simulate cardiac, respiratory, and cardio-respiratory signals.
Abdulhay, Enas +3 more
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The purpose of this study is to investigate the potential of the ensemble empirical mode decomposition (EEMD) to extract cardiogenic oscillations from inductive plethysmography signals in order to measure cardiac stroke volume. First, a simple cardio-respiratory model is used to simulate cardiac, respiratory, and cardio-respiratory signals.
Abdulhay, Enas +3 more
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Ensemble Empirical Mode Decomposition and adaptive filtering for ECG signal enhancement
2012 IEEE International Symposium on Medical Measurements and Applications Proceedings, 2012The morphologic analysis of electrocardiogram (ECG) signals, which are always contaminated by certain types of noise, is a very important standard for medical diagnosis of heart diseases and other pathological phenomena. In this paper a novel ECG enhancement method based on Ensemble Empirical Mode Decomposition (EEMD) and adaptive filtering is proposed
Xiaochuan He +2 more
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GPU-based Ensemble Empirical Mode Decomposition approach to spectrum discrimination
2012 4th Workshop on Hyperspectral Image and Signal Processing (WHISPERS), 2012Because of the improvement of optical remote sensing instrument, hyperspectral images now collect information of the ground with hundreds of wavelengths. This spectral information can be used to identify different materials, since each material should have its unique absorption spectrum.
Yung-Ling Wang +3 more
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Quantitative diagnosis for bearing faults by improving ensemble empirical mode decomposition
ISA Transactions, 2018In the bearing health assessment issues, using the adaptive nonstationary vibration signal processing methods in the time-frequency domain, lead to improving of early fault detection. On the other hand, the noise and random impulses which contaminates the input data, are a major challenge in extracting fault-related features.
Mohammad Sadegh, Hoseinzadeh +2 more
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A fast entropy assisted complete ensemble empirical mode decomposition algorithm
The 2014 2nd International Conference on Systems and Informatics (ICSAI 2014), 2014Empirical mode decomposition (EMD) is a simple and real-time procedure to adaptively decompose a signal into a set of oscillation scales, but it faces the serious problem of mode mixing. The improved complete ensemble EMD with adaptive noise (Improved CEEMDAN) can successfully eliminate the mode mixing by adding white noise's IMFs and utilizing an ...
Yihai Liu, Xiaomin Zhang, Yang Yu 0040
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Improvement of the Ensemble Empirical Mode Decomposition with Parallel Computing
2019 Ural Symposium on Biomedical Engineering, Radioelectronics and Information Technology (USBEREIT), 2019In this paper we propose a novel approach to increase a processing speed of the Ensemble Empirical Mode Decomposition algorithm and its Complementary version with usage of parallel computations. It is shown that this computational scheme is effective and leads to a significant increase in processing speed up to approximately 4.1 times with 8 parallel ...
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Classification of epileptic EEG data by using ensemble empirical mode decomposition
2018 26th Signal Processing and Communications Applications Conference (SIU), 2018In this study, our aim is to distinguish pre-seizure and seizure data from epileptic EEG signals using Ensemble Empirical Mode Decomposition (EEMD) and various classifiers. For this purpose, epileptic EEG data from 13 epileptic patients have been recorded using surface electrodes at Izmir Kâtip Celebi University School of Medicine, Neurology Department.
Ozlem Karabiber Cura +3 more
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TREND EXTRACTION FOR SEASONAL TIME SERIES USING ENSEMBLE EMPIRICAL MODE DECOMPOSITION
Advances in Adaptive Data Analysis, 2011In this paper, we investigate eligibility of trend extraction through the empirical mode decomposition (EMD) and performance improvement of applying the ensemble EMD (EEMD) instead of the EMD for trend extraction from seasonal time series. The proposed method is an approach that can be applied on any time series with any time scales fluctuations.
Mhamdi, Farouk +2 more
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GPGPU-Aided Ensemble Empirical-Mode Decomposition for EEG Analysis During Anesthesia
IEEE Transactions on Information Technology in Biomedicine, 2010Ensemble empirical-mode decomposition (EEMD) is a novel adaptive time-frequency analysis method, which is particularly suitable for extracting useful information from noisy nonlinear or nonstationary data. Unfortunately, since the EEMD is highly compute-intensive, the method does not apply in real-time applications on top of commercial-off-the-shelf ...
Dan Chen 0001 +4 more
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