A wavelet-based ECG delineator: evaluation on standard databases

By Juan Pablo Martínez, Rute Almeida, Salvador Olmos, Ana Paula Rocha, Pablo Laguna. IEEE Transactions on Biomedical Engineering, 2004

1,711 citations recorded by the source corpus.

Subfields: Cardiology and Cardiovascular Medicine, Cognitive Neuroscience

View Paper: https://www.semanticscholar.org/paper/31546e96be62af2f8e09af5a0921db145341ade7

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In Cardiology and Cardiovascular Medicine

In Cognitive Neuroscience

Citations in the corpus

Analysis of Human Electrocardiogram for Biometric Recognition (2008, background): “Even if these detectors are accurate in identifying exact fiducial locations validated by cardiologists, there is no universally acknowledged rule for defining exactly where the wave boundaries lie [14].”
Logistic Regression-HSMM-Based Heart Sound Segmentation (2016, methodology): ““gQRS” (available on Physionet [25]), “jQRS” (previously used in two studies [26], [27]), an algorithm based on parabolic fitting [28], and a wavelet-based ECG delineator [29].”
ECG-based heartbeat classification for arrhythmia detection: A survey (2016, methodology): “More sophisticated methods have also been used, such as methods based on neural networks [53], genetic algorithms [50], wavelet transform [60,61,4], filter banks [46], b i o m e d i c i n e 1 2 7 ( 2 0 1 6 ) 144–164”
QRS detection using K-Nearest Neighbor algorithm (KNN) and evaluation on standard ECG databases (2012, result): “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].”
Cardiologist-Level Arrhythmia Detection with Convolutional Neural Networks (2017, methodology): “Current algorithms for R-peak extraction tend to use wavelet transformations to compute features from the raw ECG followed by finely-tuned threshold based classifiers (Li et al., 1995; Martı́nez et al., 2004).”
Wavelet-based Machine Learning Techniques for ECG Signal Analysis (2014, methodology): “Many methods for the detection of QRS complex (or the R-point) in the ECG have been proposed [4–6].”
Heartbeat Classification Using Feature Selection Driven by Database Generalization Criteria (2011, methodology): “For background and implementation details, the interested reader is referred to [17] for a more detailed description of the WT and its implementation for ECG delineation.”
Robust Neural-Network-Based Classification of Premature Ventricular Contractions Using Wavelet Transform and Timing Interval Features (2006, methodology): “Many examples of highly accurate detectors can be found in literature [12], [13], [17].”
Heartbeat classification using disease-specific feature selection (2014, methodology): “While many methods can be used to locate QRS waves and detect the fiducial points, such as the techniques used in our previous work [20,21] and wavelet-based methods [22,23], the objective of the present study is to develop a heartbeat classifier by feature selection.”
ECG signal analysis through hidden Markov models (2006, methodology): “The standard deviation for all recordings is computed as the average of the standard deviation of each recording [11].”