Relevant Intrinsic Mode Functions Research Articles

A VMD-deep learning approach for individual load monitoring and forecasting for residential buildings energy management

This paper presents a comprehensive solution for Multi-Individual load disaggregation and Multi-individual load Forecasting in residential energy systems. The approach combines Variable Mode Decomposition (VMD) and deep learning algorithms, incorporating feature selection using Random Forest Regressor and parameter optimization through the Salp Swarm Algorithm (SSA). Correlation analysis is used to identify the most relevant Intrinsic Mode Functions (IMFs) for each appliance, while self-correlation is used to determine the past window for forecasting appliances’ usage a day ahead. The robustness of VMD against noise contributes to the improved accuracy of appliance designation. The load disaggregation strategy yields significant results, indicating the high performance of the disaggregation tasks of these models. Specifically, the proposed disaggregation models exhibit an average RMSE and MAPE of 4.10 W and 4.14 %, respectively. Furthermore, we conduct a comparison among three deep learning architectures (GRU, LSTM, 1DCNN) based Multi-Individual load forecasting models, revealing that the GRU-based MILF model outperforms the other two models, it shows an average performance of RMSE and MAPE of 9.54 W and 8.10 %. The research provides valuable insights and a practical solution for accurate load disaggregation and forecasting, contributing to enhanced energy efficiency, cost savings, and sustainable power consumption in residential settings, and give detailed energetic bill with the aim to analyze the occupant consumption behavior, and gives depth insights about power consumption and cost of each appliance and their operation time.

An enhanced indirect modal identification procedure for bridges based on the dynamic response of moving vehicles

Indirect dynamic structural identification, referred to as Vehicle Scanning Method (VSM), pertains to the estimation of bridge modal parameters by using data recorded from sensors directly mounted on a moving vehicle. This procedure has recently gained increasing attention due to its low cost and simple implementation. Nevertheless, the non-stationary and complex nature of the vehicle–bridge interaction phenomenon poses some limitations to the applicability of the method, especially in terms of accuracy. In this regard, in this paper, an enhanced procedure for the dynamic identification of bridges modal parameters based on the VSM is introduced, taking into account the effects of vehicle/bridge damping and road pavement roughness. Specifically, based on the Variational Mode Decomposition (VMD) method, the relevant Intrinsic Mode Functions (IMFs) and corresponding modal frequencies are determined from the recorded signal. Further, the Natural Excitation Technique (NExT) is adopted in conjunction with a noise-robust area ratio-based approach, for modal damping ratios estimation. Finally, mode shapes are evaluated by properly correcting the instantaneous amplitudes of each IMF considering the influence of the corresponding estimated modal damping ratio. Several numerical simulations are presented to show the validity of the proposed procedure and comparisons with the classical Hilbert Spectrum-based identification approach are employed to assess its reliability and improved accuracy.

Machine learning‐based classification of multiple heart disorders from PCG signals

AbstractTimely and accurate detection and diagnosis of heart disorders is a significant problem in the medical community since the mortality rate is increasing. Pulsing of cardiac structures and blood turbulence creates heart sounds recorded and detected through Phonococardiogram (PCG). As a non‐invasive technique, PCG signals have a strong ability to be used for designing automatic classification of possible heart disorders. This paper presents an expert system design for the detection and classification of PCG signals for five classes, namely, healthy, aortic stenosis, mitral stenosis, mitral regurgitation, and mitral valve prolapse. In this work, a single‐channel PCG signal is first decomposed using Empirical Mode Decomposition (EMD) into different modes known as intrinsic mode functions (IMFs). Manual signal analysis is applied to identify the relevant IMFs to construct a preprocessed signal. We proposed an automated energy‐based signal reconstruction through IMFs. The proposed algorithms automatically identify the relevant IMFs and added them together to form a preprocessed signal. After preprocessing, the first nine features of Mel Frequency Cepstral Coefficients (MFCC) were computed and passed to several classification methods such as Fine Tree, Quadratic Discriminant, Kernel Naive Bayes, Support Vector Machines (SVM), Fine K‐Nearest Neighbours (Fine‐KNN), Ensemble Bagged Trees and Neural Network. The best performance of 99.3% accuracy was obtained via Fine‐KNN using 10‐fold cross‐validation. The proposed method was evaluated on a publicly available dataset of heart sounds. The proposed method demonstrated improved performance as compared to the existing state‐of‐the‐art methods.

