An error-bounded median filter for correcting ECG baseline wander.

The baseline wander (BLW) in electrocardiogram (ECG) is a common disturbance that has a significant influence on the ECG wave pattern recognition. Many methods, such as IIR filter, mean filter, etc., can be used to correct BLW; However, most of them work on the original ECG signals. Compressed ECG data are economic for data storage and transmission, and if the baseline correction can be processed on them, it will be more efficient than we decompress them first and then do such correction. In this paper, we propose a new type of median filter CM_Filter, which works on the synopses of straight lines achieved from ECG by piecewise linear approximation (PLA) under maximum error bound. In CM_Filter, a heuristic strategy "Quick-Finding" is deduced by a property of straight lines in order to get the quality-assured median values from the synopses. The extended experimental tests demonstrate that the proposed filter is very efficient in execution time, and effective for correcting both slow and abrupt ECG baseline wander.

Comparing different wavelet transforms on removing electrocardiogram baseline wanders and special trends.

BACKGROUND: Electrocardiogram (ECG) signal, an important indicator for heart problems, is commonly corrupted by a low-frequency baseline wander (BW) artifact, which may cause interpretation difficulty or inaccurate analysis. Unlike current state-of-the-art approach using band-pass filters, wavelet transforms can accurately capture both time and frequency information of a signal. However, extant literature is limited in applying wavelet transforms (WTs) for baseline wander removal. In this study, we aimed to evaluate 5 wavelet families with a total of 14 wavelets for removing ECG baseline wanders from a semi-synthetic dataset. METHODS: We created a semi-synthetic ECG dataset based on a public QT Database on Physionet repository with ECG data from 105 patients. The semi-synthetic ECG dataset comprised ECG excerpts from the QT database superimposed with artificial baseline wanders. We extracted one ECG excerpt from each of 105 patients, and the ECG excerpt comprised 14 s of randomly selected ECG data. Twelve baseline wanders were manually generated, including sinusoidal waves, spikes and step functions. We implemented and evaluated 14 commonly used wavelets up to 12 WT levels. The evaluation metric was mean-square-error (MSE) between the original ECG excerpt and the processed signal with artificial BW removed. RESULTS: Among the 14 wavelets, Daubechies-3 wavelet and Symlets-3 wavelet with 7 levels of WT had best performance, MSE = 0.0044. The average MSEs for sinusoidal waves, step, and spike functions were 0.0271, 0.0304, 0.0199 respectively. For artificial baseline wanders with spikes or step functions, wavelet transforms in general had lower performance in removing the BW; however, WTs accurately located the temporal position of an impulse edge. CONCLUSIONS: We found wavelet transforms in general accurately removed various baseline wanders. Daubechies-3 and Symlets-3 wavelets performed best. The study could facilitate future real-time processing of streaming ECG signals for clinical decision support systems.

Baseline wander and power-line interference elimination of ECG signals using efficient signal-piloted filtering.

A signal-piloted linear phase filtering tactic for removing baseline wander and power-line interference from the electrocardiogram (ECG) signals is suggested. The system is capable of adjusting its parameters by following the incoming signal variations. It renders the processing of lesser samples by inferior order filters. The applicability is demonstrated by using the MIT-BIH ECG database. The precision of the approach is also studied regarding the signal-to-noise ratio (SNR). Results showed that the proposed method achieves a 2.18-fold compression gain and notable computational efficiency over conventional counterpart while securing an analogous output SNR. A comparison of the designed solution is made with the contemporary empirical mode decomposition with Kalman filtering and eigenvalue decomposition based tactics. Results show that the suggested method performs better in terms of output SNR for the studied cases.

An intelligent learning approach for improving ECG signal classification and arrhythmia analysis.

The recognition of cardiac arrhythmia in minimal time is important to prevent sudden and untimely deaths. The proposed work includes a complete framework for analyzing the Electrocardiogram (ECG) signal. The three phases of analysis include 1) the ECG signal quality enhancement through noise suppression by a dedicated filter combination; 2) the feature extraction by a devoted wavelet design and 3) a proposed hidden Markov model (HMM) for cardiac arrhythmia classification into Normal (N), Right Bundle Branch Block (RBBB), Left Bundle Branch Block (LBBB), Premature Ventricular Contraction (PVC) and Atrial Premature Contraction (APC). The main features extracted in the proposed work are minimum, maximum, mean, standard deviation, and median. The experiments were conducted on forty-five ECG records in MIT BIH arrhythmia database and in MIT BIH noise stress test database. The proposed model has an overall accuracy of 99.7 % with a sensitivity of 99.7 % and a positive predictive value of 100 %. The detection error rate for the proposed model is 0.0004. This paper also includes a study of the cardiac arrhythmia recognition using an IoMT (Internet of Medical Things) approach.

