Empirical Mode Decomposition Approach Research Articles

Drivers of Stock Prices in Ghana: An Empirical Mode Decomposition Approach

This study utilized the empirical mode decomposition (EMD) technique and examined which group of investors based on their trading frequencies influence stock prices in Ghana. We applied this technique to a dataset of daily closing prices of GSE Financial Stock Index for the period 04/01/2011 to 28/08/2015. The daily closing prices were decomposed into six intrinsic mode functions (IMFs) and a residue. We used the hierarchical clustering method to reconstruct the IMFs into high frequency, low frequency, and trend components. Using statistical measures such as Pearson product moment correlation coefficient and the Kendall rank correlation, we found that the low frequency and trend components of stock prices are the main drivers of the GSE stock index. These low-frequency traders are the institutional investors. Therefore, stock prices on the GSE are affected by real economic growth but not short-lived market fluctuations.

A two-stage variational jump point detection algorithm for real estate analysis

Real estate is one of the most important forms of portfolio, which plays an essential role in the economy of Hong Kong. Extensive investigations indicate that jump points in the private domestic price indices are subject to various factors such as international events, regional incidents, and government policies. Detecting jump points and identifying their relationships with these factors are indispensable since such findings can facilitate policymakers and investors to look forward. In this paper, we propose a novel two-stage variational methodology using the total variation minimization algorithm and derivative-based detector with the thresholding function for detecting jump points in the Hong Kong private domestic price indices. Our algorithm can weaken statistical fluctuation of the price indices via the total variation formulation and accurately detect jump points based on its rate of change. Comparative experimental results based on the monthly private domestic price indices from January 2010 to July 2020 reveal that the proposed method can detect 40 jump points corresponding to the aforementioned factors whereas the current state-of-the-art empirical mode decomposition approach can only detect 29 jump points, which indicates the superior performance of the proposed method.

Efficient oscillation detection for verification of mechatronic closed-loop systems using search-based testing

In mechatronic system design, the systematic early-stage verification in simulation environments is a crucial aspect. In particular, the development of highly sophisticated and well performing controllers requires complementary testing by extensive simulations. The assessment and evaluation of the control strategy and its tuning is ideally carried out by specialists and is usually very time consuming. Due to time constraints, often only the nominal configuration and a limited number of other configurations are considered in the simulation-based analysis of the closed-loop system. To cover a more comprehensive set of test cases and speed up the analysis, assistive and automated tools are essential. This paper proposes a method to systematically search for oscillations in closed-loop mechatronic systems, which, in most cases, indicate a poor control performance or even an unstable system behavior. Although oscillation detection methods are very well established in the process industry, these methods do not directly apply to mechatronic systems where frequent changes of the operating points and tracking of fast trajectories have to be taken into account. A recently proposed method which can cope with these challenges uses the Empirical Mode Decomposition (EMD) combined with an assessment of the decomposed modes. This is the basis for the construction of a normalized and continuous oscillation index, which provides a suitable measure for the oscillation strength. The performance of the selected EMD approach compared to other state-of-the-art concepts is illustrated by measurement data of a hydraulic valve. The introduced oscillation index serves as an objective function for a Search-based Testing (SbT) approach to systematically select those parameter combinations which yield a worst-case scenario for the closed-loop system. The high potential of the combination of SbT with an oscillation-based objective function for the automated verification of closed-loop mechatronic systems is demonstrated for the example of a goods lift.