A Novel Rolling Bearing Fault Diagnosis Method Based on MFO-Optimized VMD and DE-OSELM

Rolling bearings are critical in maintaining smooth operation of rotating machinery and considerably influence its reliability. The signals collected from rolling bearings in field conditions are often subjected to noise, creating a challenge to extract weaker fault features. This paper proposes a rolling bearing fault diagnosis method that addresses the above-mentioned problem through the moth-flame optimization algorithm optimized variational mode decomposition (MFO-optimized VMD) and an ensemble differential evolution online sequential extreme learning machine (DE-OSELM). By using the dynamic adaptive weight factor and genetic algorithm cross operator, the optimization accuracy and global optimization ability of the moth-flame optimization (MFO) are improved, and the two basic parameters of VMD decomposition level and quadratic penalty factor are adaptive selected. Since the vibration characteristics of the signal cannot be fully interpreted by a single index, The effective weighted correlation sparsity index (EWCS) is utilized to extract the relevant intrinsic mode functions (IMF) of VMD decomposition and extract their energies as features. In order to improve the classification accuracy, The energy feature set is subsequently inputted into DE-OSELM for training and classification purposes, and the proposed method is assessed via a sample set with four different health states of actual rolling bearings. Our proposed method results are compared with other diagnosis methods, proving its feasibility to diagnose rolling bearing faults with higher classification accuracy.

An Ice-Penetrating Signal Denoising Method Based on WOA-VMD-BD

Chang’E-7 will be launched around 2026 to explore resources at the lunar south pole. Glaciers are suitable scenes on the earth for lunar penetrating radar verification. In the verification experiment, ice-penetrating signals are inevitably polluted by noise, affecting the accuracy and reliability of glacier detection. This paper proposes a denoising method for ice-penetrating signals based on the combination of whale optimization algorithm (WOA), variational mode decomposition (VMD), and the improved Bhattacharyya distance (BD). Firstly, a fitness function for WOA is established based on permutation entropy (PE), and the number of decomposition modes K and the quadratic penalty factor α in the VMD are optimized using WOA. Then, VMD is performed on the signal to obtain multiple intrinsic mode functions (IMFs). Finally, according to the BD, the relevant IMFs are selected for signal reconstruction and denoising. The simulation results indicate the strengths of this method in enhancing the signal-to-noise ratio (SNR), and its performance is better than empirical mode decomposition (EMD). Experiments on the detected signals of the Mengke Glacier No. 29 indicate that the WOA-VMD-BD method can efficiently eliminate noise from the data and procure well-defined layered profiles of the glacier. The research in this paper helps observe the layered details of the lunar regolith profile and interpret the data in subsequent space exploration missions.

Design data decomposition-based reference evapotranspiration forecasting model: A soft feature filter based deep learning driven approach

Reference evapotranspiration can cause huge discrepancies in soil moisture and runoff which is responsible for uncertainties in drought warning systems. Reference evapotranspiration (ETo) is one of the major drought elements that leads to soil dryness, vegetation surfaces and transpiration. An innovative strategy is proposed based on Multivariate Variational Mode Decomposition hybridized with Soft Feature Filter and Gated Recurrent Unit to design MVMD-SoFeFilterGRU to forecast one-day daily ETo at (t+1). Initially, the importance of each predictor was determined using correlation matrix to identify significant lags at (t+1). Next, the intrinsic mode functions (IMFs) in terms of signals were obtained via MVMD to decompose the lags. The SoFeFilter approach was employed to select the most relevant IMFs which were then incorporated into the GRU to construct the MVMD-SoFeFilter-GRU model to forecast one-day ahead daily ETo. For comparison, the LSTM, BiLSTM, RNN, BiRNN, and BiGRU models were combined with MVMD and SoFeFilter to create MVMD-SoFeFilterLSTM, MVMD-SoFeFilterBiLSTM, MVMD-SoFeFilterRNN, MVMD-SoFeFilterBiRNN, and MVMD-SoFeFilterBiGRU models. Further, the results were also compared against the standalone GRU, BiGRU, LSTM, BiLSTM, RNN, and BiRNN models based on goodness-of-fit metrics for two stations in Queensland, Australia. For example, in Gympie station, the MVMD-SoFeFilterGRU model produced highest values of WIE=0.9795, NSE=0.9234, LME=0.7645, and for Redcliffe station, these metrics are WIE=0.9800, NSE=0.9257, LME=0.7580. The findings confirm that the MVMD-SoFeFilterGRU is the most precise to forecast one-day ahead ET0.

