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Empirical mode decomposition (EMD) allows decomposing an observed multicomponent signal into a set of monocomponent signals called Intrinsic Mode Functions (IMFs). EMD provides a large number of IMFs and it is important to select the fundamental IMFs and eliminate the redundant ones. This paper proposes a new criterion, based simultaneously on the Minkowski distance and the Jensen Rényi divergence...
Biomedical signals are in general non-linear and non-stationary which renders them difficult to analyze with classical time series analysis techniques. Empirical Mode Decomposition (EMD) in conjunction with a Hilbert spectral transform, together called Hilbert-Huang Transform, is ideally suited to extract informative components which are characteristic of underlying biological or physiological processes...
According to the non-stationary feature of ECG signal, a new classification method of arrhythmia is introduced. This method combines empirical mode decomposition (EMD) with singular value decomposition (SVD), using support vector machines (SVM) for classifying. First, ECG signal is decomposed into a set of intrinsic mode function (IMF) using empirical mode decomposition method. The initial feature...
Epilepsy is a neurological disorder that affects around 50 million people worldwide. The seizure detection is an important component in the diagnosis of epilepsy. In this study, the Empirical Mode Decomposition (EMD) method was proposed on the development of an automatic epileptic seizure detection algorithm. The algorithm first computes the Intrinsic Mode Functions (IMFs) of EEG records, then calculates...
Spectral analysis of heart rate variability (HRV) is used for the assessment of cardiovascular autonomic control. In this study data driven adaptive technique empirical mode decomposition (EMD) and the associated Hilbert spectrum has been used to evaluate the effect of local anesthesia on HRV parameters in a group of fourteen patients undergoing brachial plexus block (local anesthesia) using transarterial...
An improved Hilbert-Huang transform(HHT) combined with wavelet packet transform(WPT) is proposed for recognizing continuous electroencephalogram (EEG) in brain computer interfaces (BCIs). The HHT consists of empirical mode decomposition(EMD) and Hilbert-Huang spectrum (HHS). Firstly, the WPT decomposes the signal into a set of narrow band signals, then a series of intrinsic mode functions (IMFs) can...
In this paper, we attempt to analyze the effectiveness of the Empirical Mode Decomposition (EMD) for discriminating epilepticl periods from the interictal periods. The Empirical Mode Decomposition (EMD) is a general signal processing method for analyzing nonlinear and nonstationary time series. The main idea of EMD is to decompose a time series into a finite and often small number of intrinsic mode...
This paper introduces an approach to obtain the feature vectors of surface electromyography (sEMG) signal based on Hilbert Huang transform (HHT). An adaptive segmentation method that could effectively select appropriate intrinsic mode function (IMF) is proposed. With the features gathered by using the energy of one channel signal, we also provide an optimized strategy based on experiments and experiences...
This study used empirical mode decomposition (EMD) for filtering power line noise in electrocardiogram signals. When the signal-to-noise (SNR) is low, the power line noise is separated out as the first intrinsic mode function (IMF), but when the SNR is high, a part of the signal along with the noise is decomposed as the first IMF. To overcome this problem, we add a pseudo noise at a frequency higher...
The mechanisms involved in the generation of heart sounds have always been of interest, mainly for diagnosis purposes. As a result, mathematical models have been proposed for first (S1) and second (S2) heart sounds and different efforts have been made to select the best signal processing tool to analyze them. Different frequency analysis techniques have been used to relate cardiac structure to the...
The mechanisms involved in the generation of heart sounds have always been of interest, mainly for diagnosis purposes. As a result, mathematical models have been proposed for first (S1) and second (S2) heart sounds and different efforts have been made to select the best signal processing tool to analyze them. Different frequency analysis techniques have been used to relate cardiac structure to the...
Removal of baseline wander and power line interference in ECG signal is a classical problem. A new method is proposed to remove baseline wander and power line interference in ECG signal based on empirical mode decomposition and notch filter. Principles and characteristics of empirical mode decomposition are presented; ECG signal is decomposed into a series of intrinsic mode functions (IMFs). Then...
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