Efficient algorithm for baseline wander and powerline noise removal from ECG signals based on discrete Fourier series.

Electrocardiogram (ECG) signals are often contaminated with artefacts and noises which can lead to incorrect diagnosis when they are visually inspected by cardiologists. In this paper, the well-known discrete Fourier series (DFS) is re-explored and an efficient DFS-based method is proposed to reduce contribution of both baseline wander (BW) and powerline interference (PLI) noises in ECG records. In the first step, the determination of the exact number of low frequency harmonics contributing in BW is achieved. Next, the baseline drift is estimated by the sum of all associated Fourier sinusoids components. Then, the baseline shift is discarded efficiently by a subtraction of its approximated version from the original biased ECG signal. Concerning the PLI, the subtraction of the contributing harmonics calculated in the same manner reduces efficiently such type of noise. In addition of visual quality results, the proposed algorithm shows superior performance in terms of higher signal-to-noise ratio and smaller mean square error when faced to the DCT-based algorithm.

A new multi-stage combined kernel filtering approach for ECG noise removal.

Electrocardiogram (ECG) signals are contaminated with different artifacts and noise sources which increase the difficulty in analyzing the ECG signals and obtaining accurate diagnosis of heart diseases. In this paper, a new multi-stage combined adaptive filtering design based on Kernel Recursive Least Squares Tracker (KRLST) and Kernel Recursive Least Squares with Approximate Linear Dependency (ALDKRLS) algorithms is proposed for removing artifacts and noise sources, while preserving the low frequency components and the tiny features of the ECG signal. The capability of the proposed approach is demonstrated by investigating several ECG signals from the MIT-BIH database and comparing the results with other adaptive filtering techniques. The results show that the combined ALDKRLS-KRLST approach is much superior in terms of attenuating artifacts components, sensitivity of ECG peak detection, and heart diseases diagnosis. This reveals the effectiveness of the proposed technique as an effective framework for achieving high-resolution ECG from noisy ECG recordings.

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.

Intelligent use of advanced capabilities of diagnostic ECG algorithms in a monitoring environment.

A large number of ST-elevation notifications are generated by cardiac monitoring systems, but only a fraction of them is related to the critical condition known as ST-segment elevation myocardial infarction (STEMI) in which the blockage of coronary artery causes ST-segment elevation. Confounders such as acute pericarditis and benign early repolarization create electrocardiographic patterns mimicking STEMI but usually do not benefit from a real-time notification. A STEMI screening algorithm able to recognize those confounders utilizing capabilities of diagnostic ECG algorithms in variation analysis of ST segments helps to avoid triggering a non-actionable ST-elevation notification. However, diagnostic algorithms are generally designed to analyze short ECG snapshots collected in low-noise resting position and hence are susceptible to high levels of noise common in a monitoring environment. We developed a STEMI screening algorithm which performs a real-time signal quality evaluation on the ECG waveform to select the segments with quality high enough for subsequent analysis by a diagnostic ECG algorithm. The STEMI notifications generated by this multi-stage STEMI screening algorithm are significantly fewer than ST-elevation notifications generated by a continuous ST monitoring strategy.

ECG signal denoising via empirical wavelet transform.

This paper presents new methods for baseline wander correction and powerline interference reduction in electrocardiogram (ECG) signals using empirical wavelet transform (EWT). During data acquisition of ECG signal, various noise sources such as powerline interference, baseline wander and muscle artifacts contaminate the information bearing ECG signal. For better analysis and interpretation, the ECG signal must be free of noise. In the present work, a new approach is used to filter baseline wander and power line interference from the ECG signal. The technique utilized is the empirical wavelet transform, which is a new method used to compute the building modes of a given signal. Its performance as a filter is compared to the standard linear filters and empirical mode decomposition.The results show that EWT delivers a better performance.

Computationally efficient real-time interpolation algorithm for non-uniform sampled biosignals.