Performance of Bearing Ball Defect Classification Based on the Fusion of Selected Statistical Features.

Among the existing bearing faults, ball ones are known to be the most difficult to detect and classify. In this work, we propose a diagnosis methodology for these incipient faults’ classification using time series of vibration signals and their decomposition. Firstly, the vibration signals were decomposed using empirical mode decomposition (EMD). Time series of intrinsic mode functions (IMFs) were then obtained. Through analysing the energy content and the components’ sensitivity to the operating point variation, only the most relevant IMFs were retained. Secondly, a statistical analysis based on statistical moments and the Kullback–Leibler divergence (KLD) was computed allowing the extraction of the most relevant and sensitive features for the fault information. Thirdly, these features were used as inputs for the statistical clustering techniques to perform the classification. In the framework of this paper, the efficiency of several family of techniques were investigated and compared including linear, kernel-based nonlinear, systematic deterministic tree-based, and probabilistic techniques. The methodology’s performance was evaluated through the training accuracy rate (TrA), testing accuracy rate (TsA), training time (Trt) and testing time (Tst). The diagnosis methodology has been applied to the Case Western Reserve University (CWRU) dataset. Using our proposed method, the initial EMD decomposition into eighteen IMFs was reduced to four and the most relevant features identified via the IMFs’ variance and the KLD were extracted. Classification results showed that the linear classifiers were inefficient, and that kernel or data-mining classifiers achieved classification rates through the feature fusion. For comparison purposes, our proposed method demonstrated a certain superiority over the multiscale permutation entropy. Finally, the results also showed that the training and testing times for all the classifiers were lower than 2 s, and 0.2 s, respectively, and thus compatible with real-time applications.

A multivariate EMD-LSTM model aided with Time Dependent Intrinsic Cross-Correlation for monthly rainfall prediction

Accurate prediction of rainfall is a complex problem because of the large number of controlling factors, complex interrelationships between them and the multiscaling behaviour of the process. Because of the multiscaling behaviour of the problem, the use of hybrid modelling involving decomposition techniques is preferable over strenuous physical models and standalone driven techniques for accurate rainfall predictions. This study proposes a novel hybrid modelling framework integrating Long Short Term Memory (LSTM) and Multivariate Empirical Mode Decomposition (MEMD) aided with Time Dependent Intrinsic Cross-Correlation (TDICC) analysis algorithm for monthly rainfall predictions. The application of the proposed model is demonstrated for monthly rainfall prediction of 2005–2015 period at All India spatial domain considering El-Niño Southern Oscillation(ENSO), Indian Ocean Dipole(IOD) and five antecedent values of rainfall are the input variables. The proposed framework first uses MEMD to obtain a set of orthogonal components namely Intrinsic Mode Functions (IMFs) identifying and aligning the common scales embedded in the multiple input variables considered. Subsequently, scale specific rainfall information are predicted by incorporating them on relevant IMFs and their significant lags identified through TDIC and TDICC analyses. Final aggregation of predicted rainfall components from different scales gives the monthly rainfall of a generic time step ahead. The efficacy of the proposed MEMD-TDICC-LSTM framework is compared with five other hybrid models such as MEMD-TDIC-ANN, MEMD-TDICC-ANN, MEMD-ACO-ANN, MEMD-ACO-LSTM and MEMD-TDIC-LSTM, which used Time Dependent Intrinsic Correlation (TDIC) and Ant Colony Optimization (ACO) for predictor selection and Artificial Neural Network (ANN) as modelling tool. The study has used different graphical representations and ten different statistical performance evaluation measures for the prediction of validation period, it is observed that the proposed model could achieve a predictive skill of 0.98, Nash-SutcliffeEfficiency (NSE) of 0.95 and Index of Agreement (IA) of 0.91 which is better than all the five remaining models. The capability of MEMD-TDICC-LSTM model to predict the extremes is also confirmed by using the bar graph for the drought year rainfall of 2009 and is observed that the model is successful in capturing the extremes with an annual rainfall 975.16 mm closer to observed value 927.3 mm. MEMD algorithm facilitates the inclusion of multiple large scale climatic oscillations as inputs and their multi time scale decomposition, TDICC helps to fix the relevant inputs at different time scales and the LSTM functions as the robust modelling tool. Further, the framework using this specific combination resulted in substantial reduction in modelling complexity and faster execution, as the approach considers only the most relevant and significant inputs in the process.

