ECG Enhancement and QRS Detection Based on Sparse Derivatives

Ning, Xiaoran; Selesnick, Ivan

doi:10.1016/j.bspc.2013.06.005

Cited by 82 publications

(52 citation statements)

References 27 publications

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“…Various pre-processing techniques have been proposed in the literature, such as the derivative-based technique used in the classic and popular algorithm proposed by Pan and Tompkins in [10]. Numerous other pre-processing techniques have also been proposed, including those based on artificial neural networks [11][12][13], wavelet transforms [14][15][16][17], quadratic filter [18], S-transform [19], sparse derivatives [20] and Shannon energy envelope [21,22].…”

Section: Introductionmentioning

confidence: 99%

QRS Complex Detection in ECG Signals Using the Synchrosqueezed Wavelet Transform

Sharma

2016

IETE Journal of Research

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The QRS complex is the most distinctive feature in an electrocardiogram (ECG) signal. Therefore, its detection serves as the starting point for various applications, such as detection of other waves and segments, heart-rate calculation, derivation of respiration, etc. In this paper, a novel technique for QRS detection is proposed. The technique is based on the recently proposed synchrosqueezed wavelet transform (SSWT), which is obtained by application of a post-processing technique known as synchrosqueezing to the continuous wavelet transform. Following SSWT, various other processing steps are applied, including a nonlinear mapping technique, which is novel in the context of QRS detection, to finally detect the R-peaks. The proposed algorithm is evaluated on the MIT-BIH arrhythmia database and overall sensitivity, positive predictivity and error rate obtained are 99.92%, 99.93%, and 0.15%, respectively.

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Section: Introductionmentioning

confidence: 99%

QRS Complex Detection in ECG Signals Using the Synchrosqueezed Wavelet Transform

Sharma

2016

IETE Journal of Research

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“…There are slightly different numbers of overall beats for different numbers. According to references [16,17,19], several segments in ECG data have been discarded because R peaks are unrecognizable even for physicians. The computational cost has been considered by the simulation time cost: Proposed method<Method 4< Method 3<Method 1<Method 2.…”

Section: Resultsmentioning

confidence: 99%

“…heart beats in the sample. LMS Algorithm is compared with Method 1 [16] based on sparse derivatives, Method 2 [17] based on positive and negative slope of QRS complex, Method 3 [18] based on wavelet transform, Method 4 [19] based on topological mapping. The performances of above algorithms for MIT-BIH Arrhythmia Database are shown in Table I.Compared with other algorithms, LMS Algorithm achieves better performance on all the evaluation indices.…”

Section: Resultsmentioning

confidence: 99%

QRS detection by lifting scheme constructing multi-resolution morphological decomposition

Zhang

Heather

Zhang

2014

2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society

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QRS complex detecting algorithm is core of ECG auto-diagnosis method and deeply influences cardiac cycle division for signal compression. However, ECG signals collected by noninvasive surface electrodes areusually mixed with several kinds of interference, and its waveform variation is the main reason for the hard realization of ECG processing. This paper proposes a QRS complex detecting algorithm based on multi-resolution mathematical morphological decomposition. This algorithm possesses superiorities in R peak detection of both mathematical morphological method and multi-resolution decomposition. Moreover, a lifting constructing method with Maximizationupdating operator is adopted to further improve the algorithm performance. And an efficient R peak search-back algorithm is employed to reduce the false positives (FP) and false negatives (FN). The proposed algorithm provides a good performance applying to MIT-BIH Arrhythmia Database, and achieves over 99% detection rate, sensitivity and positive predictivity, respectively, and calculation burden is low. Therefore, the proposed method is appropriate for portable medical devices in Telemedicine system.

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“…The algorithms based on spectral has been developed by author [6,7] or wavelet features [8,9], amplitude activity [10,11] and spatial context [12,13] ,ECG signal characterization [14][15][16][17][18][19] The author of this paper worked on Z eighty processor for detection of QRS in real time [14]. Ning et al proposed a technique to determine a exact peak based on largest magnitude within a fixed time window [20]. Jain et al Proposed, the ECG Feature Extractor provided by Lab VIEW Biomedical toolkit detects QRS waves [21].…”

Section: Prior Workmentioning

confidence: 99%

Qualitative features selection techniques by profiling statistical features of ECG

Chandrakar

Sharma

2015

2015 Annual IEEE India Conference (INDICON)

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The measurement of the electrical activity of the heart can be done with electrocardiogram (ECG). Automatic arrhythmia-diagnosis systems which results in high accuracy rates for inside and outside patient are still an important area of research. The accuracy of such system depends on accuracy of the classification system. All this classification system required qualitative features for classification .This paper proposed a unique method of profiling of statistical features for selection of qualitative features through ECG waveform. The proposed approach for selection of qualitative features can classify and differentiate abnormal heartbeats and normal (NORM).Left bundle branch block (LBBB), right bundle branch block (RBBB), ventricular premature contractions (VPC) and atrial premature contractions (APC) comes under abnormal heart beats.

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ECG Enhancement and QRS Detection Based on Sparse Derivatives

Cited by 82 publications

References 27 publications

QRS Complex Detection in ECG Signals Using the Synchrosqueezed Wavelet Transform

QRS Complex Detection in ECG Signals Using the Synchrosqueezed Wavelet Transform

QRS detection by lifting scheme constructing multi-resolution morphological decomposition

Qualitative features selection techniques by profiling statistical features of ECG

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