EEG Artifact Removal System for Depression Using a Hybrid Denoising Approach

Introduction: A number of computer- aided diagnosis systems for depression are being offered to be used by the clinicians as a method to authorize the diagnosis. EEG may be used as an objective analysis tool for identification of depression in the initial stage so as to avoid it from reaching a severe and permanent state. However, artifact contamination reduces the accuracy in EEG signal processing systems. Methods: This work proposes a novel denoising method based on EMD (Empirical Mode Decomposition) with detrended fluctuation analysis (DFA) and wavelet packet transform. As the first stage, real EEG recordings corresponding to depression patients are decomposed into various mode functions by applying EMD. Then, DFA is used as the mode selection criteria. Further wavelet packets decomposition (WPD) based evaluation is used to extract the cleaner signal. Results: Simulations have been carried out on real EEG databases for depression to demonstrate the effectiveness of the proposed techniques. To conclude the efficacy of the proposed technique, SNR and MAE have been identified. The results show improved signal to noise ratio and lower values of MAE for the combined EMD-DFA-WPD technique. Also, Random Forest and SVM (Support Vector Machine) based classification shows improved accuracy of 98.51% and 98.10% for the proposed denoising technique. Whereas the accuracy of the EMD- DFA is 98.01% and 95.81% and EMD combined with DWT technique is 98.0% and 97.21% for the EMD- DFA technique for RF and SVM respectively as compared to the proposed method. Also, the classification performance for both the classifiers has been compared with and without denoising to highlight the effectiveness of the proposed technique. Conclusion: Proposed denoising system results in better classification of depressed and healthy individuals resulting in better diagnosing system. These results can be further analyzed using other approaches as a solution to the mode mixing problem of EMD approach.

Fault isolation of analog circuit using an optimized ensemble empirical mode decomposition approach based on multi-objective optimization

This article proposed a practical approach to isolating faults in analog circuits. The contribution of this article is twofold. First, the optimized empirical mode decomposition approach is presented based on the Hellinger distance such that there is a minimum dependency between intrinsic mode functions. Features with high distinction could be extracted by employing intrinsic mode functions in fault detection problem of analog benchmark circuits. Second, the non-dominated sorting genetic algorithm is employed to retain excellent features and speed up the execution, resulting in the high accuracy of fault detection and isolation. The number of features and mean squared error are selected as objective functions. The features from the data are also extracted using the fast Fourier and wavelet transforms for comparison. Finally, the support vector machine and artificial neural network are employed to isolate faults. Two circuits under test are simulated, and the output signals of the faulty and fault-free circuits are extracted by the Monte Carlo analysis. According to the obtained simulation results, the proposed method with a low-dimensional feature vector outperformed the previous methods, and the computational time has also reduced significantly.

Improved epileptic seizure detection using singular spectrum empirical mode decomposition and machine learning approach

Electroencephalography (EEG) is multi-channel electrical signal acquiring tool used to acquire the brain signals and used in various application such as neurological disorder detection and prediction, monitoring psychological condition, emotion analysis and Brain-Computer Interface (BCI). Epilepsy is a highly prevalent neurological disorder caused by the occurrence of seizures. Epilepsy is a neurological condition of the brain where some neurons change its behaviours such as hyper-activeness and channel synchronization. The EEG signals recorded from epileptic patients are analysed for monitoring and extracting behaviour of signals during onset seizures. The Time and Frequency Domain (TFD), Wavelet Transform (WT) and Empirical Mode Decomposition (EMD) are well-proven feature extraction methods used for various applications. The objective of the paper is to propose a new effective method, Singular Spectrum Empirical Mode Decomposition (SSEMD) for effective classification of Normal and Epileptic EEG Signals. The high-performance machine learning classifiers are used for classification of EEG signal in normal and epileptic class. The performance observed with the proposed feature extraction method is 99.8 percent of detection accuracy with nearly zero false positive rates. The average dimensionality reduction of 70 percent of total feature space is observed due to the use of ANOVA.

A hybrid method for removal of power line interference and baseline wander in ECG signals using EMD and EWT

The removal of power line interference (PLI) and baseline wander (BW) is an essential task for the analysis of the ECG signal. This paper presents a new method to suppress PLI and BW from electrocardiogram (ECG) signals using a hybrid approach of empirical mode decomposition (EMD) and empirical wavelet transform (EWT). The proposed method consists of four stages: EMD-based ECG signal decomposition, construction of high and low-frequency sub-signals based on the ratio of the zero crossing numbers (RZCN), estimation of PLI and BW through using EWT, and the interference subtraction. The proposed method is tested and validated through experiments performed over a wide variety of real ECG signals which are available on the MIT-BIH arrhythmia database. The results show that the proposed method can effectively remove PLI and BW without distorting the ECG signal components. The efficacy of the proposed method is compared with some of the state-of-the-art methods in terms of output signal-to-noise ratio (SNRo), cross-correlation coefficient (ρ), percent root mean square difference (PRD), and weighted diagnostic distortion (WDD) at a different input signal-to-noise ratio (SNRi). The qualitative and quantitative results show that the proposed method outperforms the existing methods.

