An efficient removal of power-line interference and baseline wander from ECG signals by employing Fourier decomposition technique
TL;DR: A new methodology based on the Fourier decomposition method (FDM) to separate both BW and PLI simultaneously from the recorded ECG signal and obtain clean ECG data and has low computational complexity which makes it suitable for real-time pre-processing of ECG signals.
About: This article is published in Biomedical Signal Processing and Control.The article was published on 2020-03-01. It has received 93 citations till now. The article focuses on the topics: Discrete Fourier transform & Discrete cosine transform.
Citations
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TL;DR: Signal-to-noise ratio, percentage root-mean-square difference, and root mean square error are used to compare the ECG signal denoising performance and the experimental result showed that the proposed stationary wavelet transform based ECGDenoising technique outperformed the other ECG Denoising techniques as more ECGs signal components are preserved than other denoised algorithms.
Abstract: Electrocardiogram (ECG) signals are used to diagnose cardiovascular diseases. During ECG signal acquisition, various noises like power line interference, baseline wandering, motion artifacts, and electromyogram noise corrupt the ECG signal. As an ECG signal is non-stationary, removing these noises from the recorded ECG signal is quite tricky. In this paper, along with the proposed denoising technique using stationary wavelet transform, various denoising techniques like lowpass filtering, highpass filtering, empirical mode decomposition, Fourier decomposition method, discrete wavelet transform are studied to denoise an ECG signal corrupted with noise. Signal-to-noise ratio, percentage root-mean-square difference, and root mean square error are used to compare the ECG signal denoising performance. The experimental result showed that the proposed stationary wavelet transform based ECG denoising technique outperformed the other ECG denoising techniques as more ECG signal components are preserved than other denoising algorithms.
77 citations
TL;DR: The single-lead ECG signal is divided into 1-min segments, and separated into frequency bands using Fourier decomposition method, which makes it computationally efficient and can be used for real-time sleep apnea detection.
63 citations
TL;DR: Chaos theory and ARTFA have been considered in this paper to estimate reliable and robust thresholds for R-peak detection and show that the proposed technique enhances sensitivity, positive predictive value and accuracy thereby improving the detection of arrhythmias.
Abstract: Timely detection of cardiac abnormalities from an Electrocardiogram (ECG) signal is very essential. This requires its appropriate and efficient processing. In the literature, most of the researchers focussed on linear techniques that were applied on filtered ECG datasets leaving an ample scope for exploring the use of non-linear techniques in the presence and absence of natural noise-processes. Therefore, there is a need of supplementing the existing research on ECG signal interpretation by using non-linear techniques on noisy ECG data. Non-linear techniques are expected to yield supplementary clues about the non-linearities in the underlying cardiovascular system. One such promising non-linear technique, known as chaos theory (analysis), has been considered here to estimate reliable and robust thresholds for R-peak detection using fractal dimension, Approximate Entropy, Sample Entropy, correlation dimension, and Lyapunov exponent based on time-delay dimension (embedding). Also, time–frequency analysis techniques have shown their effectiveness for analyzing such types of non-linear and non-stationary signals due to simultaneous interpretation of the signal in both time and frequency domain. Among existing TFA techniques such as wavelet transform, short time Fourier transform, Hilbert transform, Auto-regressive Time Frequency Analysis (ARTFA) offers good time–frequency resolution. Therefore, Chaos theory and ARTFA have been considered in this paper. First, raw ECG signal was filtered using Savitzky Golay Digital Filtering (SGDF) because it retains all important signal features after filtering. Second, a novel optimal trajectory detection step was proposed on the basis of phase space reconstruction (attractors) in chaos theory. Third, ARTFA has been used to find the spectral components of the extracted features using chaos theory. Here, ARTFA has been used for finding the autoregressive coefficients in the first step and time–frequency description in the second step. Burg method has been considered for Auto-regressive modeling to fit an ARTFA model for analyzing ECG signal by minimizing (least squares) the forward and backward prediction errors. MIT-BIH arrhythmia database has been considered for validating the present research effort. Some real time signals were also tested to explore direct usage of the proposed technique in practical applications. The obtained results show that the proposed technique enhances sensitivity, positive predictive value and accuracy thereby improving the detection of arrhythmias. Computational cost of the proposed technique is reduced to a great extent by using the chaos theory (analysis) yielding efficient detection performance. All results have been obtained in MATLAB environment R2011a. Improved values viz. 99.96% SE, 99.97% PPV, and 99.93% ACC are obtained.
