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Detección y Análisis de frecuencias instántaneas en sistemas dinámicos

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Detección y Análisis de frecuencias instántaneas en sistemas dinámicos

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In communication links between satellites and ground stations, the transmitted signal is exposed to disturbances such as noise of different types that hinder reception or retrieval of information. For that reason, different methods are used to process the received signal and to remove these perturbations, and thus recover the information. Different techniques of signal analysis have been used to achieve the above described but the results are not completely satisfactory. The Hilbert Huang Transform has been applied increasingly in different areas and satellite signals are not the exception. In this work, a study where the Hilbert-Huang transform is used to analyze the received signal and to remove these perturbations, and thus recover the transmitted signal is presented
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In this work, a novel identification method of relevant Intrinsic Mode Functions, obtained from Electroencephalographic signals, by using an entropy criteria is proposed. The idea is to reduce the number of Intrinsic Mode Functions that are necessary for the electroencephalographic source reconstruction. An entropy cost function is applied on the Intrinsic Mode Functions generated by the Empirical Mode Decomposition for automatic IMF selection. The resulting Enhanced Empirical Mode Decomposition is evaluated in simulated and real data bases containing normal and epileptic activity by means of a relative error measure. The proposed approach shows to improve the electroencephalographic source reconstruction specifically for epileptic seizure detection.
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This paper shows a method to locate actives sources from pre-processed electroencephalographic signals. These signals are processed using multivariate empirical mode decomposition (MEMD). The intrinsic mode functions are analyzed through the Hilbert-Huang spectral entropy. A cost function is proposed to automatically select the intrinsic mode functions associated with the lowest spectral entropy values and they are used to reconstruct the neural activity generated by the active sources. Multiple sparse priors are used to locate the active sources with and without multivariate empirical mode decomposition and the performance is estimated using the Wasserstein metric. The results were obtained for conditions with high noise (Signal-to-Noise-Ratio of -5dB), where the estimated location, for five sources, was better for multiple sparse prior with Multivariate Empirical Mode Decomposition, and with low noise (Signal-to-Noise-Ratio of 20dB), where the estimated location, for three sources, was better for multiple sparse prior without MEMD.
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The applications of Empirical Mode Decomposition (EMD) in Biomedical Signal analysis have increased and is common now to find publications that use EMD to identify behaviors in the brain or heart. EMD has shown excellent results in the identification of behaviours from the use of electroencephalogram (EEG) signals. In addition, some advances in the computer area have made it possible to improve their performance. In this paper, we presented a method that, using an entropy analysis, can automatically choose the relevant Intrinsic Mode Functions (IMFs) from EEG signals. The idea is to choose the minimum number of IMFs to reconstruct the brain activity. The EEG signals were processed by EMD and the IMFs were ordered according to the entropy cost function. The IMFs with more relevant information are selected for the brain mapping. To validate the results, a relative error measure was used.
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The localization of active brain sources from Electroencephalogram (EEG) is a useful method in clinical applications, such as the study of localized epilepsy, evoked-related-potentials, and attention deficit/hyperactivity disorder. The distributed-source model is a common method to estimate neural activity in the brain. The location and amplitude of each active source are estimated by solving the inverse problem by regularization or using Bayesian methods with spatio-temporal constraints. Frequency and spatio-temporal constraints improve the quality of the reconstructed neural activity. However, separation into frequency bands is beneficial when the relevant information is in specific sub-bands. We improved frequency-band identification and preserved good temporal resolution using EEG pre-processing techniques with good frequency band separation and temporal resolution properties. The identified frequency bands were included as constraints in the solution of the inverse problem by decomposing the EEG signals into frequency bands through various methods that offer good frequency and temporal resolution, such as empirical mode decomposition (EMD) and wavelet transform (WT). We present comparative analysis of the accuracy of brain-source reconstruction using these techniques. The accuracy of the spatial reconstruction was assessed using the Wasserstein metric for real and simulated signals. We approached the mode-mixing problem, inherent to EMD, by exploring three variants of EMD: masking EMD, Ensemble-EMD (EEMD), and multivariate EMD (MEMD). The results of the spatio-temporal brain source reconstruction using these techniques show that masking EMD and MEMD can largely mitigate the mode-mixing problem and achieve a good spatio-temporal reconstruction of the active sources. Masking EMD and EEMD achieved better reconstruction than standard EMD, Multiple Sparse Priors, or wavelet packet decomposition when EMD was used as a pre-processing tool for the spatial reconstruction (averaged over time) of the brain sources. The spatial resolution obtained using all three EMD variants was substantially better than the use of EMD alone, as the mode-mixing problem was mitigated, particularly with masking EMD and EEMD. These findings encourage further exploration into the use of EMD-based pre-processing, the mode-mixing problem, and its impact on the accuracy of brain source activity reconstruction.
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Empirical Mode Decomposition (EMD) is an adaptive time-frequency analysis method, which is very useful for extracting information from noisy nonlinear or nonstationary data. The applications of this technique in Biomedical Signal analysis has increased and is now common to find publications that use EMD to identify behaviors in the brain or heart. In this paper, a novel identification method of relevant Intrinsic Mode Functions (IMFs), obtained from EEG signals, using an entropy analysis is proposed. The idea is to reduce the number of IMFs that are necessary for the reconstruction of neural activity. The entropy cost function is applied on the IMFs generated by the EMD in order to automatically select the IMFs with relevant information. A relative error measure has been used to validate our proposal.