scholarly journals Non-Adaptive Methods for Fetal ECG Signal Processing: A Review and Appraisal

Sensors ◽  
2018 ◽  
Vol 18 (11) ◽  
pp. 3648 ◽  
Author(s):  
Rene Jaros ◽  
Radek Martinek ◽  
Radana Kahankova
Keyword(s):  
Signal Processing ◽  
Wavelet Transforms ◽  
Principal Component ◽  
Fetal Monitoring ◽  
Adaptive Methods ◽  
Component Analysis ◽  
Adaptive Signal Processing ◽  
Extensive Literature ◽  
Fetal Electrocardiogram ◽  
Fetal Ecg

Fetal electrocardiography is among the most promising methods of modern electronic fetal monitoring. However, before they can be fully deployed in the clinical practice as a gold standard, the challenges associated with the signal quality must be solved. During the last two decades, a great amount of articles dealing with improving the quality of the fetal electrocardiogram signal acquired from the abdominal recordings have been introduced. This article aims to present an extensive literature survey of different non-adaptive signal processing methods applied for fetal electrocardiogram extraction and enhancement. It is limiting that a different non-adaptive method works well for each type of signal, but independent component analysis, principal component analysis and wavelet transforms are the most commonly published methods of signal processing and have good accuracy and speed of algorithms.

Atmospheric radar signal processing using principal component analysis

2014 ◽  
Vol 32 ◽  
pp. 79-84 ◽  
Author(s):  
D. Uma Maheswara Rao ◽  
T. Sreenivasulu Reddy ◽  
G. Ramachandra Reddy
Keyword(s):  
Principal Component Analysis ◽  
Signal Processing ◽  
Principal Component ◽  
Component Analysis ◽  
Radar Signal ◽  
Radar Signal Processing

Principal component analysis for functional data

2018 ◽  
Author(s):  
Peter Hall
Keyword(s):  
Principal Component Analysis ◽  
Linear Regression ◽  
Functional Data ◽  
Weighted Least Squares ◽  
Principal Component ◽  
Adaptive Methods ◽  
Component Analysis ◽  
Infinite Dimensional ◽  
Role Of Principal ◽  
Functional Linear Regression

This article discusses the methodology and theory of principal component analysis (PCA) for functional data. It first provides an overview of PCA in the context of finite-dimensional data and infinite-dimensional data, focusing on functional linear regression, before considering the applications of PCA for functional data analysis, principally in cases of dimension reduction. It then describes adaptive methods for prediction and weighted least squares in functional linear regression. It also examines the role of principal components in the assessment of density for functional data, showing how principal component functions are linked to the amount of probability mass contained in a small ball around a given, fixed function, and how this property can be used to define a simple, easily estimable density surrogate. The article concludes by explaining the use of PCA for estimating log-density.

scholarly journals A Null Space-Based Blind Source Separation for Fetal Electrocardiogram Signals

Sensors ◽  
2020 ◽  
Vol 20 (12) ◽  
pp. 3536
Author(s):  
Luay Taha ◽  
Esam Abdel-Raheem
Keyword(s):  
Null Space ◽  
Component Analysis ◽  
Considerable Improvement ◽  
Comb Filter ◽  
Control Logic ◽  
Fetal Electrocardiogram ◽  
Linear Predictor ◽  
Statistical Measures ◽  
Fetal Ecg ◽  
Fast Independent Component Analysis

This paper presents a new non-invasive deterministic algorithm of extracting the fetal Electrocardiogram (FECG) signal based on a new null space idempotent transformation matrix (NSITM). The mixture matrix is used to compute the ITM. Then, the fetal ECG (FECG) and maternal ECG (MECG) signals are extracted from the null space of the ITM. Next, MECG and FECG peaks detection, control logic, and adaptive comb filter are used to remove the unwanted MECG component from the raw FECG signal, thus extracting a clean FECG signal. The visual results from Daisy and Physionet real databases indicate that the proposed algorithm is effective in extracting the FECG signal, which can be compared with principal component analysis (PCA), fast independent component analysis (FastICA), and parallel linear predictor (PLP) filter algorithms. Results from Physionet synthesized ECG data show considerable improvement in extraction performances over other algorithms used in this work, considering different additive signal-to-noise ratio (SNR) increasing from 0 dB to 12 dB, and considering different fetal-to-maternal SNR increasing from −30 dB to 0 dB. The FECG detection of the NSITM is evaluated using statistical measures and results show considerable improvement in the sensitivity (SE), the accuracy (ACC), and the positive predictive value (PPV), as compared with other algorithms. The study demonstrated that the NSITM is a feasible algorithm for FECG extraction.

