Empirical Mode Decomposition and Analysis of Non-Stationary Cardiac Signals

Dominant Frequency ◽

Accurate Estimate ◽

Linear Discriminant ◽

Robust Detection ◽

Detection Techniques ◽

T Wave ◽

Ambulatory Ecg ◽

Mode Decomposition

Each year 400,000 North Americans die from sudden cardiac death (SCD). T- wave alternans (TWA) refers to an alternating pattern in the T-wave portion of the surface electrocardiogram (ECG) and has been shown as a risk stratifier for SCD. These subtle changes in the T-waves are in the micro-volt scale and ambulatory ECG recordings usually contain biological noise. Also, data non-stationarity owing to heart rate variability and the amplitude variability in TWA magnitude can limit the accuracy of the detection techniques. This necessitates the need for robust detection algorithms for processing such non-stationary data. In this thesis, we have proposed an Empirical Mode Decomposition (EMD) based scheme combined with the Instantaneous Frequency (IF). EMD decomposes the signal into several monocomponent signals called Intrinsic Mode Functions (IMFs). IF extracted from these IMFs provides an accurate estimate of time varying frequency components and hence can aid during characterization of TWAs. In order to validate the performance of the proposed detection technique, the feature vectors extracted from the IMFs were fed to a linear discriminant analysis (LDA) classifier. The performance assessment was carried out using two datasets: (a) Synthetic TWAs: 72 signals obtained from publicly accessible Physionet database and (b) TWAs from patients: 55 ambulatory ECG signals obtained from the Toronto General Hospital. Using an unbiased leave-one-out cross validation strategy, maximum overall classification accuracies of 86.1% and 81.8% were achieved for TWA detection from synthetic and ambulatory ECG recordings respectively. In addition, the usability of the proposed technique has been investigated to assess its suitability for addressing another cardiovascular problem stroke. Atrial Fibrillation (AF) has been identified as a risk factor to increase the chances of stroke. The most common method in studying the complex AF electrograms is to employ dominant frequency (DF) analysis; however, due to signal non-stationarity DF does not always provide the best estimate of the atrial activation rate. As a result, analyzing the electrograms via EMD and IF has been investigated as the second contribution of this work.

Empirical Mode Decomposition and Analysis of Non-Stationary Cardiac Signals

10.32920/ryerson.14664297 ◽

2021 ◽

Author(s):

Nastaran Rahnama

Keyword(s):

Dominant Frequency ◽

Accurate Estimate ◽

Linear Discriminant ◽

Robust Detection ◽

Detection Techniques ◽

T Wave ◽

Ambulatory Ecg ◽

Mode Decomposition

Proceedings of the Institution of Mechanical Engineers Part C Journal of Mechanical Engineering Science ◽

Adaptive variational mode decomposition based on artificial fish swarm algorithm for fault diagnosis of rolling bearings

10.1177/0954406215623311 ◽

2016 ◽

Vol 231 (4) ◽

pp. 635-654 ◽

Cited By ~ 25

Author(s):

Jun Zhu ◽

Chao Wang ◽

Zhiyong Hu ◽

Fanrang Kong ◽

Xingchen Liu

Keyword(s):

Fault Diagnosis ◽

Variational Mode Decomposition ◽

Rolling Bearings ◽

Artificial Fish Swarm Algorithm ◽

Detection Techniques ◽

Mode Decomposition ◽

Artificial Fish Swarm ◽

Swarm Algorithm

The bearing fault diagnosis is of vital significance in maintaining the safety of rotation machine. Among various fault detection techniques, the diagnosis based on vibration signal is widely applied in monitoring the condition of rotation machine. Variational mode decomposition (VMD) is a novel signal analysis method, which can decompose a multi-component signal into a certain number of band-limited intrinsic mode functions (BLIMFs) nonrecursively. VMD could overcome some problems such as mode mixing, the inference of noise, the determination of wavelet base, which exist in empirical mode decomposition, ensemble empirical mode decomposition, wavelet transform, respectively. However, the empirical selection of the parameters for VMD would affect the result of the decomposition. This paper presents an adaptive VMD method with parameter optimization for detecting the localized faults of rolling bearing. Kurtosis, sensitive to transient impulsive components, is employed as optimization index to evaluate the performance of the VMD. Two parameters in the VMD, namely the number of decomposition modes and data-fidelity constraint, are optimized synchronously based on the kurtosis index through artificial fish swarm algorithm. Executing VMD with the acquired parameters, the optimal BLIMF is obtained. The spectrum analysis of the optimal BLIMF could identify the characteristic frequency caused by the localized crack effectually. The validity of the proposed method is proved by means of a cyclic transient impulse response signal and two experiments with practical vibration signals of rolling bearings. Compared to several existing methods, the proposed method demonstrates reinforced results.

