2002
DOI: 10.1080/03091900110096038
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QRS detection using new wavelets

Abstract: This paper deals with a new wavelet (WVT) which has been developed and very effectively and efficiently used for the detection of QRS segments from the ECG signal. After carrying out the detection using five existing wavelets (two symmetric--WT1 and WT2--and three asymmetric--WT3, WT4 and WT5), two new wavelets (WT6 and WT7) were constructed and used for QRS detection. WT6 is a symmetric wavelet and has been constructed by a trial-and-error method. WT7 is an adaptive symmetric wavelet and adjusts its threshold… Show more

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Cited by 35 publications
(9 citation statements)
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“…Unlike the Fourier transform, which exclusively employs harmonic functions, the wavelet framework allows for a choice of wave form. In practical applications, it is often not so clear how to make this choice [28]. Sometimes a choice is driven by theoretical considerations of a general nature, such as the wavelet being orthogonal and having as many vanishing moments as possible [7].…”
Section: Introductionmentioning
confidence: 99%
“…Unlike the Fourier transform, which exclusively employs harmonic functions, the wavelet framework allows for a choice of wave form. In practical applications, it is often not so clear how to make this choice [28]. Sometimes a choice is driven by theoretical considerations of a general nature, such as the wavelet being orthogonal and having as many vanishing moments as possible [7].…”
Section: Introductionmentioning
confidence: 99%
“…The detection performance of our proposed algorithm in comparison to other published works tested on MIT-BIH Arrhythmia database is also given in Table 5 [11,12,6,13,30,14,16,15,10,17,31,32] . The percentage of sensitivity/rate of accurate QRS detection given in Table 5 is not directly comparable, because, different number of beats has been used by different researchers.…”
Section: Resultsmentioning
confidence: 99%
“…This is mainly due to the time variant characteristic of the QRS-complex. Later on wavelet transform (WT) was proposed to overcome the drawbacks of this fixed filtering bandwidth and moving window duration [13–16] . In order to further improve the detection accuracy, new signal analysis technique based on empirical mode decomposition has been proposed for detection of QRS-complexes [17] .…”
Section: Introductionmentioning
confidence: 99%
“…The algorithm incorporates a filter bank which decomposes the ECG into subbands with uniform frequency bandwidths. • Saxena et al (2002) proposed a new wavelet for QRS detection and tested for high accuracy using CSE and MIT-BIH Arrhythmia databases. • Kohler et al (2002) proposed an extensive review of various approaches of QRS detection based on: 1 signal derivatives and digital filters 2 wavelet-based QRS detection 3 neural network approaches 4 additional approaches like adaptive filters, hidden Markov models, mathematical morphology, matched filters, genetic algorithms, and Hilbert transform-based QRS detection etc.…”
Section: Introductionmentioning
confidence: 99%