Dynamic Changes and Temporal Association with Ambient Temperatures: Nonlinear Analyses of Stroke Events from a National Health Insurance Database.

Background: The associations between ambient temperatures and stroke are still uncertain, although they have been widely studied. Furthermore, the impact of latitudes or climate zones on these associations is still controversial. The Tropic of Cancer passes through the middle of Taiwan and divides it into subtropical and tropical areas. Therefore, the Taiwan National Health Insurance Database can be used to study the influence of latitudes on the association between ambient temperature and stroke events. Methods: In this study, we retrieved daily stroke events from 2010 to 2015 in the New Taipei and Taipei Cities (the subtropical areas) and Kaohsiung City (the tropical area) from the National Health Insurance Research Database. Overall, 70,338 and 125,163 stroke events, including ischemic stroke and intracerebral hemorrhage, in Kaohsiung City and the Taipei Area were retrieved from the database, respectively. We also collected daily mean temperatures from the Taipei and Kaohsiung weather stations during the same period. The data were decomposed by ensemble empirical mode decomposition (EEMD) into several intrinsic mode functions (IMFs). There were consistent 6-period IMFs with intervals around 360 days in most decomposed data. Spearman’s rank correlation test showed moderate-to-strong correlations between the relevant IMFs of daily temperatures and events of stroke in both areas, which were higher in the northern area compared with those in the southern area. Conclusions: EEMD is a useful tool to demonstrate the regularity of stroke events and their associations with dynamic changes of the ambient temperature. Our results clearly demonstrate the temporal association between the ambient temperature and daily events of ischemic stroke and intracranial hemorrhage. It will contribute to planning a healthcare system for stroke seasonally. Further well-designed prospective studies are needed to elucidate the meaning of these associations.

Modified fuzzy Q-learning based wind speed prediction

Renewable energy has taken a center stage in sustainable and environmentally safe power generation. In this work, a novel model free Reinforcement Learning based wind speed forecasting technique has been proposed. Our technique uses modified fuzzy Q learning (MFQL) framework to accurately predict 1-min ahead wind speed from the publicly available data online. Empirical Mode Decomposition (EMD) and Pearson’s correlation coefficient have been used in the pre-processing stages to identify seven most relevant intrinsic mode functions (IMF) from the raw wind speed data. These IMFs form the inputs to an MFQL based forecaster which accurately forecasts wind speed using a reward/punishment approach. MFQL predictor is put to test on wind speed data obtained from National Institute of Wind Energy and Wind Resource Assessment data portal for 10 Indian cities, i.e., Bhogat, Chandori, Kotada, Charanka, Gandhi Nagar, Jambua, Keshod, Sadodar, Surat and Vartej located in the state of Gujarat, India. Our predictor is able to achieve an accuracy of 96.23% for Gandhi Nagar, 94.84% for Bhogat, 94.12% for Kotada and similar results are obtained for other locations. MFQL approach has been compared with SVR and k-NN. Results show that MFQL approach has higher accuracy than SVR and k-NN techniques.

