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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.

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Citations
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Proceedings ArticleDOI

QRS complex detection using cuckoo search optimization algorithm

TL;DR: It is proved from the results that the proposed COA-AF based QRS detector gives efficient performance with sensitivity, positive predictivity, and detection error rate of 99.75%, 99.79%, and 0.44% respectively.
Proceedings ArticleDOI

Optimal anisotropic lead scaling of multichannel ECG to reduce magnitude signal variability

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Book ChapterDOI

Diagnosing Heart Diseases Using Morphological and Dynamic Features of Electrocardiogram (ECG)

TL;DR: An automatic method is proposed for the heart beat classification of 15 classes mapped into five main categories with 93.84% average accuracy and 99.5% overall accuracy having been achieved using MIT-BIH dataset as a validation database.
Proceedings ArticleDOI

Detection of heartbeats based on the Bayesian framework

TL;DR: This work proposes to estimate the QRS complex parameters based on the maximum-likelihood (ML) principle to reduce the complexity of original method and its iterative counterpart is investigated by using the decomposition method.
Proceedings ArticleDOI

Blind Elimination of Electrical Artifacts Caused by the Electrosurgical Units (ESU) for ECG Signals

TL;DR: This research study focuses on the reduction of the electrical artifacts (EA) from electrocardiographic (ECG) signals in order to improve the ECG monitoring in an operating room and it has been found out that the T-SVD method gives better results.
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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