This Letter presents a novel, computationally efficient interpolation method that has been optimised for use in electrocardiogram baseline drift removal. In the authors' previous Letter three isoelectric baseline points per heartbeat are detected, and here utilised as interpolation points. As an extension from linear interpolation, their algorithm segments the interpolation interval and utilises different piecewise linear equations. Thus, the algorithm produces a linear curvature that is computationally efficient while interpolating non-uniform samples. The proposed algorithm is tested using sinusoids with different fundamental frequencies from 0.05 to 0.7 Hz and also validated with real baseline wander data acquired from the Massachusetts Institute of Technology University and Boston's Beth Israel Hospital (MIT-BIH) Noise Stress Database. The synthetic data results show an root mean square (RMS) error of 0.9 µV (mean), 0.63 µV (median) and 0.6 µV (standard deviation) per heartbeat on a 1 mVp-p 0.1 Hz sinusoid. On real data, they obtain an RMS error of 10.9 µV (mean), 8.5 µV (median) and 9.0 µV (standard deviation) per heartbeat. Cubic spline interpolation and linear interpolation on the other hand shows 10.7 µV, 11.6 µV (mean), 7.8 µV, 8.9 µV (median) and 9.8 µV, 9.3 µV (standard deviation) per heartbeat.

Baseline wander removal of electrocardiogram signals using multivariate empirical mode decomposition.

A new method for removing the baseline wander (BW) noise based on multivariate empirical mode decomposition is presented. The proposed method is compared with recently introduced technique for BW removal using Hilbert vibration decomposition in terms of correlation coefficient criterion and signal-to-noise ratio. To evaluate the performance of the proposed method, real BW signals are added to synthetic and clinical electrocardiogram (ECG) signals. It is shown that presented methodology has significant scope of removing BW noise in real world ECG signals.

ECG baseline wander correction based on mean-median filter and empirical mode decomposition.

A novel approach of ECG baseline wander correction based on mean-median filter and empirical mode decomposition is presented in this paper. The low frequency parts of the original signals were removed by the mean median filter in a nonlinear way to obtain the baseline wander estimation, then its series of IMFs were sifted by t-test after empirical mode decomposition. The proposed method, tested by the ECG signals in MIT-BIH Arrhythmia database and European ST_T database, is more effective compared with other baseline wander removal methods.

Methods of removing the baseline wander in ECG based on the lifting wavelet transform / 中国医学装备

Objective:To avoid the redundant computation based on the convolution operation in the traditional wavelet transform, and to remove the baseline wander noise existing in the course of collecting the ECG signal. Methods: Use the lifting wavelet transform with two wavelets, and constitute the ECG signal with the noise removed after decomposing, setting the subband coefficient including the noise to zero, and rebuilding. Results:Use MATLAB to remove the baseline wander noise in the ECG signal and bw provided by the MIT-BIH database, and the results show that the baseline wander was removed effectively. Conclusion:The baseline wander noise in the ECG signal can be removed accurately though the method mentioned above, the waveform information in the original ECG signal can be maintained effectively, and subsequently, that can provide help for detecting the characteristic parameters in the ECG signal.

Fractal and EMD based removal of baseline wander and powerline interference from ECG signals.

This paper presents novel methods for baseline wander removal and powerline interference removal from electrocardiogram (ECG) signals. Baseline wander and clean ECG have been modeled as 1st and 2nd-order fractional Brownian motion (fBm) processes, respectively. This fractal modeling is utilized to propose projection operator based approach for baseline wander removal. Powerline interference is removed by using a hybrid approach of empirical mode decomposition method (EMD) and wavelet analysis. Simulation results are presented to show the efficacy of both the methods. The proposed methods have been shown to preserve ECG shapes characteristic of heart abnormalities.

Simultaneous powerline interference and baseline wander removal from ECG and EMG signals by sinusoidal modeling.

We present a compact approach to joint modeling of powerline interference (PLI) and baseline wonder (BW) for denoising of biopotential signals. Both PLI and BW are modeled by a set of harmonically related sinusoids modulated by low-order time polynomials. The sinusoids account on the harmonicity and mean instantaneous frequency of the PLI in the analysis window, while the polynomials capture the frequency and amplitude deviations from their nominal values and characterize the BW at the same time. The resulting model is linear-in-parameters and the solution to the corresponding linear system is estimated in a simple and efficient way through linear least-squares. The proposed modeling method was evaluated on real electrocardiographic (ECG) and electromyographic (EMG) signals against three reference methods for different analysis scenarios. The comparative study suggests that the proposed method outperforms the reference methods in terms of residual interference energy in the denoised biopotential signals.