Fault diagnosis of rolling bearings in non-stationary running conditions using improved CEEMDAN and multivariate denoising based on wavelet and principal component analyses

This paper presents a fault diagnosis method for rolling bearings working in non-stationary running conditions. The proposed approach is based on an improved version of the improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN), the multivariate denoising using wavelet analysis and principal component analysis (PCA), the spectral kurtosis, and the order tracking analysis (OTA). The results show that the improved CEEMDAN has completely decomposed the raw signal into different intrinsic mode functions (IMFs) representing the natural oscillatory modes embedded into the signal. The most relevant IMF from which the defect was extracted is selected by the kurtogram plot which allows locating the optimal frequency band having the highest kurtosis value. Multivariate denoising based on wavelet analysis and PCA is used to increase the signal-to-noise ratio (SNR) of the selected IMF. The results show the great contribution of the denoising approach when comparing the selected denoised IMF with the original one. Finally, order tracking analysis is applied on the denoised IMF’s envelope to remove the effect of speed variation, and an envelope order spectrum is obtained. The proposed approach is first applied on theoretical signal simulating rolling bearing defect in variable regime including three different phases. The final order spectrum shows exactly the simulated defect order and several of its harmonics. For the experimental validation, several signals of defective rolling bearings have been measured on the Machine Fault Simulator test rig in variable regime. Despite the combined variable regime including acceleration-constant regime-deceleration, at the same time, the obtained results indicate the efficiency of the proposed method to extract the fault order with high accuracy. The maximum error between the theoretical order and the experimentally obtained one was 1.3% for outer race defect and 1% for inner race defect. Finally, the performances of the proposed method are compared to those of another diagnosis method designed for variable regime conditions. Both outer race and inner race defects are considered in acceleration regime. The results show the superiority of the proposed method to highlight the defect order with highest clarity.

Classification of low-density EEG for epileptic seizures by energy and fractal features based on EMD.

We are here to present a new method for the classification of epileptic seizures from electroencephalogram (EEG) signals. It consists of applying empirical mode decomposition (EMD) to extract the most relevant intrinsic mode functions (IMFs) and subsequent computation of the Teager and instantaneous energy, Higuchi and Petrosian fractal dimension, and detrended fluctuation analysis (DFA) for each IMF. We validated the method using a public dataset of 24 subjects with EEG signals from 22 channels and showed that it is possible to classify the epileptic seizures, even with segments of six seconds and a smaller number of channels (e.g., an accuracy of 0.93 using five channels). We were able to create a general machine-learning-based model to detect epileptic seizures of new subjects using epileptic-seizure data from various subjects, after reducing the number of instances, based on the k-means algorithm.

Gear Fault Diagnosis Based on Kurtosis Criterion VMD and SOM Neural Network

A gear fault diagnosis method based on kurtosis criterion variational mode decomposition (VMD) and self-organizing map (SOM) neural network is proposed. Firstly, the VMD algorithm is used to decompose the gear vibration signal, and the instantaneous frequency mean is calculated as the evaluation index, and the characteristic curve is drawn to screen out the most relevant intrinsic mode functions (IMFs) of the original vibration signal. Then, the number of VMD decompositions is determined, and the kurtosis value of IMFs are extracted to form the feature vectors. Then, the kurtosis value feature vectors of IMFs are normalized to form the kurtosis value normalized vectors. Finally, the normalized vectors of kurtosis value are input into SOM neural network to realize gear fault diagnosis. When the number of training times of SOM neural network is 100, the gear fault category is accurately classified by SOM neural network. The results show that when the training times of SOM neural network is 100 times, the gear fault diagnosis method, based on the kurtosis criterion VMD and SOM neural network is 100%, which indicates that the new method has a good effect on gear fault diagnosis.

