R-peaks detection based on stationary wavelet transform

Merah, Mostefa; Abdelmalik, Taleb Ahmed; Larbi, B.H.

doi:10.1016/j.cmpb.2015.06.003

Cited by 146 publications

(75 citation statements)

References 27 publications

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“…In this case, the filters are upsampled by a factor 2 at each stage (Fig 1(b)). Therefore, the WT at each scale has the same length of the original signal, thus keeping its original resolution [25]. The equivalent frequency response of the filters at scale \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$k$ \end{document} is:\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}\begin{equation*} Q_{k} (f)=\begin{cases} G(f) & k=1 \\ G(2^{k-1}f)\prod \limits _{l=0}^{k-2} {H(2^{l}f)} & k\ge 2 \end{cases} \end{equation*}\end{document}…”

Section: Methodsmentioning

confidence: 99%

A New Wavelet-Based ECG Delineator for the Evaluation of the Ventricular Innervation

Cesari

Mehlsen

et al. 2017

IEEE J. Transl. Eng. Health Med.

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T-wave amplitude (TWA) has been proposed as a marker of the innervation of the myocardium. Until now, TWA has been calculated manually or with poor algorithms, thus making its use not efficient in a clinical environment. We introduce a new wavelet-based algorithm for the delineation QRS complexes and T-waves, and the automatic calculation of TWA. When validated in the MIT/BIH Arrhythmia database, the QRS detector achieved sensitivity and positive predictive value of 99.84% and 99.87%, respectively. The algorithm was validated also on the QT database and it achieved sensitivity of 99.50% for T-peak detection. In addition, the algorithm achieved delineation accuracy that is similar to the differences in delineation between expert cardiologists. We applied the algorithm for the evaluation of the influence in TWA of anticholinergic and antiadrenergic drugs (i.e., atropine and metoprolol) for healthy subjects. We found that the TWA decreased significantly with atropine and that metoprolol caused a significant increase in TWA, thus confirming the clinical hypothesis that the TWA is a marker of the innervation of the myocardium. The results of this paper show that the proposed algorithm can be used as a useful and efficient tool in clinical practice for the automatic calculation of TWA and its interpretation as a non-invasive marker of the autonomic ventricular innervation.

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Section: Methodsmentioning

confidence: 99%

A New Wavelet-Based ECG Delineator for the Evaluation of the Ventricular Innervation

Cesari

Mehlsen

et al. 2017

IEEE J. Transl. Eng. Health Med.

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“…There have been many WT-based methods, but their performances have been observed lower than those of non-WT-based methods [1, 4, 10, 13, 16–18, 25]. Our study demonstrates the possibility of obtaining high-peak detection accuracy in ECG signal analysis by using WT method.…”

Section: Resultsmentioning

confidence: 73%

“…The detection of fiducial points is critical for the initial diagnosis and analysis of CVDs. In Figure 1(), we can see the peaks of the QRS complex, the highest of which is known as the R peak in the QRS interval [10]. Other time segments are also shown, such as the P-Q and S-T segments.…”

Section: Introductionmentioning

confidence: 99%

“…Those methods include slope-based threshold methods [14, 15], wavelet-transform-based methods [1, 4, 10, 16–18], mathematical-morphology-based methods [6, 19], digital filtering methods [14, 20, 21], and Shannon energy envelope- (SEE-) based methods [22–25]. There are also other studies that use the wavelet transform for denoising ECG signals [4, 17].…”

Section: Introductionmentioning

confidence: 99%

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R Peak Detection Method Using Wavelet Transform and Modified Shannon Energy Envelope

Park

Lee

Park

2017

Journal of Healthcare Engineering

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Rapid automatic detection of the fiducial points—namely, the P wave, QRS complex, and T wave—is necessary for early detection of cardiovascular diseases (CVDs). In this paper, we present an R peak detection method using the wavelet transform (WT) and a modified Shannon energy envelope (SEE) for rapid ECG analysis. The proposed WTSEE algorithm performs a wavelet transform to reduce the size and noise of ECG signals and creates SEE after first-order differentiation and amplitude normalization. Subsequently, the peak energy envelope (PEE) is extracted from the SEE. Then, R peaks are estimated from the PEE, and the estimated peaks are adjusted from the input ECG. Finally, the algorithm generates the final R features by validating R-R intervals and updating the extracted R peaks. The proposed R peak detection method was validated using 48 first-channel ECG records of the MIT-BIH arrhythmia database with a sensitivity of 99.93%, positive predictability of 99.91%, detection error rate of 0.16%, and accuracy of 99.84%. Considering the high detection accuracy and fast processing speed due to the wavelet transform applied before calculating SEE, the proposed method is highly effective for real-time applications in early detection of CVDs.

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“…These methods are based on mathematical functions, filtering approaches (digital fil-5 ters [2], adaptive filters [3]), classification methods (neural network approaches [4], support vector machine (SVM) [5], fuzzy C-means algorithm [6]), wavelet transform [7] and empirical mode decomposition (EMD) [8]. Low pass differentiation (LPD) [9], hidden Markov models (HMM) [10,11,12,13,14], partially collapsed Gibbs sampler (PCGS) [15], wavelet transform [16,17,18], correlation analysis [19,20] and extended…”

Section: Introductionmentioning

confidence: 99%

ECG fiducial point extraction using switching Kalman filter

Akhbari

Ghahjaverestan

Shamsollahi

et al. 2018

Computer Methods and Programs in Biomedicine

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In this paper, we propose a novel method for extracting fiducial points (FPs) of the beats in electrocardiogram (ECG) signals using switching Kalman filter (SKF). In this method, according to McSharry's model, ECG waveforms (P-wave, QRS complex and T-wave) are modeled with Gaussian functions and ECG baselines are modeled with first order auto regressive models. In the proposed method, a discrete state variable called "switch" is considered that affects only the observation equations. We denote a mode as a specific observation equation and switch changes between 7 modes and corresponds to different segments of an ECG beat. At each time instant, the probability of each mode is calculated and compared among two consecutive modes and a path is estimated, which shows the relation of each part of the ECG signal to the mode with the maximum probability. ECG FPs are found from the estimated path. For performance evaluation, the Physionet QT database is used and the proposed method is compared with methods based on wavelet transform, partially collapsed Gibbs sampler (PCGS) and extended Kalman filter. For our proposed method, the mean error and the root mean square error across all FPs are 2 ms (i.e. less than one sample) and 14 ms, respectively. These errors are significantly smaller than those obtained using other methods. The proposed method achieves lesser RMSE and smaller variability with respect to others.

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R-peaks detection based on stationary wavelet transform

Cited by 146 publications

References 27 publications

A New Wavelet-Based ECG Delineator for the Evaluation of the Ventricular Innervation

A New Wavelet-Based ECG Delineator for the Evaluation of the Ventricular Innervation

R Peak Detection Method Using Wavelet Transform and Modified Shannon Energy Envelope

ECG fiducial point extraction using switching Kalman filter

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