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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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Automatic classification of heartbeats using ECG morphology and heartbeat interval features

TL;DR: A method for the automatic processing of the electrocardiogram (ECG) for the classification of heartbeats and results are an improvement on previously reported results for automated heartbeat classification systems.
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The principles of software QRS detection

TL;DR: The authors provide an overview of these recent developments as well as of formerly proposed algorithms for QRS detection, which reflects the electrical activity within the heart during the ventricular contraction.
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Wavelet transforms and the ECG: a review.

TL;DR: In this review, the emerging role of the wavelet transform in the interrogation of the ECG is discussed in detail, where both the continuous and the discrete transform are considered in turn.
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ECG-based heartbeat classification for arrhythmia detection

TL;DR: This work surveys the current state-of-the-art methods of ECG-based automated abnormalities heartbeat classification by presenting the ECG signal preprocessing, the heartbeat segmentation techniques, the feature description methods and the learning algorithms used.
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Optimal selection of wavelet basis function applied to ECG signal denoising

TL;DR: A selection procedure of mother wavelet basis functions applied for denoising of the ECG signal in wavelet domain while retaining the signal peaks close to their full amplitude is presented.
References
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Proceedings ArticleDOI

Fetal heart rate signal denoising by processing the wavelet transform modulus maxima

TL;DR: In this paper, a noise reduction technique that detects noise components by analysing the evolution of the Wavelet Transform modulus maxima across scales is adapted to improve the quality of FHR recording.
Journal ArticleDOI

Wavelet Transformations in Signal Detection

TL;DR: Wavelets are well founded on rigorous mathematical theory, and the expansions are robust, and they are applied to detect ventricular delayed potentials in the electrocardiogram.
Proceedings ArticleDOI

Wavelet based compression of Holter ECG signals

TL;DR: Presents a wavelet based algorithm for compression of long ECG data records, typically associated with a Holter, that takes special use of the time-domain morphology of the signal as well as its clinical importance.
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