Results 141 to 150 of about 44,498 (172)

Multivariate empirical mode decomposition based EMG signal analysis for smart prosthesis

2018 26th Signal Processing and Communications Applications Conference (SIU), 2018
Electromyography (EMG) signals are successfully used for human-robot interaction with biomedical applications. One of the basic components of many modern prosthesis is the myoelectric control system which controls prosthetic movements through EMG signals.
Onay, Fatih, Mert, Ahmet
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Multivariate empirical mode decomposition approach for adaptive denoising of fringe patterns

Optics Letters, 2012
An adaptive approach is presented for noise reduction of optical fringe patterns using multivariate empirical mode decomposition. Adjacent rows and columns of patterns are treated as multichannel signals and are decomposed into multiscale components.
Xiang, Zhou, Tao, Yang, Haihua, Zou, Hong, Zhao   +3 more
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Epileptic Seizure Classification with Multivariate Empirical Mode Decomposition and Hilbert Vibration Decomposition

2019 27th Signal Processing and Communications Applications Conference (SIU), 2019
EEG signals are frequently used to record seizures of epilepsy. However, observation of these seizures is difficult and time-consuming. Fourier-based approaches are not suitable for the nonlinear and nonstationary nature of EEG. For this reason, empirical methods such as multivariate empirical mode decomposition (MEMD) are used in the analysis of ...
Barkin Buyukcakir, Ali Yener Mutlu
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Epileptic seizure detection using Multivariate Empirical Mode Decomposition and Support Vector Machines

2020 IEEE International Conference on Design & Test of Integrated Micro & Nano-Systems (DTS), 2020
Automatic detection of epileptic seizures is a very crucial step for diagnosing patients with drug-resistant epilepsies. If visual analysis of long-term electroencephalographic signals is the most reliable technique, automatic seizures detection can help the physicians in comparing seizures and extracting common patterns.
Mahjoub, C., Chaibi, S., Krikid, F., Nica, A., Bouquin Jeannes, R.L., Kachouri, A.   +5 more
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FPGA-Based Design for Online Computation of Multivariate Empirical Mode Decomposition

IEEE Transactions on Circuits and Systems I: Regular Papers, 2020
Multivariate or multichannel data have become ubiquitous in many modern scientific and engineering applications, e.g., biomedical engineering, owing to recent advances in sensor and computing technology. Processing these data sets is challenging owing to their large size and multidimensional nature, thus requiring specialized algorithms and efficient ...
Sikender Gul, Muhammad Faisal Siddiqui, Naveed ur Rehman   +2 more
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Fetal heart rate extraction from abdominal electrocardiograms through multivariate empirical mode decomposition

International Journal of Cardiovascular Research, 2016
Assessment of fetal heart rate (FHR) and fetal heart rate variability (fHRV) reveals important information about fetal well-being, specifically in high risk pregnancies. Abdominal electrocardiogram (abdECG) recording is a non-invasive method to capture fetal electrocardiograms.
Praveen Gupta, K.K. Sharma, S.D. Joshi
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EEG epileptic seizures separation with multivariate empirical mode decomposition for diagnostic purposes

2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2013
We present a successful application of a soft computing approach based on the multivariate empirical mode decomposition (MEMD) method to EEG epileptic seizures separation. The results of the automatic multivatiate intrinsic mode functions (IMF) clustering allowed us to separate the seizure related spikes and sharp waves.
Tomasz M, Rutkowski, Zbigniew R, Struzik, Danilo P, Mandic   +2 more
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Application of multivariate empirical mode decomposition for seizure detection in EEG signals

2010 Annual International Conference of the IEEE Engineering in Medicine and Biology, 2010
We present a method for the analysis of electroencephalogram (EEG) signals which has the potential to distinguish between ictal and seizure-free intracranial EEG recordings. This is achieved by analyzing common frequency components in multichannel EEG recordings, using the multivariate empirical mode decomposition (MEMD) algorithm.
Naveed, Ur Rehman, Yili, Xia, Danilo P, Mandic   +2 more
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Forecasting using multivariate empirical mode decomposition — Applied to iceberg drift forecast

2017 IEEE Conference on Control Technology and Applications (CCTA), 2017
The prediction of the movement of a floating object in the ocean, such as an iceberg, is a challenging problem. Large uncertainties in the driving forces and possibly in the geometry of the object itself prevent accurate forecasts. However, if observations of the past trajectory of the object are available the forecast can be improved considerably ...
Leif Erik Andersson, Muhammad Faisal Aftab, Francesco Scibilia, Lars Imsland   +3 more
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Lung-heart sound separation using noise assisted multivariate empirical mode decomposition

2013 International Symposium on Intelligent Signal Processing and Communication Systems, 2013
Separating lung sound (LS) from breath sound (BS) recording has been of interest to doctors and researchers in the last two decades. Many algorithms have been developed to solve this question, one of them is based on the empirical mode decomposition (EMD).
ChingShun Lin, Wisena A. Tanumihardja, HongHui Shih   +2 more
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