46 citations
TL;DR: The experimental results and performance measures show that the proposed FBDM is more capable for analysis of non-stationary multi-component signals such as linear frequency modulated and nonlinearfrequency modulated signals as compared to the existing methods.
35 citations
TL;DR: Results show that the Universal modified threshold level-dependent threshold value selection with Non -Negative Garrote threshold function delivers higher performance in terms of SNR, lower values of MSE and PRD, and an estimator that yields efficient results than a conventional estimator using Gaussian distribution function.
30 citations
References
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TL;DR: In this paper, a new method for analysing nonlinear and nonstationary data has been developed, which is the key part of the method is the empirical mode decomposition method with which any complicated data set can be decoded.
Abstract: A new method for analysing nonlinear and non-stationary data has been developed. The key part of the method is the empirical mode decomposition method with which any complicated data set can be dec...
18,956 citations
TL;DR: The newly inaugurated Research Resource for Complex Physiologic Signals (RRSPS) as mentioned in this paper was created under the auspices of the National Center for Research Resources (NCR Resources).
Abstract: —The newly inaugurated Research Resource for Complex Physiologic Signals, which was created under the auspices of the National Center for Research Resources of the National Institutes of He...
11,407 citations
TL;DR: Although considerable improvement has occurred in the process of care for patients with ST-elevation myocardial infarction (STEMI), room for improvement exists as discussed by the authors, and the purpose of the present guideline is to focus on the numerous advances in the diagnosis and management of patients
Abstract: Although considerable improvement has occurred in the process of care for patients with ST-elevation myocardial infarction (STEMI), room for improvement exists.[1–3][1][][2][][3] The purpose of the present guideline is to focus on the numerous advances in the diagnosis and management of patients
8,352 citations
Journal Article•
TL;DR: Elliott M. Antman,MD, FACC, FAHA, Chair; Daniel T. Anbe, MD, F ACC,FAHA; Paul Wayne Armstrong, MD; Eric R. Bates; Lee A. Green; Mary Hand; Judith S. Kushner; and Sidney C. Sloan.
7,134 citations
TL;DR: This work proposes an entirely non-recursive variational mode decomposition model, where the modes are extracted concurrently and is a generalization of the classic Wiener filter into multiple, adaptive bands.
Abstract: During the late 1990s, Huang introduced the algorithm called Empirical Mode Decomposition, which is widely used today to recursively decompose a signal into different modes of unknown but separate spectral bands. EMD is known for limitations like sensitivity to noise and sampling. These limitations could only partially be addressed by more mathematical attempts to this decomposition problem, like synchrosqueezing, empirical wavelets or recursive variational decomposition. Here, we propose an entirely non-recursive variational mode decomposition model, where the modes are extracted concurrently. The model looks for an ensemble of modes and their respective center frequencies, such that the modes collectively reproduce the input signal, while each being smooth after demodulation into baseband. In Fourier domain, this corresponds to a narrow-band prior. We show important relations to Wiener filter denoising. Indeed, the proposed method is a generalization of the classic Wiener filter into multiple, adaptive bands. Our model provides a solution to the decomposition problem that is theoretically well founded and still easy to understand. The variational model is efficiently optimized using an alternating direction method of multipliers approach. Preliminary results show attractive performance with respect to existing mode decomposition models. In particular, our proposed model is much more robust to sampling and noise. Finally, we show promising practical decomposition results on a series of artificial and real data.
4,111 citations