Clean Electrocardiogram Signal Research Articles

ECG Signal In-Band Noise De-Noising Base on EMD

The electrocardiogram (ECG) signal is widely used for diagnosis of heart disorders. However, ECG signal is a kind of weak signal to be interfered with heavy background interferences. Moreover, there are some overlaps between the interference frequency sub-bands and the ECG frequency sub-bands, so it is difficult to inhibit noise in the ECG signal. In this paper, the ECG signal in-band noise de-noising method based on empirical mode decomposition (EMD) is proposed. This method uses random permutation to process intrinsic mode functions (IMFs). It abstracts QRS complexes to separate them from noise so that the clean ECG signal is obtained. The method is validated through simulations on the MIT-BIH Arrhythmia Database and experiments on the measured test data. The results indicate that the proposed method can restrain noise, improve signal noise ratio (SNR) and reduce mean squared error (MSE) effectively.

The baseline wandering noise removal from ECG signal using forward–backward Riemann Liouville fractional integral-based empirical wavelet transform approach

The electrocardiogram (ECG) non-invasively monitors the electrical activities of the heart to diagnose the heart-related diseases. The baseline wandering noise affects the diagnosis of the heart diseases. In this paper, the baseline wandering noise removal is done using forward–backward Riemann Liouville (RL) fractional integral-based empirical wavelet transform (EWT) approach. In the designed methodology, firstly, the noisy ECG signal is decomposed into various modes from low to high frequencies. Then, the first mode is processed to remove the baseline wandering noise. The processed EWT mode is filtered by the fractional RL filter used in the forward direction and then in the backward direction for removing the baseline wandering noise from the ECG signal. After that, the processed and the unprocessed modes are used to reconstruct the denoised ECG signal. The clean ECG signal record is taken from MIT-BIH ECG-ID database, and the baseline wandering noise record is taken from the MIT-BIH noise stress test database. The performance of the proposed approach is validated in terms of the output signal-to-noise ratio (SNR[Formula: see text]). The comparative study has also been done between the proposed denoising approach and the existing state-of-the-art denoising algorithms. The experimental result proves the supremacy of our proposed denoising approach.

An Effective Optimization Scheme for ECG Signal Denoising via Low-Rank Matrix Decomposition

Electrocardiogram (ECG) signal denoising is an important preprocessing for ECG signal analysis. The contaminated ECG signal can be considered as the combination of the desired clean signal and the noise. Thus, ECG signal denoising can be considered as a problem of obtaining an optimal solution to the desired clean signal. In this paper, an effective optimization scheme for ECG signal denoising is presented based on low-rank matrix decomposition. First, the ECG denoising problem is formulated as low-rank matrix decomposition. So, an ECG beats matrix is assumed to be the combination of a sparse noise matrix and a low-rank matrix. Considering the repeatability of ECG signal, the rank of the ECG beats matrix is assumed to be one. Then, to fully exploit the low-rank property of the ECG signal, the matrix decomposition is modified by means of adding different weights to different singular values. Finally, the desired clean ECG signal is reconstructed by the low-rank component. The experimental results show that the proposed denoising method achieves the best performance of suppressing the electromyographic noise in the ECG signals compared with other optimization models.

An Improved FastICA Method for Fetal ECG Extraction.

Objective The fast fixed-point algorithm for independent component analysis (FastICA) has been widely used in fetal electrocardiogram (ECG) extraction. However, the FastICA algorithm is sensitive to the initial weight vector, which affects the convergence of the algorithm. In order to solve this problem, an improved FastICA method was proposed to extract fetal ECG. Methods First, the maternal abdominal mixed signal was centralized and whitened, and the overrelaxation factor was incorporated into Newton's iterative algorithm to process the initial weight vector randomly generated. The improved FastICA algorithm was used to separate the source components, selected the best maternal ECG from the separated source components, and detected the R-wave location of the maternal ECG. Finally, the maternal ECG component in each channel was removed by the singular value decomposition (SVD) method to obtain a clean fetal ECG signal. Results An annotated clinical fetal ECG database was used to evaluate the improved algorithm and the conventional FastICA algorithm. The average number of iterations of the algorithm was reduced from 35 before the improvement to 13. Correspondingly, the average running time was reduced from 1.25 s to 1.04 s when using the improved algorithm. The signal-to-noise ratio (SNR) based on eigenvalues of the improved algorithm was 1.55, as compared to 0.99 of the conventional FastICA algorithm. The SNR based on cross-correlation coefficients of the conventional algorithm was also improved from 0.59 to 2.02. The sensitivity, positive predictive accuracy, and harmonic mean (F1) of the improved method were 99.37%, 99.00%, and 99.19%, respectively, while these metrics of the conventional FastICA method were 99.03%, 98.53%, and 98.78%, respectively. Conclusions The proposed improved FastICA algorithm based on the overrelaxation factor, while maintaining the rate of convergence, relaxes the requirement of initial weight vector, avoids the unbalanced convergence, reduces the number of iterations, and improves the convergence performance.