Assessing Nonlinear Dynamics and Trends in Precipitation by Ensemble Empirical Mode Decomposition (EEMD) and Fractal Approach in Benin Republic (West Africa)

Climate dynamics and trends have significant environmental and socioeconomic impacts; however, in the Benin Republic, they are generally studied with diverse statistical methods ignoring the nonstationarity, nonlinearity, and self-similarity characteristics contained in precipitation time series. This can lead to erroneous conclusions and an unclear understanding of climatic dynamics. Based on daily precipitation data observed in the six synoptic stations of Benin Republic, in the period from 1951 to 2010, we have proposed (i) determining the local trends of precipitations, (ii) investigating precipitation nonlinear dynamics, and (iii) assessing climatic shift in the study period by Ensemble Empirical Mode Decomposition (EEMD) and Multifractal Detrended Fluctuation Analysis (MFDFA) method. To overcome the detrending issue in the standard MFDFA method, the EEMD algorithm is embedded into the MFDFA. The study period is subdivided into three subperiods: 1951–1970, 1971–1990, and 1991–2010. Intrinsic Mode Functions (IMFs) are obtained according to the climatic region in which the stations are located. Results show that precipitation variation is significantly governed by the five first IMFs, in which oscillation periods vary from 1 to 25 days. The trend curves decrease at all the synoptic stations, and their slope values vary accordingly to the subperiods. Referring to the values of the multifractal spectrum parameters, α 0 , and the width of the spectrum w , consistent changes are observed regardless of the subperiods and the concerned stations. The spatial and temporal variability of precipitation indicates that the multifractal properties are good indicators for assessing changes in precipitation dynamics, and the changes in their features could be explained by the global change than by the local climate variation (climatic zones). Despite the observed differences in multifractal spectra properties from the three subperiods, it is not possible to verify the subdivision of 1951–2010 in three subperiods as it is done by previous studies in West Africa. Our findings can be used in the validation of global and regional climate models since a valid model should explain empirically detected scaling properties in observed data.

Cardiac Severity Classification Using Pre Trained Neural Networks.

Electrocardiogram (ECG) is the most effective instrument for making decisions about various forms of heart disease. As a result, several researchers have focused on the ECG signal to extract the features of heartbeats with high precision and efficiency. This article offers a hybrid approach to classifying different cardiac conditions using the Feed Forward Back Propagation Neural Network (FFBPNN), by providing a pre-processed ECG signal as an excitation. The modified ECG signal is obtained through the combination of EMD (Empirical Mode Decomposition) and DWT (Discrete Wavelet Transform). In this proposed method, the input signal is first decomposed into the Intrinsic Mode Functions (IMF's) and the first three IMF's are combined to obtain a modified partially denoted ECG sample and then DWT is used to obtain an improved denoised signal. This pre-processed signal is classified using the Neural Network architecture. For the EMD approach, the ECG-based EMD-DWT signal provides improved classification accuracy of 67, 0762 percent, 90, 4305 percent for the DWT approach, and 95,0797 percent for the proposed technique. The methodology is applied to the MIT-BIH database and, in terms of classification accuracy, is found to be higher than the different methodologies.

Detection of ventricular arrhythmia using hybrid time-frequency-based features and deep neural network.

