Journal ArticleDOI
Wavelet transform-based QRS complex detector
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TLDR
AQRS complex detector based on the dyadic wavelet transform (D/sub y/WT) which is robust to time-varying QRS complex morphology and to noise is described which compared well with the standard techniques.Abstract:
In this paper, the authors describe a QRS complex detector based on the dyadic wavelet transform (D/sub y/WT) which is robust to time-varying QRS complex morphology and to noise. They design a spline wavelet that is suitable for QRS detection. The scales of this wavelet are chosen based on the spectral characteristics of the electrocardiogram (ECG) signal. They illustrate the performance of the D/sub y/WT-based QRS detector by considering problematic ECG signals from the American Heart Association (AHA) database. Seventy hours of data was considered. The authors also compare the performance of D/sub y/WT-based QRS detector with detectors based on Okada, Hamilton-Tompkins, and multiplication of the backward difference algorithms. From the comparison, results the authors observed that although no one algorithm exhibited superior performance in all situations, the D/sub y/WT-based detector compared well with the standard techniques. For multiform premature ventricular contractions, bigeminy, and couplets tapes, the D/sub y/WT-based detector exhibited excellent performance.read more
Citations
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Electrocardiogram pattern recognition and analysis based on artificial neural networks and support vector machines: a review.
TL;DR: This paper reviews methods of ECG processing from a pattern recognition perspective and focuses on features commonly used for heartbeat classification, mainly Artificial Neural Networks and Support Vector Machines.
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R Peak Detection in Electrocardiogram Signal Based on an Optimal Combination of Wavelet Transform, Hilbert Transform, and Adaptive Thresholding
TL;DR: By using wavelet and Hilbert transforms as well as by employing adaptive thresholding technique, an optimal combinational method for R peak detection namely WHAT is obtained that outperforms other techniques quantitatively and qualitatively.
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Simple real-time QRS detector with the MaMeMi filter☆
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An Algorithm Used for Ventricular Fibrillation Detection Without Interrupting Chest Compression
TL;DR: A novel continuous-wavelet-transformation-based morphology consistency evaluation algorithm was developed for the detection of disorganized VF from organized sinus rhythm (SR) without interrupting the ongoing chest compression, and was superior to other reported methods for VF detection during uninterrupted CPR.
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Model-Based Fiducial Points Extraction for Baseline Wandered Electrocardiograms
TL;DR: A fast algorithm based on the nonlinear dynamical model for the electrocardiogram (ECG) is presented and the fiducial points are accurately detected using the center and spread parameters of Gaussian-functions of the model.
References
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Journal ArticleDOI
A Real-Time QRS Detection Algorithm
Jiapu Pan,Willis J. Tompkins +1 more
TL;DR: A real-time algorithm that reliably recognizes QRS complexes based upon digital analyses of slope, amplitude, and width of ECG signals and automatically adjusts thresholds and parameters periodically to adapt to such ECG changes as QRS morphology and heart rate.
Book
Characterization of Signals From Multiscale Edges
Stéphane Mallat,S. Zhong +1 more
TL;DR: The authors describe an algorithm that reconstructs a close approximation of 1-D and 2-D signals from their multiscale edges and shows that the evolution of wavelet local maxima across scales characterize the local shape of irregular structures.
Journal ArticleDOI
Wavelets and signal processing
Olivier Rioul,Martin Vetterli +1 more
TL;DR: A simple, nonrigorous, synthetic view of wavelet theory is presented for both review and tutorial purposes, which includes nonstationary signal analysis, scale versus frequency,Wavelet analysis and synthesis, scalograms, wavelet frames and orthonormal bases, the discrete-time case, and applications of wavelets in signal processing.
Journal ArticleDOI
Detection of ECG characteristic points using wavelet transforms
TL;DR: An algorithm based on wavelet transforms (WT's) has been developed for detecting ECG characteristic points and the relation between the characteristic points of ECG signal and those of modulus maximum pairs of its WT's is illustrated.
Journal ArticleDOI
Linear and quadratic time-frequency signal representations
TL;DR: A tutorial review of both linear and quadratic representations is given, and examples of the application of these representations to typical problems encountered in time-varying signal processing are provided.