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Journal ArticleDOI

A Real-Time QRS Detection Algorithm

01 Mar 1985-IEEE Transactions on Biomedical Engineering (IEEE Trans Biomed Eng)-Vol. 32, Iss: 3, pp 230-236
TL;DR: A real-time algorithm that reliably recognizes QRS complexes based upon digital analyses of slope, amplitude, and width of ECG signals and automatically adjusts thresholds and parameters periodically to adapt to such ECG changes as QRS morphology and heart rate.
Abstract: We have developed a real-time algorithm for detection of the QRS complexes of ECG signals. It reliably recognizes QRS complexes based upon digital analyses of slope, amplitude, and width. A special digital bandpass filter reduces false detections caused by the various types of interference present in ECG signals. This filtering permits use of low thresholds, thereby increasing detection sensitivity. The algorithm automatically adjusts thresholds and parameters periodically to adapt to such ECG changes as QRS morphology and heart rate. For the standard 24 h MIT/BIH arrhythmia database, this algorithm correctly detects 99.3 percent of the QRS complexes.

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Citations
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Journal ArticleDOI
TL;DR: It is observed that the proposed method classifies ECG beats with a smaller size of network without making any concessions on the classification performance.
Abstract: This paper presents a method for electrocardiogram (ECG) beat classification based on particle swarm optimization (PSO) and radial basis function neural network (RBFNN). Six types of beats including Normal Beat, Premature Ventricular Contraction (PVC), Fusion of Ventricular and Normal Beat (F), Atrial Premature Beat (A), Right Bundle Branch Block Beat (R) and Fusion of Paced and Normal Beat (f) are obtained from the MIT-BIH arrhythmia database. Four morphological features are extracted from each beat after the preprocessing of the selected records. For classification stage of the extracted features, a RBFNN structure which is evolved by particle swarm optimization is used. Several experiments are performed over the test set and it is observed that the proposed method classifies ECG beats with a smaller size of network without making any concessions on the classification performance.

178 citations

Journal ArticleDOI
TL;DR: Transferred deep learning proved to be an efficient automatic cardiac arrhythmia detection method while eliminating the burden of training a deep convolutional neural network from scratch providing an easily applicable technique.

177 citations

Journal ArticleDOI
TL;DR: The imaging results provide new information supporting the idea that generators in the right lateral aspect of the STG are implicated in processes of frequency deviant detection, in addition to generators inThe right and left STP, and validate the experimental procedures for simultaneous ERP/fMRI of the auditory cortex.

176 citations


Cites methods from "A Real-Time QRS Detection Algorithm..."

  • ...Second, a variation of Pan and Tompkins (1985) real-time QRS detection algorithm was implemented to detect the QRS complexes by analyzing the slope, amplitude, and width of the waveforms....

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  • ...Second, a variation of Pan and Tompkins (1985) real-time QRS detection algorithm was implemented to detect the QRS complexes by analyzing the slope, amplitude, and width of the waveforms....

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Journal ArticleDOI
TL;DR: Transient similarities in the direction of wavelet propagation in the majority of patients with atrial fibrillation is consistent with the presence of transient linking, the first direct evidence that atrial activation during atrial Fibrillation in humans is not entirely random.
Abstract: BACKGROUNDAtrial fibrillation is usually thought of as a "random" pattern of circulating wavelets. However, local atrial activation should be influenced by the constant anatomy and receding tail of refractoriness from the previous activation. The general tendency for wave fronts to follow paths of previous excitation has been termed "linking." We examined intra-atrial electrograms recorded during atrial fibrillation for evidence of linking.METHODS AND RESULTSTwo minutes of atrial fibrillation were recorded in 15 patients with an orthogonal catheter. We have previously demonstrated that this catheter can be used to detect changes in the direction of local atrial activation. A mean vector was calculated for each electrogram. The similarity of the direction of the vectors from two consecutive electrograms can be quantified on a scale of 1 to -1 by calculating the cosine (cos) of the smallest angle (theta) between them. Two vectors pointing in the same or opposite directions then have cos(theta) = 1 or -1, re...

