Single Lead ECG Discrimination Between Normal Sinus Rhythm and Sleep Apnea with Intrinsic Mode Function Complexity Index Using Empirical Mode Decomposition

2018 ◽  
Author(s):  
Divaakar Siva Baala Sundaram ◽  
Rogith Balasubramani ◽  
Suganti Shivaram ◽  
Anjani Muthyala ◽  
Shivaram P. Arunachalam
Keyword(s):  
Sleep Apnea ◽  
Sinus Rhythm ◽  
Empirical Mode Decomposition ◽  
Normal Sinus Rhythm ◽  
Intrinsic Mode Function ◽  
Normal Sinus ◽  
Mode Function ◽  
Complexity Index ◽  
Mode Decomposition

Over-Sifting Components Analysis in Bidimensional Empirical Mode Decomposition

2010 ◽  
Vol 159 ◽  
pp. 377-382
Author(s):  
Guang Tao Ge
Keyword(s):  
Empirical Mode Decomposition ◽  
Intrinsic Mode Function ◽  
Mode Function ◽  
Mode Decomposition ◽  
The Mean ◽  
Components Analysis ◽  
Bidimensional Empirical Mode Decomposition

Define the course of getting mean envelope as an operation (mean envelope operation) in Empirical mode decomposition (EMD), so as to express the Intrinsic Mode Function (IMF) with mean envelopes. Summarize several rules of the mean envelope operation. On this fundamental, the abnormal components exist in the over-sifting IMFs are extracted out, and the conclusion is testified with the infinite sifting experiment.

Empirical Mode Decomposition on Geometry Signal Based Spherical Parameterization

2014 ◽  
Vol 998-999 ◽  
pp. 860-863
Author(s):  
Jian Guo Wang ◽  
Qun E ◽  
Ke Ming Yao ◽  
Xin Long Wan
Keyword(s):  
Empirical Mode Decomposition ◽  
Main Idea ◽  
Intrinsic Mode Function ◽  
Mode Function ◽  
Mode Decomposition ◽  
Spherical Parameterization ◽  
Novel Method ◽  
Key Steps

A novel method based onEmpirical Mode Decomposition(EMD) is approached to process the geometry signal. The main idea is to decompose the signal into some different detail components called Intrinsic Mode Function (IMF). The key steps are as follows: First, the signal is spherical parameterization; Second it is transformed into the plane signal and sampled regularly; Third, the translated signal is processed as an image using Bid-Empirical Mode Decomposition, getting several image IMFs; Finally, invert mapping these IMFs to geometry signal and getting the geometry signal’s IMFs.We demonstrate the power of the algorithms through a number of application examples including de-noising and enhancement.

Fault diagnosis of rolling bearing based on empirical mode decomposition and higher order statistics

2014 ◽  
Vol 229 (9) ◽  
pp. 1630-1638 ◽  
Author(s):  
Jian-hua Cai
Keyword(s):  
Power Spectrum ◽  
Order Statistics ◽  
Empirical Mode Decomposition ◽  
Gaussian Noise ◽  
Rolling Bearing ◽  
Higher Order ◽  
Intrinsic Mode Function ◽  
Order Spectrum ◽  
Mode Function ◽  
Mode Decomposition

In order to solve the problem of the faulted rolling bearing signal getting easily affected by Gaussian noise, a new fault diagnosis method was proposed based on empirical mode decomposition and high-order statistics. Firstly, the vibration signal was decomposed by empirical mode decomposition and the correlation coefficient of each intrinsic mode function was calculated. These intrinsic mode function components, which have a big correlation coefficient, were selected to estimate its higher order spectrum. Then based on the higher order statistics theory, this method uses higher order spectrum of each intrinsic mode function to reconstruct its power spectrum. And these power spectrums were summed to obtain the primary power spectrum of bearing signal. Finally, fault feature information was extracted from the reconstructed power spectrum. A model, using higher order spectrum to reconstruct power spectrum, was established. Meanwhile, analysis was conducted by using the simulated data and the recorded vibration signals which include inner race, out race, and bearing ball fault signal. Results show that the presented method is superior to traditional power spectrum method in suppressing Gaussian noise and its resolution is higher. New method can extract more useful information compared to the traditional method.

scholarly journals Feature Extraction of Ship Radiated Noise Based on Permutation Entropy of the Intrinsic Mode Function with the Highest Energy

