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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

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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

Compact image representation from multiscale edges

TL;DR: A coding algorithm is described that selects the most important image edges in order to obtain a compact representation and gives a precise characterization of the edge type which can be used for pattern recognition.
Proceedings ArticleDOI

Application of wavelet analysis in detection of fetal ECG

TL;DR: Tests on clinical data have shown that the proposed wavelet analysis method can be effective in detecting the FECG signal in many cases when compared with other methods in practice.
Proceedings ArticleDOI

QRS detection by wavelet transform

TL;DR: The wavelet transform(WT) is used for QW detection with some results in QRS detection with the WT, andvantages have been found in its use to locate signal singularities.
Proceedings ArticleDOI

Wavelet Analysis Of E.C.G. Signals

TL;DR: A preliminary attempt is proposed in this paper based on wavelet analysis to separation of relevant waves from the E.C.G. signals prior to interpretation because of the noisy nature of the input data.
Proceedings ArticleDOI

The dyadic wavelet transform based QRS detector (ECG analysis)

TL;DR: A QRS complex detector for electrocardiogram (ECG) analysis based on the dyadic wavelet transform (D/sub y/WT) is described, which overcomes the problems associated with several QRS detectors.
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