Sudden cardiac death (SCD) is a major cause of death among patients with heart diseases. It occurs mainly due to ventricular tachyarrhythmia (VTA) which includes ventricular tachycardia (VT) and ventricular fibrillation (VF) conditions. The main challenging task is to predict the VTA condition at a faster rate and timely application of automatic external defibrillator (AED) for saving lives. In this study, a VF/VT classification scheme has been proposed using a deep neural network (DNN) approach using hybrid time-frequency-based features. Two annotated public domain ECG databases (CUDB and VFDB) were used as training, test, and validation of datasets. The main motivation of this study was to implement a deep learning model for the classification of the VF/VT conditions and compared the results with other standard machine learning algorithms. The signal is decomposed with the wavelet transform, empirical mode decomposition (EMD) and variable mode decomposition (VMD) approaches and twenty-four are extracted to form a hybrid model from a window of length 5s length. The DNN classifier achieved an accuracy (Acc) of 99.2%, sensitivity (Se) of 98.8%, and specificity (Sp) of 99.3% which is comparatively better than the results of the standard classifier. The proposed algorithm can detect VTA conditions accurately, hence could reduce the rate of misinterpretations by human experts and improves the efficiency of cardiac diagnosis by ECG signal analysis.

EEG Artifact Removal System for Depression Using a Hybrid Denoising Approach.

Introduction:Several computer-aided diagnosis systems for depression are suggested for use by clinicians to authorize the diagnosis. EEG may be used as an objective analysis tool for identifying depression in the initial stage to avoid it from reaching a severe and permanent state. However, artifact contamination reduces the accuracy in EEG signal processing systems.Methods:This work proposes a novel denoising method based on Empirical Mode Decomposition (EMD) (with Detrended Fluctuation Analysis (DFA) and wavelet packet transform. Initially, real EEG recordings corresponding to depression patients are decomposed into various mode functions by applying EMD. Then, DFA is used as the mode selection criteria. Further Wavelet Packets Decomposition (WPD)-based evaluation is applied to extract the cleaner signal.Results:Simulations were conducted on real EEG databases for depression to demonstrate the effects of the proposed techniques. To conclude the efficacy of the proposed technique, SNR and MAE were identified. The obtained results indicated improved signal-to-noise ratio and lower values of MAE for the combined EMD-DFA-WPD technique. Additionally, Random Forest and SVM (Support Vector Machine)-based classification revealed the improved accuracy of 98.51% and 98.10% for the proposed denoising technique. Whereas the accuracy of the EMD- DFA is 98.01% and 95.81% and EMD combined with DWT technique equaled98.0% and 97.21% for the EMD- DFA technique for RF and SVM, respectively, compared to the proposed method. Furthermore, the classification performance for both classifiers was compared with and without denoising to highlight the effects of the proposed technique.Conclusion:Proposed denoising system results in better classification of depressed and healthy individuals resulting in a better diagnosing system. These results can be further analyzed using other approaches as a solution to the mode mixing problem of the EMD approach.

Joint empirical mode decomposition, exponential function estimation and L 1 norm approach for estimating mean value of photoplethysmogram and blood glucose level

Continuous monitoring of the blood glucose levels is essential and critical for controlling diabetes and its complications. With the improvement of the measurement accuracy of the acquisition devices developed in recent decades, developing the optical-based methods for performing the non-invasive blood glucose estimation for the consumer applications becomes very important. The authors’ previous work is based on the heart rate variability of the electrocardiogram and the existing method is based on applying the random forest to the features extracted from the photoplethysmogram. However, the accuracies of these two methods are not very high. In this study, a joint empirical mode decomposition and exponential function estimation approach is proposed for estimating the mean value of a photoplethysmogram acquired from a wearable non-invasive blood glucose device. Also, the exponential function fitting approach is employed for estimating the blood glucose levels via an L 1 norm formulation. The computer numerical simulation results show that the estimation accuracy based on their proposed method is higher than that based on the state-of-the-art methods. Therefore, their proposed method can be employed for performing blood glucose estimation effectively.

Detection of small-scale/large-scale interactions in turbulent wall-bounded flows

A study of the combined effect of outer-layer large-scale superposition, amplitude modulation, and distortions on the near-wall dynamics of turbulent wall-bounded flows is presented. A novel approach to detect the amplitude modulation is introduced that more clearly reveals this phenomenon compared to existing techniques. The study also shows that scale separation by the empirical mode decomposition approach and by conventional spectral filtering yields similar conclusions about the interaction mechanisms.

