A Comparison of the Performance of ICA Algorithms for Fetal ECG Extraction using Time Domain Multiple-Sources Interference Data
نویسنده
چکیده
This paper evaluates the performance of some major Independent Component Analysis (ICA) algorithms like Hyv ̈arinen’s fixed point algorithm, Pearson based ICA algorithm and OGWE (Optimized Generalized Weighted Estimator) ICA algorithm in a biomedical blind source separation problem. Independent signals representing Fetal ECG (FECG) and Maternal ECG (MECG) generated and then mixed linearly to simulate a recording of electrocardiogram. While ICA has been used to extract FECG, very little literature is available on its performance in clinical environment. So there is a need to evaluate performance of these algorithms in Biomedical. To quantify the performance of ICA algorithms, different samples values of simulated maternal and fetal ECG investigated. ICA algorithms separation performances are measured by performance index. This paper reports on the performance of the ICA algorithms.
منابع مشابه
A PCA/ICA based Fetal ECG Extraction from Mother Abdominal Recordings by Means of a Novel Data-driven Approach to Fetal ECG Quality Assessment
Background: Fetal electrocardiography is a developing field that provides valuable information on the fetal health during pregnancy. By early diagnosis and treatment of fetal heart problems, more survival chance is given to the infant.Objective: Here, we extract fetal ECG from maternal abdominal recordings and detect R-peaks in order to recognize fetal heart rate. On the next step, we find a be...
متن کاملImproving the Performance of ICA Algorithm for fMRI Simulated Data Analysis Using Temporal and Spatial Filters in the Preprocessing Phase
Introduction: The accuracy of analyzing Functional MRI (fMRI) data is usually decreases in the presence of noise and artifact sources. A common solution in for analyzing fMRI data having high noise is to use suitable preprocessing methods with the aim of data denoising. Some effects of preprocessing methods on the parametric methods such as general linear model (GLM) have previously been evalua...
متن کاملComponent Extraction of Complex Biomedical Signals and Performance analysis
Biomedical signals can arise from one or many sources including heart, brains and endocrine systems. Multiple sources poses challenge to researchers which may have contaminated with artifacts and noise. The Biomedical time series signal like electroencephalogram (EEG), electrocardiogram (ECG), etc. The morphology of the cardiac signal is very important in most of diagnostics based on the ECG. T...
متن کاملINTELLIGENT TECHNIQUE OF CANCELING MATERNAL ECG IN FECG EXTRACTION
In this paper, we propose a technique of artificial intelligence called adaptive neuro fuzzy inference system (ANFIS) for canceling maternal electrocardiogram (MECG) in fetal electrocardiogram extraction (FECG).This technique is used to estimate the MECG present in the abdominal signal of a pregnant woman. The FECG is then extracted by subtracting the estimated MECG from the abdominal signal. P...
متن کاملPerformance Analysis of ICA Algorithms against Multiple-Sources Interference in Biomedical Systems
This paper evaluates the performance of some major ICA algorithms like Cardoso’s Joint Approximate Diagonalization of Eigen matrices (JADE), Bell and Sejnowski’s Infomax algorithm and Comon’s algorithm in a biomedical blind source separation problem. Independent signals representing Fetal ECG (FECG) and Maternal ECG (MECG) generated and then mixed linearly to simulate a recording of electrocard...
متن کامل