Research on Multiple Sensors Vehicle Detection With EMD-Based Denoising

The performance of vehicle detection system is often affected by both internal and external noise. In this paper, a novel vehicle detection scheme called vehicle detector based on EMD-HT and multichannel GLRT (V-EHMG) is proposed, which composed of signal denoising part based on empirical mode decomposition (EMD) and signal detection part which takes advantage of the spatial–temporal relationship acquired by multiple sensors. In the first part, we propose a new denoising algorithm null-hypothesis interval threshold EMD denoising (EMD-HT) to check the similarity between the probability density function of the decomposed intrinsic mode functions (IMFs) from input signal and the generalized Gaussian distribution to determine relevant IMFs for signal reconstruction. Combined the interval threshold method, the denoised signals are acquired. In the second part, we use multichannel generalized likelihood ratio test (GLRT) detector to judge the presence of other vehicles with the denoised signals as the input. Simulation and physical experimental results show that with V-EHMG, the signal-to-noise ratio of the signal is doubled and the detection range is improved more than 40% compared with the traditional methods.

Fault Separation and Detection for Compound Bearing-Gear fault Condition Based on Decomposition of Marginal Hilbert Spectrum

For the rotating machinery system, it is a challenge to explore fault detection and diagnosis for multiple-faults condition, which simultaneously contains faulty bearing components and faulty gear components. In the study, a fault feature separation and extraction approach is proposed for the bearing-gear fault condition through combining empirical mode decomposition (EMD), Hilbert transform (HT), principal component analysis (PCA), independent component analysis (ICA) techniques. Firstly, EMD is implemented to decompose the single sensor signal to obtain multiple sub-band signals termed as the intrinsic mode functions (IMFs). Secondly, the most relevant IMFs to bearing and gear fault features are selected to construct multiple-channels model with the help of the simulated bearing and gear fault signals. Thirdly, HT is utilized to compute marginal Hilbert spectrum (MHS) for each IMF in multiple-channels model, to construct an MHS matrix. Finally, some statistically independent components are obtained by decomposing the MHS matrix with PCA and ICA, and multiple fault features are identified from these components. The experimental application of the proposed method is put into a bearing-belt-gearbox union machinery system to evaluate its validity. The experimental analysis results indicate that the proposed method is effective to separate and extract a bearing fault and a gear fault for two types of compound bearing-gear fault conditions.

ECG signal filtering based on CEEMDAN with hybrid interval thresholding and higher order statistics to select relevant modes

In this paper, we propose a novel ECG signal enhancement method based on Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) and Higher Order Statistics (HOS). In our scheme, the noisy ECG signal is first decomposed adaptively into oscillatory components called intrinsic mode functions (IMFs) by using Empirical Mode Decomposition (EMD) or its variants. Therefore, the obtained modes are separated into two groups of noisy signal modes and one group of useful signal modes, by using a novel criterion derived from the HOS namely the fourth order cumulant or kurtosis. After that, a modified shrinkage scheme based on Interval Thresholding technique is adaptively applied to each selected IMF from the noise-dominant groups in order to reduce the noise and to preserve the QRS complex. The overall filtered ECG signal is then reconstructed by combining the thresholded IMFs and the retained unprocessed lower frequency relevant IMFs. Various tests and simulations are investigated to evaluate the performance of our proposed approach in combination with the EMD, Ensemble EMD (EEMD) and CEEMDAN algorithms. The simulation results carried on MIT-BIH Arrhythmia database, show that CEEMDAN method gives better performance than the two other methods, and outperforms some state-of-the-art methods in terms of Signal to Noise Ratio (SNR) and Root Mean Square Error (RMSE).

EMD-Based Predictive Deep Belief Network for Time Series Prediction: An Application to Drought Forecasting

Drought is a stochastic natural feature that arises due to intense and persistent shortage of precipitation. Its impact is mostly manifested as agricultural and hydrological droughts following an initial meteorological phenomenon. Drought prediction is essential because it can aid in the preparedness and impact-related management of its effects. This study considers the drought forecasting problem by developing a hybrid predictive model using a denoised empirical mode decomposition (EMD) and a deep belief network (DBN). The proposed method first decomposes the data into several intrinsic mode functions (IMFs) using EMD, and a reconstruction of the original data is obtained by considering only relevant IMFs. Detrended fluctuation analysis (DFA) was applied to each IMF to determine the threshold for robust denoising performance. Based on their scaling exponents, irrelevant intrinsic mode functions are identified and suppressed. The proposed method was applied to predict different time scale drought indices across the Colorado River basin using a standardized streamflow index (SSI) as the drought index. The results obtained using the proposed method was compared with standard methods such as multilayer perceptron (MLP) and support vector regression (SVR). The proposed hybrid model showed improvement in prediction accuracy, especially for multi-step ahead predictions.

