Independent component analysis-based artefact reduction: application to the electrocardiogram for improved magnetic resonance imaging triggering (original) (raw)
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Journal of Electrocardiology, 2006
The electrocardiogram (ECG) used for patient monitoring during magnetic resonance imaging (MRI) unfortunately suffers from severe artefacts. These artefacts are due to the special environment of the MRI. Modeling helped in finding solutions for the suppression of these artefacts superimposed on the ECG signal. After we validated the linear and time invariant model for the magnetic field gradient artefact generation, we applied offline and online filters for their suppression. Wiener filtering (offline) helped in generating reference annotations of the ECG beats. In online filtering, the least-mean-square filter suppressed the magnetic field gradient artefacts before the acquired ECG signal was input to the arrhythmia algorithm. Comparing the results of two runs (one run using online filtering and one run without) to our reference annotations, we found an eminent improvement in the arrhythmia module's performance, enabling reliable patient monitoring and MRI synchronization based on the ECG signal. D
Application of independent component analysis in removing artefacts from the electrocardiogram
Neural Computing & Applications, 2006
Routinely recorded electrocardiograms (ECGs) are often corrupted by different types of artefacts and many efforts have been made to enhance their quality by reducing the noise or artefacts. This paper addresses the problem of removing noise and artefacts from ECGs using independent component analysis (ICA). An ICA algorithm is tested on three-channel ECG recordings taken from human subjects, mostly in the coronary care unit. Results are presented that show that ICA can detect and remove a variety of noise and ...
RETRACTED: Artifacts and noise removal in electrocardiograms using independent component analysis
International Journal of Cardiology, 2008
Independent component analysis (ICA) is a novel technique capable of separating independent components from electrocardiogram (ECG) complex signals. The purpose of this analysis is to evaluate the effectiveness of ICA in removing artifacts and noise from ECG recordings. ICA is applied to remove artifacts and noise in ECG segments of either an individual ECG CSE data base file or all files. The reconstructed ECGs are compared with the original ECG signal. For the four special cases discussed, the R-Peak magnitudes of the CSE data base ECG waveforms before and after applying ICA are also found. In the results, it is shown that in most of the cases, the percentage error in reconstruction is very small. The results show that there is a significant improvement in signal quality, i.e. SNR. All the ECG recording cases dealt showed an improved ECG appearance after the use of ICA. This establishes the efficacy of ICA in elimination of noise and artifacts in electrocardiograms.
Removing artifacts from electrocardiographic signals using independent components analysis
Neurocomputing, 1998
In this work, we deal with the elimination of artifacts (electrodes, muscle, respiration, etc.) from the electrocardiographic (ECG) signal. We use a new tool called independent component analysis (ICA) that blindly separates mixed statistically independent signals. ICA can separate the signal from the interference, even if both overlap in frequency. In order to estimate the mixing parameters in real time, we propose a self-adaptive step-size, derived from the study of the averaged behavior of those parameters, and a two-layers neural network. Simulations were carried out to show the performance of the algorithm using a standard ECG database.
Independent Component Analysis for Motion Artifacts Removal from Electrocardiogram
A method of using Independent Component Analysis to remove motion induced artifacts in the signals picked up by ECG electrodes is developed in this paper. In a first aid setting, ECG electrodes on patients cannot always keep stationary, resulting in a large amount of contact noise in acquired signals. Similar problems occur in ECGs in motion, e.g. sports and ambulatory ECGs. The motion induced artifacts are known to undermine the arrhythmia recognition. An artificial neural system for automated ECG classification with an extra independent component analysis de-noising pre-processor is proposed and validated by pre-recorded real ECG and noise datasets. The proposed system shows improved recognition accuracy, providing a useful means to more accurately detect arrhythmia from ECGs in the presence of no trivial motion related noises.
2017
Visual inspection of waveforms on paper or screen by experts can hardly meet the needs of ambulatory, long term monitoring and sports electrocardiograms (ECGs). Automated systems deploying signal processing and pattern recognitions are sought. Arrhythmias identification represents an important branch of ECG pattern recognition. Motion induced artifacts are known to be a major cause of mis-recognition and misdiagnosis. This paper proposes the use of independent component analysis (ICA) to enhance multi-channel ECG signals. Using the MIT dataset with 8 ECG conditions, cross validation shows significant improvement when processing noisy data. The improvement is signal to noise ratio related: the lower the signal to noise ratio the more significant improvement has been observed. Significant improvements in classification sensitivities and specificity are achievable in the presence of non-trivial motion related noise.
