The principles of software QRS detection

By Bert-Uwe Köhler, C. Hennig, Reinhold Orglmeister. IEEE Engineering in Medicine and Biology Magazine, 2002

1,378 citations recorded by the source corpus.

Subfields: Biomedical Engineering, Cardiology and Cardiovascular Medicine, Cognitive Neuroscience

View Paper: https://www.semanticscholar.org/paper/85aa1c6883d6b51171a80c57d82370207d4f1b89

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Biomedical Engineering

In Cardiology and Cardiovascular Medicine

In Cognitive Neuroscience

Citations in the corpus

Studies on fractional order differentiators and integrators: A survey (2011, methodology): “Various techniques used for the detection of QRS complex using software is summarized in [97].”
QRS detection using K-Nearest Neighbor algorithm (KNN) and evaluation on standard ECG databases (2012, methodology): “Kohler et al. [19] proposed an extensive review of various approaches of QRS detection based on: (i) signal derivatives and digital ﬁlters, (ii) wavelet-based QRS detection, (iii) neural network approaches, (iv) additional approaches like adaptive ﬁlters, hidden Markov models, mathematical…”
QRS complexes detection for ECG signal: The Difference Operation Method (2008, background): “where x(n) is the input signal at time n, and xd(n) is the difference output signal at time n [1,13].”
ECG-based heartbeat classification for arrhythmia detection: A survey (2016, methodology): “[59], many of the methods presented in the literature do not use a standardized database, or use only part of it, which makes it difficult to fairly compare methods.”
Analysis of First-Derivative Based QRS Detection Algorithms (2008, methodology): “These methods also have the advantage of not necessitating manual segmentation of data, training of the algorithms, or patient-specific modifications that are often required for other detection methods [ 12 ].”
A novel cardiovascular risk stratification model incorporating ECG and heart rate variability for patients presenting to the emergency department with chest pain (2016, methodology): “A 5–28 Hz band-pass filter was applied to the lead II sample to facilitate peak detection [29].”
A wavelet-based ECG delineator: evaluation on standard databases (2004, background): “An extensive review of the approaches proposed in the last decade can be found in [1].”
A Nonlinear Bayesian Filtering Framework for ECG Denoising (2007, background): “Although there have been rather robust R-peak detectors developed to overcome the misdetection of the R-peaks [32], a precise study of this issue requires the”