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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.

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Citations
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Journal ArticleDOI

A 2-D ECG compression method based on wavelet transform and modified SPIHT

TL;DR: A two-dimensional wavelet-based electrocardiogram (ECG) data compression method which employs a modified set partitioning in hierarchical trees (SPIHT) algorithm and achieves high compression ratio with relatively low distortion and is effective for various kinds of ECG morphologies.
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Wavelet energy based diagnostic distortion measure for ECG

TL;DR: Wavelet Energy based diagnostic distortion (WEDD) provides a better prediction accuracy and exhibits a statistically better monotonic relationship with the MOS ratings than Wavelet based weighted percentage root mean square difference (PRD) measure (WWPRD), PRD and other objective measures.
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Low-oscillation complex wavelets

TL;DR: The authors believe that low oscillation complex wavelets have wide applicability to other practical signal analysis problems, and their possible application to two such problems is discussed briefly—the interrogation of arrhythmic ECG signals and the detection and characterization of coherent structures in turbulent flow fields.
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Continuous wavelet transform modulus maxima analysis of the electrocardiogram: beat characterisation and beat-to-beat measurement

TL;DR: An R-wave detector is developed and tested using patient signals recorded in the Coronary Care Unit of the Royal Infirmary of Edinburgh and with the MIT/BIH database, achieving a sensitivity and positive predictive value of 99.73% and 99.68%, respectively.
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A model-based Bayesian framework for ECG beat segmentation

TL;DR: Simulation results show that the Bayesian filtering framework may be effectively used for ECG beat segmentation and extraction of fiducial points and can contribute to and enhance the clinical ECGBeat segmentation performance.
References
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Journal ArticleDOI

A Real-Time QRS Detection Algorithm

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

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

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.
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