Relevant Features Selection for Automatic Prediction of Preterm Deliveries from Pregnancy ElectroHysterograhic (EHG) records.

In this study, we proposed an approach able to predict whether a pregnant woman with contractions would give birth earlier than expected (i.e., before the 37 t h week of gestation (WG)). It only processes non-invasive electrohysterographic (EHG) signals fully automatically without assistance of an expert or an additional medical system. We used term and preterm EHG signals of 30-minutes duration collected between the 27 t h and the 32 n d WG. Preterm deliveries (< 37W G) had occurred in average 4.00 ± 1.88 weeks since recording dates. Each recording contains three bipolar channels. Using the Huang-Hilbert transform (HHT), we obtained up to twelve intrinsic mode functions (IMFs) for each signal. We found that the most relevant IMFs for both term and preterm records were IMF3 and IMF6. From these two IMFs, we extracted 8 most relevant features targeting EHG signal specificities. We investigated features classifications using support vector machine (SVM) for the 3 single-channels and for all their possible combinations. High discrimination power between term and preterm EHG records was obtained with linear-SVM classifiers. For almost all the cases, mean areas under curves (AUC) exceeded 0.92. A two-channel combination (7 features) achieved the best mean results with A c c u r a c y = 95.70%, S e n s i t i v i t y = 98.40%, S p e c i f i c i t y = 93.00% and A U C = 0.95. Results of the three-channel combination (9 features) were A c c u r a c y = 92.30%, S e n s i t i v i t y = 93.00%, S p e c i f i c i t y = 91.60% and A U C = 0.96. The best single-channel (8 features) gave the mean values: A c c u r a c y = 90.40%, S e n s i t i v i t y = 93.60% and A U C = 0.94. Thus, the advantage of our approach is the high diagnostic performance at low computational cost.

Noise-assisted MEMD based relevant IMFs identification and EEG classification

Noise-assisted multivariate empirical mode decomposition (NA-MEMD) is suitable to analyze multichannel electroencephalography (EEG) signals of non-stationarity and non-linearity natures due to the fact that it can provide a highly localized time-frequency representation. For a finite set of multivariate intrinsic mode functions (IMFs) decomposed by NA-MEMD, it still raises the question on how to identify IMFs that contain the information of inertest in an efficient way, and conventional approaches address it by use of prior knowledge. In this work, a novel identification method of relevant IMFs without prior information was proposed based on NA-MEMD and Jensen-Shannon distance (JSD) measure. A criterion of effective factor based on JSD was applied to select significant IMF scales. At each decomposition scale, three kinds of JSDs associated with the effective factor were evaluated: between IMF components from data and themselves, between IMF components from noise and themselves, and between IMF components from data and noise. The efficacy of the proposed method has been demonstrated by both computer simulations and motor imagery EEG data from BCI competition IV datasets.

Relevant modes selection method based on Spearman correlation coefficient for laser signal denoising using empirical mode decomposition

Empirical mode decomposition (EMD) is a recently proposed nonlinear and nonstationary laser signal denoising method. A noisy signal is broken down using EMD into oscillatory components that are called intrinsic mode functions (IMFs). Thresholding-based denoising and correlation-based partial reconstruction of IMFs are the two main research directions for EMD-based denoising. Similar to other decomposition-based denoising approaches, EMD-based denoising methods require a reliable threshold to determine which IMFs are noise components and which IMFs are noise-free components. In this work, we propose a new approach in which each IMF is first denoised using EMD interval thresholding (EMD-IT), and then a robust thresholding process based on Spearman correlation coefficient is used for relevant modes selection. The proposed method tackles the problem using a thresholding-based denoising approach coupled with partial reconstruction of the relevant IMFs. Other traditional denoising methods, including correlation-based EMD partial reconstruction (EMD-Correlation), discrete Fourier transform and wavelet-based methods, are investigated to provide a comparison with the proposed technique. Simulation and test results demonstrate the superior performance of the proposed method when compared with the other methods.