A novel approach to the extraction of fetal electrocardiogram based on empirical mode decomposition and correlation analysis

2017 ◽  
Vol 40 (3) ◽  
pp. 565-574 ◽  
Author(s):  
Peyman Ghobadi Azbari ◽  
Mostafa Abdolghaffar ◽  
Saeed Mohaqeqi ◽  
Mohammad Pooyan ◽  
Alireza Ahmadian ◽  
...  
Keyword(s):  
Correlation Analysis ◽  
Empirical Mode Decomposition ◽  
Fetal Electrocardiogram ◽  
Novel Approach ◽  
Mode Decomposition

B-SPLINE ANALYTICAL REPRESENTATION OF THE MEAN ENVELOPE FOR EMPIRICAL MODE DECOMPOSITION

2010 ◽  
Vol 08 (02) ◽  
pp. 175-195 ◽  
Author(s):  
TIANXIANG ZHENG ◽  
LIHUA YANG
Keyword(s):  
Empirical Mode Decomposition ◽  
Basic Concept ◽  
Spline Functions ◽  
Mathematical Foundation ◽  
Knot Insertion ◽  
B Spline ◽  
Explicit Formulation ◽  
Novel Approach ◽  
Mode Decomposition ◽  
The Mean

This paper investigates how the mean envelope, the subtrahend in the sifting procedure for the Empirical Mode Decomposition (EMD) algorithm, represents as an expansion in terms of basis. To this end, a novel approach that gives an alternative analytical expression using B-spline functions is presented. The basic concept lies mainly on the idea that B-spline functions form a basis for the space of splines and have refined-node representations by knot insertion. This newly-developed expression is essentially equivalent to the conventional one, but gives a more explicit formulation on this issue. For the purpose of establishing the mathematical foundation of the EMD methodology, this study may afford a favorable opportunity in this direction.

Minimizing the trend effect on detrended cross-correlation analysis with empirical mode decomposition

2012 ◽  
Vol 45 (2) ◽  
pp. 166-173 ◽  
Author(s):  
Xiaojun Zhao ◽  
Pengjian Shang ◽  
Chuang Zhao ◽  
Jing Wang ◽  
Rui Tao
Keyword(s):  
Correlation Analysis ◽  
Empirical Mode Decomposition ◽  
Cross Correlation ◽  
Mode Decomposition ◽  
Cross Correlation Analysis

scholarly journals ECG Denoising Using Artificial Neural Networks and Complete Ensemble Empirical Mode Decomposition

2021 ◽  
Vol 12 (2) ◽  
pp. 2382-2389
Author(s):  
Rajesh Birok, Et. al.
Keyword(s):  
Neural Networks ◽  
Empirical Mode Decomposition ◽  
State Of The Art ◽  
Signal To Noise Ratio ◽  
Low Frequency ◽  
Ensemble Empirical Mode Decomposition ◽  
Novel Approach ◽  
Mode Decomposition ◽  
The Neural Network ◽  
Art Methods

Electrocardiogram (ECG) is a documentation of the electrical activities of the heart. It is used to identify a number of cardiac faults such as arrhythmias, AF etc.  Quite often the ECG gets corrupted by various kinds of artifacts, thus in order to gain correct information from them, they must first be denoised. This paper presents a novel approach for the filtering of low frequency artifacts of ECG signals by using Complete Ensemble Empirical Mode Decomposition (CEED) and Neural Networks, which removes most of the constituent noise while assuring no loss of information in terms of the morphology of the ECG signal. The contribution of the method lies in the fact that it combines the advantages of both EEMD and ANN. The use of CEEMD ensures that the Neural Network does not get over fitted. It also significantly helps in building better predictors at individual frequency levels. The proposed method is compared with other state-of-the-art methods in terms of Mean Square Error (MSE), Signal to Noise Ratio (SNR) and Correlation Coefficient. The results show that the proposed method has better performance as compared to other state-of-the-art methods for low frequency artifacts removal from EEG.  

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