Journal of Cardiovascular Magnetic Resonance, 2013
Background: In Cardiovascular Magnetic Resonance (CMR), the synchronization of image acquisition with heart motion is performed in clinical practice by processing the electrocardiogram (ECG). The ECG-based synchronization is well established for MR scanners with magnetic fields up to 3 T. However, this technique is prone to errors in ultra high field environments, e.g. in 7 T MR scanners as used in research applications. The high magnetic fields cause severe magnetohydrodynamic (MHD) effects which disturb the ECG signal. Image synchronization is thus less reliable and yields artefacts in CMR images. Methods: A strategy based on Independent Component Analysis (ICA) was pursued in this work to enhance the ECG contribution and attenuate the MHD effect. ICA was applied to 12-lead ECG signals recorded inside a 7 T MR scanner. An automatic source identification procedure was proposed to identify an independent component (IC) dominated by the ECG signal. The identified IC was then used for detecting the R-peaks. The presented ICA-based method was compared to other R-peak detection methods using 1) the raw ECG signal, 2) the raw vectorcardiogram (VCG), 3) the state-of-the-art gating technique based on the VCG, 4) an updated version of the VCG-based approach and 5) the ICA of the VCG. Results: ECG signals from eight volunteers were recorded inside the MR scanner. Recordings with an overall length of 87 min accounting for 5457 QRS complexes were available for the analysis. The records were divided into a training and a test dataset. In terms of R-peak detection within the test dataset, the proposed ICA-based algorithm achieved a detection performance with an average sensitivity (Se) of 99.2%, a positive predictive value (+P) of 99.1%, with an average trigger delay and jitter of 5.8 ms and 5.0 ms, respectively. Long term stability of the demixing matrix was shown based on two measurements of the same subject, each being separated by one year, whereas an averaged detection performance of Se = 99.4% and +P = 99.7% was achieved. Compared to the state-of-the-art VCG-based gating technique at 7 T, the proposed method increased the sensitivity and positive predictive value within the test dataset by 27.1% and 42.7%, respectively. Conclusions: The presented ICA-based method allows the estimation and identification of an IC dominated by the ECG signal. R-peak detection based on this IC outperforms the state-of-the-art VCG-based technique in a 7 T MR scanner environment.
Application of ICA in Removing Artefacts from the ECG
Neural Computing and Applications
Routinely recorded electrocardiograms (ECGs) are often corrupted by different types of artefacts and many efforts have been made to enhance their quality by reducing the noise or artefacts. This paper addresses the problem of removing noise and artefacts from ECGs using Independent Component Analysis (ICA). An ICA algorithm is tested on 3−channel ECG recordings taken from human subjects, mostly in the Coronary Care Unit (CCU). Results are presented that show that ICA can detect and remove a variety of noise and artefact sources in these ECGs. One difficulty with the application of ICA is the determination of the order of the independent components. A new technique based on simple statistical parameters is proposed to solve this problem in this application. The developed technique is successfully applied to the ECG data and offers potential for online processing of ECG using ICA.
Role of independent component analysis in intelligent ECG signal processing
2014
The Electrocardiogram (ECG) reflects the activities and the attributes of the human heart and reveals very important hidden information in its structure. The information is extracted by means of ECG signal analysis to gain insights that are very crucial in explaining and identifying various pathological conditions. The feature extraction process can be accomplished directly by an expert through, visual inspection of ECGs printed on paper or displayed on a screen. However, the complexity and the time taken for the ECG signals to be visually inspected and manually analysed means that it‟s a very tedious task thus yielding limited descriptions. In addition, a manual ECG analysis is always prone to errors: human oversights. Moreover ECG signal processing has become a prevalent and effective tool for research and clinical practices. A typical computer based ECG analysis system includes a signal preprocessing, beats detection and feature extraction stages, followed by classification. Auto...