Independent Subspaces

2011 ◽  
pp. 892-901 ◽  
Author(s):  
Lei Xu
Keyword(s):  
Machine Learning ◽  
Principal Component Analysis ◽  
Signal Processing ◽  
Factor Analysis ◽  
Independent Component Analysis ◽  
General Framework ◽  
Principal Component ◽  
Component Analysis ◽  
The Third ◽  
Complicated Task

Several unsupervised learning topics have been extensively studied with wide applications for decades in the literatures of statistics, signal processing, and machine learning. The topics are mutually related and certain connections have been discussed partly, but still in need of a systematical overview. The article provides a unified perspective via a general framework of independent subspaces, with different topics featured by differences in choosing and combining three ingredients. Moreover, an overview is made via three streams of studies. One consists of those on the widely studied principal component analysis (PCA) and factor analysis (FA), featured by the second order independence. The second consists of studies on a higher order independence featured independent component analysis (ICA), binary FA, and nonGaussian FA. The third is called mixture based learning that combines individual jobs to fulfill a complicated task. Extensive literatures make it impossible to provide a complete review. Instead, we aim at sketching a roadmap for each stream with attentions on those topics missing in the existing surveys and textbooks, and limited to the authors’ knowledge.

scholarly journals Human face recognition methods based on principle component analysis (PCA), wavelet and support vector machine (SVM) : a comparative study

2020 ◽  
Vol 20 (2) ◽  
pp. 991
Author(s):  
Eman A. Gheni ◽  
Zahraa M. Algelal
Keyword(s):  
Face Recognition ◽  
Wavelet Transforms ◽  
Principal Component ◽  
Component Analysis ◽  
Recognition System ◽  
Performance Comparison ◽  
Support Vector ◽  
Human Face ◽  
Human Face Recognition ◽  
Feature Extracting

<p class="JESTECAbstract">Human face Recognition systems are increasingly gaining more importance and can be utilized throughout many applications like video surveillance, Security, human-computer intelligent interaction, etc. this paper presents performance comparison between three feature extraction techniques for an automatic face recognition system. In the first step, we benefit from wavelet Transforms, Principal Component Analysis (PCA) and combining Wavelet with PCA as feature extracting methods. After feature vectors generation, linear and nonlinear Support Vector Machines (SVM) are usually used for implementing the classification or recognition step. These methods are compared on accuracy in an ORL database for face recognition applications including 400 images of 40 people.</p>

scholarly journals Neural Network Implementations for PCA and Its Extensions

2012 ◽  
Vol 2012 ◽  
pp. 1-19 ◽  
Author(s):  
Jialin Qiu ◽  
Hui Wang ◽  
Jiabin Lu ◽  
Biaobiao Zhang ◽  
K.-L. Du
Keyword(s):  
Neural Network ◽  
Minor Component ◽  
Principal Component ◽  
Component Analysis ◽  
Adaptive Signal Processing ◽  
Singular Value ◽  
Eigenvalue Decomposition ◽  
Blind Signal Separation ◽  
Neural Network Models ◽  
Linear Discriminant

Many information processing problems can be transformed into some form of eigenvalue or singular value problems. Eigenvalue decomposition (EVD) and singular value decomposition (SVD) are usually used for solving these problems. In this paper, we give an introduction to various neural network implementations and algorithms for principal component analysis (PCA) and its various extensions. PCA is a statistical method that is directly related to EVD and SVD. Minor component analysis (MCA) is a variant of PCA, which is useful for solving total least squares (TLSs) problems. The algorithms are typical unsupervised learning methods. Some other neural network models for feature extraction, such as localized methods, complex-domain methods, generalized EVD, and SVD, are also described. Topics associated with PCA, such as independent component analysis (ICA) and linear discriminant analysis (LDA), are mentioned in passing in the conclusion. These methods are useful in adaptive signal processing, blind signal separation (BSS), pattern recognition, and information compression.

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