Proceedings of the Institution of Mechanical Engineers Part C Journal of Mechanical Engineering Science ◽

A fast online bandwidth empirical mode decomposition scheme for avoidance of the mode mixing problem

10.1177/0954406217741515 ◽

2017 ◽

Vol 232 (20) ◽

pp. 3652-3674 ◽

Cited By ~ 1

Author(s):

SH Momeni Massouleh ◽

Seyed Ali Hosseini Kordkheili ◽

H Mohammad Navazi

Keyword(s):

Low Frequency ◽

Dominant Frequency ◽

Auxiliary Function ◽

Online Data ◽

Decomposition Scheme ◽

Mode Decomposition ◽

Mixing Problem ◽

Mode Mixing

The main objective of this work is to propose a scheme to extract intrinsic mode functions of online data with an acceptable speed as well as accuracy. For this purpose, an individual block framework method is firstly employed to extract the intrinsic mode functions. In this method, buffers are selected such that they overlap with their neighbors to prevent the end effect errors with no need for the averaging process. And in order to avoid the mode mixing problem, a bandwidth empirical mode decomposition scheme is developed to effectively improve the results. Through this scheme, an auxiliary function made of both high- and low-frequency components corresponding to noise and dominant frequency is added to data for the strengthening of the components for the better extraction of intrinsic mode functions during sifting process. An index criterion as well as a threshold limit is also introduced to separate high- and low-frequency parts of data at desired frequency range. Advantages of the proposed scheme are assessed and comparisons with the available methods are presented. Solution of different types of examples and experimentally generated data for two faulty ball bearings reveals that the present easily implemented scheme achieves results with lower computational efforts and accuracy.

A Comparative Study of the Empirical Mode Decomposition and Wavelet Analysis on Their Application for Structural Health Monitoring

Design Engineering ◽

10.1115/imece2004-59832 ◽

2004 ◽

Cited By ~ 2

Author(s):

Adriana Hera ◽

Abhijeet Shinde ◽

Zhikun Hou

Keyword(s):

Comparative Study ◽

Wavelet Packet ◽

Noisy Data ◽

Vibration Signal ◽

Mode Shapes ◽

Progressive Damage ◽

Detection Techniques ◽

Mode Decomposition

The paper presents a comparative study of the effectiveness of three novel damage detection techniques namely Continuous Wavelet Transform (CWT), Empirical Mode Decomposition (EMD) and Wavelet Packet Sifting (WPS). The health condition of a mechanical or civil engineering structure can be assessed by monitoring a change in natural frequencies and mode shapes. CWT method can be used to identify the instantaneous values of these modal parameters by the wavelet ridges. Using the EMD method, intrinsic mode functions (IMF) can be sifted from a vibration signal, whereas a newly-developed WPS technique can decompose a signal into its dominant mono-frequency components. Instantaneous modal information can be extracted by incorporating the EMD and WPS with the Hilbert Transform. These techniques are illustrated for simulated vibration data from a three-degree-of-freedom system subjected to (i) sudden damage and (ii) progressive damage. The aspects related to the implementation algorithms, sensitivity to damage type and the robustness issues in case of noisy data are discussed. In case of progressive damage, all methods performed well. WPS technique performed better in case of sudden damage whereas CWT demonstrated robustness in case of noisy data.

Proceedings of the Institution of Mechanical Engineers Part C Journal of Mechanical Engineering Science ◽

Multifractal characterization of mechanical vibration signals through improved empirical mode decomposition-based detrended fluctuation analysis

10.1177/0954406216664547 ◽

2016 ◽

Vol 231 (22) ◽

pp. 4139-4149 ◽

Cited By ~ 1

Author(s):

Du Wenliao ◽

Guo Zhiqiang ◽

Gong Xiaoyun ◽

Xie Guizhong ◽

Wang Liangwen ◽

...