Denoising of Electrocardiogram (ECG) signal by using empirical mode decomposition (EMD) with non-local mean (NLM) technique

In this paper, the investigation on effectiveness of the empirical mode decomposition (EMD) with non-local mean (NLM) technique by using the value of differential standard deviation for denoising of ECG signal is performed. Differential standard deviation is calculated for collecting information related to the input noise so that appropriate formation in EMD and NLM framework can be performed. EMD framework in the proposed methodology is used for reduction of the noise from the ECG signal. The output of the EMD passes through NLM framework for preservation of the edges and cancel the noise present in the ECG signal after the EMD process. The performance of the proposed methodology has been validated by using added white and color Gaussian noise to the clean ECG signal from MIT-BIH arrhythmia database at different signal to noise ratio (SNR). The proposed denoising technique shows lesser mean of percent root mean square difference (PRD), mean square error (MSE), and better mean SNR improvement compared to other well-known methods at different input SNR. The proposed methodology also shows lesser standard deviation PRD, MSE, and SNR improvement compared to other well-known methods at different input SNR.

ECG Signal De-noising and Baseline Wander Correction Based on CEEMDAN and Wavelet Threshold.

A novel electrocardiogram (ECG) signal de-noising and baseline wander correction method based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and wavelet threshold is proposed. Although CEEMDAN is based on empirical mode decomposition (EMD), it represents a significant improvement of the original EMD by overcoming the mode-mixing problem. However, there has been no previous study on using CEEMDAN to de-noise ECG signals, to the authors’ best knowledge. In the proposed method, the original noisy ECG signal is decomposed into a series of intrinsic mode functions (IMFs) sorted from high to low frequency by CEEMDAN. Each IMF is then analyzed by the autocorrelation method to find out the first few high frequency IMFs containing random noise, and these IMFs should be de-noised by the wavelet threshold. The zero-crossing rate (ZCR) of all IMFs, including final residue, are computed, and the IMFs with ZCR less than a certain value are removed. Finally, the remaining IMFs are reconstructed to obtain the clean ECG signal. The proposed algorithm is validated through experiments using the MIT–BIH ECG databases, and the results show that the random noise in the ECG signal can be effectively suppressed, and at the same time the baseline wander can be corrected efficiently.

Extended Kalman smoother with differential evolution technique for denoising of ECG signal.

Electrocardiogram (ECG) signal gives a lot of information on the physiology of heart. In reality, noise from various sources interfere with the ECG signal. To get the correct information on physiology of the heart, noise cancellation of the ECG signal is required. In this paper, the effectiveness of extended Kalman smoother (EKS) with the differential evolution (DE) technique for noise cancellation of the ECG signal is investigated. DE is used as an automatic parameter selection method for the selection of ten optimized components of the ECG signal, and those are used to create the ECG signal according to the real ECG signal. These parameters are used by the EKS for the development of the state equation and also for initialization of the parameters of EKS. EKS framework is used for denoising the ECG signal from the single channel. The effectiveness of proposed noise cancellation technique has been evaluated by adding white, colored Gaussian noise and real muscle artifact noise at different SNR to some visually clean ECG signals from the MIT-BIH arrhythmia database. The proposed noise cancellation technique of ECG signal shows better signal to noise ratio (SNR) improvement, lesser mean square error (MSE) and percent of distortion (PRD) compared to other well-known methods.