175 citations

Journal ArticleDOI
TL;DR: The NLPCA techniques are used to classify each segment into one of two classes: normal and abnormal (ST+, ST-, or artifact) and test results show that using only two nonlinear components and a training set of 1000 normal samples from each file produce a correct classification rate.
Abstract: The detection of ischemic cardiac beats from a patient's electrocardiogram (EGG) signal is based on the characteristics of a specific part of the beat called the ST segment. The correct classification of the beats relies heavily on the efficient and accurate extraction of the ST segment features. An algorithm is developed for this feature extraction based on nonlinear principal component analysis (NLPCA). NLPCA is a method for nonlinear feature extraction that is usually implemented by a multilayer neural network. It has been observed to have better performance, compared with linear principal component analysis (PCA), in complex problems where the relationships between the variables are not linear. In this paper, the NLPCA techniques are used to classify each segment into one of two classes: normal and abnormal (ST+, ST-, or artifact). During the algorithm training phase, only normal patterns are used, and for classification purposes, we use only two nonlinear features for each ST segment. The distribution of these features is modeled using a radial basis function network (RBFN). Test results using the European ST-T database show that using only two nonlinear components and a training set of 1000 normal samples from each file produce a correct classification rate of approximately 80% for the normal beats and higher than 90% for the ischemic beats.

174 citations


Cites methods from "A Real-Time QRS Detection Algorithm..."

  • ...Thepeak is detected using the Pan and Tompkins algorithm [3]....

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  • ...These techniques deal mainly with ECG pattern recognition [1], [3], [4], parameter extraction,...

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References
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Journal ArticleDOI
TL;DR: This review asserts that most one-channel QRS detectors described in the literature can be considered as having the same basic structure and a discussion of some of the current detection schemes is presented.
Abstract: The QRS detection algorithm is an essential part of any computer-based system for the analysis of ambulatory ECG recordings. This review asserts that most one-channel QRS detectors described in the literature can be considered as having the same basic structure. A discussion of some of the current detection schemes is presented with regard to this structure. Some additional features of QRS detectors are mentioned. The evaluation of performance and the problem of multichannel detection, which is now gaining importance, are also briefly treated.

254 citations

Journal ArticleDOI
TL;DR: The problem of detecting the QRS complex in the presence of noise was analysed and an optimised threshold criterion based on FP/FN was developed.
Abstract: The problem of detecting the QRS complex in the presence of noise was analysed. Most QRS detectors contain a filter to improve the signal-to-noise ratio and compare the signal with a threshold. In an earlier paper we identified an optimal filter. Various techniques to generate threshold and detector designs were studied. Automatic gain-control circuits with a fixed threshold have a very slow response to different rhythms. Automatic threshold circuits based on simple peak-detection schemes have a fast response, but are very sensitive to sudden variations in QRS amplitudes and noise transients. None of the methods described to date present any optimisation criteria for detecting the signal (QRS complex) in the presence of noise. The probabilities of FPs (false positives) and FNs (false negatives) were investigated and an optimised threshold criterion based on FP/FN was developed. Presently, data are being collected to compare various techniques from their ROC (receiver operating characteristics).

151 citations

Journal ArticleDOI
TL;DR: An automated Holtes scanning system based on two microcomputers that detects QRS complexes and measures the QRS durations using computations of first and second derivatives, and can process Holter tapes at 60 times real time and produce printed summaries and 24 h trend plots.
Abstract: We have developed an automated Holtes scanning system based on two microcomputers. One is a preprocessor that detects QRS complexes and measures the QRS durations using computations of first and second derivatives. Thismicrocomputer interfaces to a secondmicro-computer that does arrhythmia analysis, logging, and reporting using R-R intervals and QRS durations. This system can process Holter tapes at 60 times real time and produce printed summaries and 24 h trend plots of several variables including heart rate and PVC count.

127 citations


"A Real-Time QRS Detection Algorithm..." refers methods in this paper

  • ...The slope of the R wave is a popular signal feature used to locate the QRS complex in many QRS detectors [5]....

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Journal ArticleDOI
P. A. Lynn1
TL;DR: The possibilities for extending the class of lowpass recursive digital filters to include high pass, bandpass, and bandstop filters are described, and experience with a PDP 11 computer has shown that these filters may be programmed simply using machine code, and that online operation at sampling rates up to about 8 kHz is possible.
Abstract: After reviewing the design of a class of lowpass recursive digital filters having integer multiplier and linear phase characteristics, the possibilities for extending the class to include high pass, bandpass, and bandstop (‘notch’) filters are described. Experience with a PDP 11 computer has shown that these filters may be programmed simply using machine code, and that online operation at sampling rates up to about 8 kHz is possible. The practical application of such filters is illustrated by using a notch desgin to remove mains-frequency interference from an e.c.g. waveform.

104 citations

Journal ArticleDOI
TL;DR: In this paper a new robust single lead QRS-detection algorithm is presented, allowing real-time applications and results are presented.

101 citations