Keyword(s):

Detrended Fluctuation Analysis ◽

Fluctuation Analysis ◽

Vibration Signals ◽

Multifractal Detrended Fluctuation Analysis ◽

Mode Decomposition ◽

Kolmogorov Smirnov ◽

Detrended Fluctuation ◽

Smirnov Test

A novel multifractal detrended fluctuation analysis based on improved empirical mode decomposition for the non-linear and non-stationary vibration signal of machinery is proposed. As the intrinsic mode functions selection and Kolmogorov–Smirnov test are utilized in the detrending procedure, the present approach is quite available for contaminated data sets. The intrinsic mode functions selection is employed to deal with the undesired intrinsic mode functions named pseudocomponents, and the two-sample Kolmogorov–Smirnov test works on each intrinsic mode function and Gaussian noise to detect the noise-like intrinsic mode functions. The proposed method is adaptive to the signal and weakens the effect of noise, which makes this approach work well for vibration signals collected from poor working conditions. We assess the performance of the proposed procedure through the classic multiplicative cascading process. For the pure simulation signal, our results agree with the theoretical results, and for the contaminated time series, the proposed method outperforms the traditional multifractal detrended fluctuation analysis methods. In addition, we analyze the vibration signals of rolling bearing with different fault types, and the presence of multifractality is confirmed.

Classification of sleep apnea using EMD-based features and PSO-trained neural networks

Biomedical Engineering / Biomedizinische Technik ◽

10.1515/bmt-2021-0025 ◽

2021 ◽

Vol 0 (0) ◽

Author(s):

Sajjad Afrakhteh ◽

Ahmad Ayatollahi ◽

Fatemeh Soltani

Keyword(s):

Neural Networks ◽

Sleep Apnea ◽

Back Propagation ◽

Pso Algorithm ◽

Bp Algorithm ◽

Support Vector ◽

Linear Discriminant ◽

Mode Decomposition ◽

Obstructive Sleep

Abstract In this study, we propose a method for detecting obstructive sleep apnea (OSA) based on the features extracted from empirical mode decomposition (EMD) and the neural networks trained by particle swarm optimization (PSO) in the classification phase. After extracting the features from the intrinsic mode functions (IMF) of each heart rate variability (HRV) signal of each segment, these features were applied to the input of popular classifiers such as multi-layer perceptron neural networks (MLPNN), Naïve Bayes, linear discriminant analysis (LDA), k-nearest neighborhood (KNN), and support vector machines (SVM) were applied. The results show that the MLPNN learned with back propagation (BP) algorithm has a diagnostic accuracy of less than 90%, and this may be due to being derivative based property of the BP algorithm, which causes trapping in the local minima. For Improving MLPNN’s performance, we used the PSO algorithm instead of the BP method in training part. Therefore, the MLPNN’s accuracy improved from 89.36 to 97.66% after the application of the PSO algorithm. The proposed method has also reached to 97.78 and 97.96% in sensitivity and specificity, respectively. So, it can be concluded that the proposed method achieves better or comparable results when compared with the previous works in this field.

Ensemble Empirical Mode Decomposition Based Deep Learning Model for Short-Term Wind Power Forecasting

10.21741/9781644901731-8 ◽

2022 ◽

Author(s):

J.M. González-Sopeña

Keyword(s):

Deep Learning ◽

Wind Power ◽

Time Series Data ◽

Ensemble Empirical Mode Decomposition ◽

Series Data ◽

Wind Power Forecasting ◽

Mode Decomposition ◽

Power Forecasting

Abstract. In the last few years, wind power forecasting has established itself as an essential tool in the energy industry due to the increase of wind power penetration in the electric grid. This paper presents a wind power forecasting method based on ensemble empirical mode decomposition (EEMD) and deep learning. EEMD is employed to decompose wind power time series data into several intrinsic mode functions and a residual component. Afterwards, every intrinsic mode function is trained by means of a CNN-LSTM architecture. Finally, wind power forecast is obtained by adding the prediction of every component. Compared to the benchmark model, the proposed approach provides more accurate predictions for several time horizons. Furthermore, prediction intervals are modelled using quantile regression.