Predicting Primary Energy Consumption Using Hybrid ARIMA and GA-SVR Based on EEMD Decomposition

Forecasting energy consumption is not easy because of the nonlinear nature of the time series for energy consumptions, which cannot be accurately predicted by traditional forecasting methods. Therefore, a novel hybrid forecasting framework based on the ensemble empirical mode decomposition (EEMD) approach and a combination of individual forecasting models is proposed. The hybrid models include the autoregressive integrated moving average (ARIMA), the support vector regression (SVR), and the genetic algorithm (GA). The integrated framework, the so-called EEMD-ARIMA-GA-SVR, will be used to predict the primary energy consumption of an economy. An empirical study case based on the Taiwanese consumption of energy will be used to verify the feasibility of the proposed forecast framework. According to the empirical study results, the proposed hybrid framework is feasible. Compared with prediction results derived from other forecasting mechanisms, the proposed framework demonstrates better precisions, but such a hybrid system can also be seen as a basis for energy management and policy definition.

RETRACTED: Forecasting biofuel production using adaptive integrated optimization network model

Abstract Recently, biofuel production has been increasing all over the world over the past few decades owing to the relative changes in energy price and policy intervention. Several research studies are focussed on producing optimal biofuel production using numerous approaches. However, those approaches have few drawbacks such as computational complexity, less accuracy, high error value rate, etc. So to overcome such shortcomings, this paper aims in developing a novel Adaptive Integrated Optimization Network (AION approach) to attain optimal biofuel production with high accuracy and minimum error value rate. Moreover, the proposed AION approach comprises four significant phases namely Pre-processing of data, Re-construction of component, Prediction of an individual and Ensemble forecasting. The initial process utilizes an empirical mode decomposition (EMD) approach to divide a raw complicated data. The reconstruction phase employs the Fine to coarse (FTC) reconstructing approach that rebuilds the IMF into two different t-testing components namely the low-frequency component and the high-frequency component. Moreover, Gray Wolf Optimization (GWO) based Adaptive Wavelet Neural Network (AWNN) and Long Short Term Memory-Recurrent Neural Network (LSTM-RNN) approaches are applied to predict the low and the high-frequency components. Then the forecasted results are summed by using an ADD operator so as to obtain absolute forecasting results. The experimental analysis reveals that the proposed AION approach provides an optimal biofuel production with enhanced accuracy, minimum error value rate, and complexity issues.

Application of machine learning techniques and empirical mode decomposition for the classification of analog modulated signals

<!--[if gte mso 11]><w:PermStart w:id="205147274"
 w:edGrp="everyone"/><![endif]--><p class="Abstract">In this article, an automatic Analog Modulation Classifier based on Empirical mode decomposition and Machine learning approaches (AMC-EM) is proposed. The AMC-EM operates without a priori information and can recognise typical analog modulation schemes: amplitude modulation, phase modulation, frequency modulation, and single sideband modulation. The AMC-EM uses Empirical Mode Decomposition (EMD) to evaluate the features of the signal for the successive classification by using Machine Learning (ML). In the design of the AMC-EM, the selection of the specific ML technique is performed by comparing, with numerical tests, the performance of the (i) Support Vector Machine (SVM), (ii) k-nearest neighbor classifier, and (iii) adaptive boosting, since they are commonly used in the field of signal classification. The tests have highlighted that the SVM, specifically the quadratic SVM, permits the best possible performance concerning classification accuracy, by considering different noise intensities superimposed on the signal. To assess the advantages of the proposal, a comparison with other classifiers available in the literature has been undertaken through numerical tests. Finally, the AMC-EM is experimentally evaluated, and the experimental results agree with those of the simulation.</p><p class="Abstract"><span lang="EN-US"><br /><!--[if gte mso 11]><w:PermEnd w:id="205147274"/><![endif]--></span></p>

Low-frequency noise attenuation in seismic and microseismic data using mathematical morphological filtering

