QRS detection algorithm based on the quadratic filter

Phukpattaranont, Pornchai

doi:10.1016/j.eswa.2015.02.012

Cited by 102 publications

(51 citation statements)

References 38 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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“…Additionally, large P or T waves can also disturb the detection of QRS complexes. Therefore, as mentioned above, most QRS complex detection algorithms filter the ECG signals in a preprocessing stage [5][6][7][8][9][10][11]. In this study, we implement mean filtering to remove baseline wander.…”

Section: Ecg Signals Contain a Variety Of Types Of Noises Including Pmentioning

confidence: 99%

“…A high-pass filter, MaMeMi, has been used to remove noise from ECG signals [9]. In addition, quadratic and two event-related moving average filters have been used to reinforce the QRS complexes, which aid the detection of R peaks [10,11]. In all these methods, most of the filtering operations can cancel the noise; however, the amplitude of the R peaks is also reduced, which affects the subsequent decision stage.…”

Section: Introductionmentioning

confidence: 99%

Short - time detection of QRS complex es using dual channel s based on U-Net and bidirectional long short - term memory

Rui¹,

Liu²,

Wang³

et al. 2020

Preprint

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Cardiovascular disease is associated with high rates of morbidity and mortality, and can be reflected by abnormal features of electrocardiogram (ECG). Detecting changes in the QRS complexes in ECG signals is regarded as a straightforward, noninvasive, inexpensive, and preliminary diagnosis approach for evaluating the cardiac health of patients. Therefore, detecting QRS complexes in ECG signals must be accurate over short times. However, the reliability of automatic QRS detection is restricted by all kinds of noise and complex signal morphologies. In this study, we proposed a new algorithm for short-time detection of QRS complexes using dual channels based on U-Net and bidirectional long short-term memory. First, a proposed preprocessor with mean filtering and discrete wavelet transform was initially applied to remove different types of noise. Next the signal was transformed and annotations were relabeled. Finally, a method combining U-Net and bidirectional long short-term memory with dual channels was used for the short-time detection of QRS complexes. The proposed algorithm was trained and tested using 44 ECG records from the MIT-BIH arrhythmia database. It achieved, on average, high values for detection sensitivity (99.56%), positive predictivity (99.72%), and accuracy (99.28%) on the test set, indicating an improvement compared to algorithms reported in the literature.

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“…The purpose of the ECG signal preprocessing is to remove the low-frequency baseline drift and high-frequency noise. Many previous studies have designed linear or non-linear filters to remove additive noise and interferences, such as moving averaging filters [1], low-pass filters [2], bandpass filters [2][3][4][5], non-linear filters [6], quadratic filters [7], and Savitzky-Golay (SG) smoothing filters [8]. The methods for noise removal based on the wavelet transform have also appeared in the previous works by removing part of the approximation and detail wavelet coefficients to filter out high frequency noise and baseline drift effects, respectively [9][10][11][12][13][14].…”

Section: Introductionmentioning

confidence: 99%

A Novel Wavelet-Based Algorithm for Detection of QRS Complex

et al. 2019

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Accurate QRS detection is an important first step for almost all automatic electrocardiogram (ECG) analyzing systems. However, QRS detection is difficult, not only because of the wide variety of ECG waveforms but also because of the interferences caused by various types of noise. This study proposes an improved QRS complex detection algorithm based on a four-level biorthogonal spline wavelet transform. A noise evaluation method is proposed to quantify the noise amount and to select a lower-noise wavelet detail signal instead of removing high-frequency components in the preprocessing stage. The QRS peaks can be detected by the extremum pairs in the selected wavelet detail signal and the proposed decision rules. The results show the high accuracy of the proposed algorithm, which achieves a 0.25% detection error rate, 99.84% sensitivity, and 99.92% positive prediction value, evaluated using the MIT-BIT arrhythmia database. The proposed algorithm improves the accuracy of QRS detection in comparison with several wavelet-based and non-wavelet-based approaches.

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QRS detection algorithm based on the quadratic filter

Cited by 102 publications

References 38 publications

QRS Complex Detection in ECG Signals Using the Synchrosqueezed Wavelet Transform

QRS Complex Detection in ECG Signals Using the Synchrosqueezed Wavelet Transform

Short - time detection of QRS complex es using dual channel s based on U-Net and bidirectional long short - term memory

A Novel Wavelet-Based Algorithm for Detection of QRS Complex

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