2016 ◽  
Author(s):  
Yu-Xing Li ◽  
Ya-An Li ◽  
Zhe Chen ◽  
Xiao Chen
Keyword(s):  
Feature Extraction ◽  
Empirical Mode Decomposition ◽  
Characteristic Parameter ◽  
Intrinsic Mode Function ◽  
Permutation Entropy ◽  
Intrinsic Mode Functions ◽  
Characteristic Parameters ◽  
Radiated Noise ◽  
Mode Function ◽  
Mode Decomposition

In order to solve the problem of feature extraction of underwater acoustic signals in complex ocean environment, a new method for feature extraction from ship radiated noise is presented based on empirical mode decomposition theory and permutation entropy. It analyzes the separability for permutation entropies of the intrinsic mode functions of three types of ship radiated noise signals, and discusses the permutation entropy of the intrinsic mode function with the highest energy. In this study, ship radiated noise signals measured from three types of ships are decomposed into a set of intrinsic mode functions with empirical mode decomposition method. Then, the permutation entropies of all intrinsic mode functions are calculated with appropriate parameters. The permutation entropies are obviously different in the intrinsic mode functions with the highest energy, thus, the permutation entropy of the intrinsic mode function with the highest energy is regarded as a new characteristic parameter to extract the feature of ship radiated noise. After that, the characteristic parameters, namely, the energy difference between high and low frequency, permutation entropy, and multi-scale permutation entropy, are compared with the permutation entropy of the intrinsic mode function with the highest energy. It is discovered that the four characteristic parameters are at the same level for similar ships, however, there are differences in the parameters for different types of ships. The results demonstrate that the permutation entropy of the intrinsic mode function with the highest energy is better in separability as the characteristic parameter than the other three parameters by comparing their fluctuation ranges and the average values of the four characteristic parameters. Hence, the feature of ship radiated noise can be extracted efficiently with the method.

Faults Monitoring and Diagnosis of Ball Bearing Based on Hilbert-Huang Transformation

2005 ◽  
Vol 291-292 ◽  
pp. 649-654 ◽  
Author(s):  
H. Li ◽  
H.Q. Zheng ◽  
L.W. Tang
Keyword(s):  
Energy Spectrum ◽  
Machine Tool ◽  
Empirical Mode Decomposition ◽  
Ball Bearing ◽  
Intrinsic Mode Function ◽  
Vibration Signals ◽  
Monitoring And Diagnosis ◽  
Mode Function ◽  
Mode Decomposition ◽  
Faults Diagnosis

A novel scheme for ball bearing faults detection is presented based on Hilbert-Huang transformation and its energy spectrum. The basic method is introduced in detail. The energy spectrum is applied in the research of the faults diagnosis for the ball bearing of machine tool. Firstly, the analyzed vibration signals are separated into a series of intrinsic mode function using the empirical mode decomposition. Then, the energy spectrum is applied to the intrinsic mode function. The experimental results show that this method based on Hilbert-Huang transformation and energy spectrum can effectively diagnosis the faults of the ball bearing.

scholarly journals Gear Fault Diagnosis Based on Empirical Mode Decomposition and 1.5 Dimension Spectrum

2016 ◽  
Vol 2016 ◽  
pp. 1-10 ◽  
Author(s):  
Jianhua Cai ◽  
Xiaoqin Li
Keyword(s):  
Fault Diagnosis ◽  
Power Spectrum ◽  
Correlation Coefficient ◽  
Empirical Mode Decomposition ◽  
Vibration Signal ◽  
Intrinsic Mode Function ◽  
Dimension Spectrum ◽  
Mode Function ◽  
Mode Decomposition ◽  
Gear Fault

Aiming at the nonlinear and nonstationary feature of mechanical fault vibration signal, a new fault diagnosis method, which is based on a combination of empirical mode decomposition (EMD) and 1.5 dimension spectrum, is proposed. Firstly, the vibration signal is decomposed by EMD and the correlation coefficient between each intrinsic mode function and original signal is calculated. Then these intrinsic mode function components, which have a big correlation coefficient, are selected to estimate its 1.5 dimension spectrum. And this method uses 1.5 dimension spectrum of each intrinsic mode function to reconstruct its power spectrum. And these power spectrums are summed to obtain the primary power spectrum of gear fault signal. Finally, the information feature of fault is extracted from the reconstructed 1.5 dimension spectrum. A model to reconstruct 1.5 dimension spectrum is established, and the principle and steps of the method are presented. Some simulated and measured gear fault signals have been processed to demonstrate the effectiveness of new method. The result shows that this method can greatly inhibit the interference of Gauss noise to raise the SNR and recognize the secondary phase coupling feature of the signal. The proposed method has a good real-time performance and provides an effective method to determine the early crack fault of gear root.

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