SUMMARYLow-frequency noise is one of the most common types of noise in seismic and microseismic data. Conventional approaches, such as the high-pass filtering method, utilize the low-frequency nature and distinguish between signal and noise based on their different frequency contents. However, conventional approaches are limited or even invalid when the signal and noise shares the same frequency band. Moreover, high-pass filtering method will suppress not only low-frequency noise but also low-frequency signal when they overlap in a same frequency band, which is extremely important in the inversion process for building the subsurface velocity model. To overcome the drawbacks of conventional high-pass filtering approach, we developed a novel method based on the mathematical morphology theorem to separate signal and noise using their differences in morphological scale. We extracted empirical relation between morphological scale and frequency band so that the mathematical morphology based approach can be conveniently used in low-frequency noise attenuation. The proposed method is termed as the mathematical morphological filtering (MMF) method. We compare the MMF approach with high-pass filtering and empirical mode decomposition (EMD) approaches using synthetic, reflection seismic and microseismic examples. The various examples demonstrate that the proposed MMF method can preserve more low-frequency signal than the high-pass filtering approach, and is more efficient and causes fewer artefacts than the EMD approach.

Mean-optimized mode decomposition: An improved EMD approach for non-stationary signal processing

As an effective signal separation method of non-stationary signal, empirical mode decomposition (EMD) has been widely used in the data or time series analysis of many engineering fields. However, the decomposing result of EMD often is affected by the fitting in mean curve construction and the sifting process. In this paper, the mean-optimized mode decomposition (MOMD) procedure is proposed to enhance the performance of the original EMD in mean curve construction. Also, the proposed MOMD algorithm is compared with original EMD through analyzing two artificial signals and the analysis results demonstrate that MOMD has much more significantly improvement in decomposition performance and precision than the original EMD. Last, MOMD is introduced to the signal processing stemming from the faulty rolling bearing and the rotor system with failure. Also, the comparison of the proposed MOMD method with EMD was made and the analysis results show that MOMD obtains much more accurate IMFs and fault diagnostic effect than the original EMD method.

A complete empirical ensemble mode decomposition and support vector machine-based approach to predict Bitcoin prices

Bitcoin as an asset class has received phenomenal investor attention and is considered to have similar characteristics like gold. This study aims to analyze the price behavior of bitcoin and apply machine learning algorithm for its prediction. Understanding the nature of Bitcoin price series is a multi-scale problem, and it can be best examined by analyzing its compositional characteristics. This study uses complete empirical ensemble mode decomposition (CEEMD) to analyze the nature of Bitcoin price series. Daily Bitcoin prices from 2012 to 2018 are used to perform CEEMD to identify the short term, medium term, and long-term trend in the Bitcoin price series. The study uses support vector machine (SVM) learning algorithm to find whether it can predict Bitcoin prices and finds that SVM predicts five steps ahead Bitcoin prices for the short term, medium term, long term, and overall Bitcoin price level.

EMD-Based Borehole TEM Array Signal Denoising and Baseline Wander Correction for NDT of Downhole Casings

Transient electromagnetic (TEM) techniques have been proven to be efficient for nondestructive testing (NDT) operations due to their pulsed eddy-current properties. However, in the field of downhole measurements, harsh environments may significantly influence the NDT performance in downhole casings. In this paper, an empirical mode decomposition (EMD) method based on borehole TEM array signal denoising and baseline wander (BW) correction is proposed to compensate for the bad measurement conditions that affect downhole NDT. Based on the borehole TEM signal model, we investigated the principle of the EMD approach for the borehole TEM response, where the background magnetic noise and temperature drift effects were analyzed by considering the motion measurement and effective permeability. It was found out that although the BW can be effectively removed with the EMD approach, the performance of the signal denoising is closely related to the measurement speed of the downhole NDT sensors. To solve this problem, we proposed an array-based ensemble EMD method to improve the denoising performance of the borehole TEM signals by formulating a three-dimensional borehole TEM data structure, where the generation of the noise-aided data can be more efficient by employing the borehole TEM array. The performance of the proposed method was verified by applying it to a borehole TEM system for the NDTs of oil-well casings. In addition, field experiments were conducted, and the results demonstrated the effectiveness of the proposed method.