Implementation of Extended Kalman Filter for Real-Time Noncontact ECG Signal Acquisition in Android-Based Mobile Monitoring System

Noncontact electrocardiogram (ECG) measurement using capacitive-coupled technique is a very reliable long-term noninvasive health-care remote monitoring system. It can be used continuously without interrupting the daily activities of the user and is one of the most promising developments in health-care technology. However, ECG signal is a very small electric signal. A robust system is needed to separate the clean ECG signal from noise in the measurement environment. Noise may come from many sources around the system, for example, bad contact between the sensor and body, common-mode electrical noise, movement artifacts, and triboelectric effect. Thus, in this paper, the extended Kalman filter (EKF) is applied to denoise a real-time ECG signal in capacitive-coupled sensors. The ECG signal becomes highly stable and noise-free by combining the common analog signal processing and the digital EKF in the processing step. Furthermore, to achieve ubiquitous monitoring, android-based application is developed to process the heart rate in a realtime ECG measurement.

Analysis of Human Electrocardiogram for Biometric Recognition Using Analytic and AR Modeling Extracted Parameters

In this paper, a new approach for automatic analysis of single lead ECG for human recognition is proposed and evaluated. Following the pre-processing step, the ECG stream is partitioned into separate windows where each window includes single beat of ECG signal. After successful QRS detection, various temporal, amplitude and AR coefficients are extracted and used as an input to a classifier in order to identify the individuals. In this work, proposed system has been tested using records from three different publicly available ECG databases. Signal pre-processing techniques, applied parameter extraction methods and some intermediate and final classification results are presented in this paper. that PAW yields equivalent performance in terms of accuracy compared to conventional temporal and amplitude feature extraction methods. Even though, PAW is complicated process which needs powerful digital signal processors to overcome the time delay. In this paper, a new approach for automatic analysis of single lead electrocardiogram (ECG) for human recognition and individual identification is proposed. This approach depends on on analytic (Amplitude, Time and Width) and modelling (AR) features extracted from the ECG beat. Obtained results indicate high level of accuracy and shorter processing time needed to identify the individuals. Eighteen analytic and modelling features are extracted to identify individuals and k nearest neighbour (knn) classification algorithm applied in order to classify those features and evaluate the proposed approach. ECG feature selection and extraction using AR modelling has recently been used (9) resulting in accurate classification of various arrhythmia and ventricular arrhythmia conditions. The remainder of this paper is organized as follows. Section II gives a brief description of the techniques used in the pre-processing phase to clean ECG signals of noise and other artefacts. Section III provides a review of QRS detection methods used in this work. Feature selection and extraction methods are discussed in Section IV whilst Section V contains experimental results and discussion of those results. Conclusions are presented in Section VI.

Limitations of VCG based gating methods in ultra high field cardiac MRI

Background The electrocardiogram (ECG) is important for gating purposes in cardiac magnetic resonance imaging (CMR). However, the magnetohydrodynamic (MHD) effect, which is caused by the flow of blood in the static magnetic field, makes it difficult to record clean ECG signals for gating. The vectorcardiogram (VCG), which can be derived from the ECG signal, is commonly used for gating purposes and has been well established for magnetic fields strength of up to 3T. However, for higher field strengths this method is prone to errors [1]. This work tries to explain the reasons for the VCG based methods not being suitable for cardiac gating in ultra high field MRI. Methods ECGs were recorded using a standard 12-lead Holter ECG (Getemed, Germany) inside a 7T MR scanner (Siemens Magnetom), a 3T MR scanner (Philips Achieva) and outside the MR scanner as references. Measurements were made on five healthy volunteers while MR imaging was switched off. The VCGs were derived from the 12-lead ECG using the inverse Dower matrix [2]. The VCG method [3] was implemented and applied to the acquired data sets. This method measures the distance in the VCG space between a static reference vector defined by the R-wave (R) peak of an ECG signal recorded outside the MR scanner (at 0T) and the VCG vector recorded inside the MR scanner. Those points where a minimal distance and angle between both vectors is reached are classified as R peaks.