Empirical Mode Decomposition in Discrete Time Signals Denoising

Acta Marisiensis. Seria Technologica ◽

10.2478/amset-2019-0002 ◽

2019 ◽

Vol 16 (1) ◽

pp. 10-13 ◽

Cited By ~ 1

Author(s):

Zoltán Germán-Salló

Keyword(s):

Signal To Noise Ratio ◽

Main Idea ◽

Discrete Wavelet ◽

Nonlinear Signal Processing ◽

Mode Decomposition ◽

Time Signals ◽

Nonlinear Signal ◽

Filtering Procedure

Abstract This study explores the data-driven properties of the empirical mode decomposition (EMD) for signal denoising. EMD is an acknowledged procedure which has been widely used for non-stationary and nonlinear signal processing. The main idea of the EMD method is to decompose the analyzed signal into components without using expansion functions. This is a signal dependent representation and provides intrinsic mode functions (IMFs) as components. These are analyzed, through their Hurst exponent and if they are found being noisy components they will be partially or integrally eliminated. This study presents an EMD decomposition-based filtering procedure applied to test signals, the results are evaluated through signal to noise ratio (SNR) and mean square error (MSE). The obtained results are compared with discrete wavelet transform based filtering results.

Texture Feature Extraction Method for Ground Nephogram Based on Hilbert Spectrum of Bidimensional Empirical Mode Decomposition

Journal of Atmospheric and Oceanic Technology ◽

10.1175/jtech-d-13-00238.1 ◽

2014 ◽

Vol 31 (9) ◽

pp. 1982-1994 ◽

Cited By ~ 2

Author(s):

Xiaoying Chen ◽

Aiguo Song ◽

Jianqing Li ◽

Yimin Zhu ◽

Xuejin Sun ◽

...

Keyword(s):

Recognition Rate ◽

Texture Feature ◽

Bidimensional Empirical Mode Decomposition

Hilbert Spectrum ◽

Feature Extraction Method ◽

Hilbert Huang Transform ◽

Mode Decomposition ◽

Marginal Spectrum ◽

Abstract It is important to recognize the type of cloud for automatic observation by ground nephoscope. Although cloud shapes are protean, cloud textures are relatively stable and contain rich information. In this paper, a novel method is presented to extract the nephogram feature from the Hilbert spectrum of cloud images using bidimensional empirical mode decomposition (BEMD). Cloud images are first decomposed into several intrinsic mode functions (IMFs) of textural features through BEMD. The IMFs are converted from two- to one-dimensional format, and then the Hilbert–Huang transform is performed to obtain the Hilbert spectrum and the Hilbert marginal spectrum. It is shown that the Hilbert spectrum and the Hilbert marginal spectrum of different types of cloud textural images can be divided into three different frequency bands. A recognition rate of 87.5%–96.97% is achieved through random cloud image testing using this algorithm, indicating the efficiency of the proposed method for cloud nephogram.

Spatiotemporal Trend and Variability of Precipitation in Taiwan Based on Multi-dimensional Ensemble Empirical Mode Decomposition (MEEMD)

10.5194/egusphere-egu21-10609 ◽

2021 ◽

Author(s):

Chun-Hsiang Tang ◽

Christina W. Tsai

Keyword(s):

Time Series ◽

Ensemble Empirical Mode Decomposition ◽

Short Term ◽

Time Frequency ◽

Spatial Locality ◽

Temperature And Precipitation ◽

Mode Decomposition ◽

Climatic Scenarios

AbstractMost of the time series in nature are nonlinear and nonstationary affected by climate change particularly. It is inevitable that Taiwan has also experienced frequent drought events in recent years. However, drought events are natural disasters with no clear warnings and their influences are cumulative. The difficulty of detecting and analyzing the drought phenomenon remains. To deal with the above-mentioned problem, Multi-dimensional Ensemble Empirical Mode Decomposition (MEEMD) is introduced to analyze the temperature and rainfall data from 1975~2018 in this study, which is a powerful method developed for the time-frequency analysis of nonlinear, nonstationary time series. This method can not only analyze the spatial locality and temporal locality of signals but also decompose the multiple-dimensional time series into several Intrinsic Mode Functions (IMFs). By the set of IMFs, the meaningful instantaneous frequency and the trend of the signals can be observed. Considering stochastic and deterministic influences, to enhance the accuracy this study also reconstruct IMFs into two components, stochastic and deterministic, by the coefficient of auto-correlation.In this study, the influences of temperature and precipitation on the drought events will be discussed. Furthermore, to decrease the significant impact of drought events, this study also attempts to forecast the occurrences of drought events in the short-term via the Artificial Neural Network technique. And, based on the CMIP5 model, this study also investigates the trend and variability of drought events and warming in different climatic scenarios.&#160;Keywords: Multi-dimensional Ensemble Empirical Mode Decomposition (MEEMD), Intrinsic Mode Function(IMF), Drought