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.
Citations
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Proceedings Article•
01 Sep 2006TL;DR: It was found that humans are inconsistent and almost always under-estimate the T-offset (if defined to be the end of any repolarization), so an alternative QT end point is proposed, which is probably due to the truncation of any human estimation when the T wave tail is consumed by noise.
Abstract: A method is presented to determine the QT interval by fitting a nonlinear artificial ECG model to segmented regions of a human ECG. The model consists of a set of temporally Gaussian functions with different widths and heights. These parameters are fitted to a given ECG (segmented around the QRS complex to include the P and T wave) using a nonlinear least squares optimization routine. The Q onset and T offset can be determined precisely (in a statistical sense) from the parameters of the Gaussian. Since the fitted waveform contains no noise, the differential is smooth. Waveform boundaries can also be determined by searching for the minimum of a differential. Furthermore, the residual error provides an estimate of the confidence in the fit, and hence, the derived QT interval. Using the human expert-annotated PhysioNet QT database, various QT interval estimation schemes were compared using the model-fitted ECG to find an optimal marker of the QT interval. It was found that humans are inconsistent and almost always under-estimate the T-offset (if defined to be the end of any repolarization). This is probably due to the truncation of any human estimation when the T wave tail is consumed by noise. We therefore propose an alternative QT end point. Finally, an entry based on the most favourable technique was submitted in the PhysioNet / Computers in Cardiology Challenge 2006; QT Interval Measurement, which is intended to produce a comparison of several automatic and human annotators on the Physikalisch-Technische Bundesanstalt diagnostic ECG database. A follow-up paper to address differences between those generated by our method and the consensus of the other entries will be submitted shortly.
33 citations
Cites methods from "A Real-Time QRS Detection Algorithm..."
...• QRS detection: A standard QRS detector was used [4] that locates a maximum in a smoothed, differentiated, squared and integrated ECG....
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TL;DR: Association between sleep HRV and long-term CVD outcomes was demonstrated here, suggesting that altered HRV during sleep might occur many years prior to the onset of CVD.
33 citations
Proceedings Article•
01 Sep 2014TL;DR: The developed algorithm presents a promising approach to detect heart beats in multivariate records and had the best score applied to the hidden Phase 1 dataset of the 2014 PhysioNetlCinC challenge.
Abstract: Background: This contribution relates to the PhysioNet/CinC Challenge 2014 on Robust Detection of Heart Beats in Multimodal Data. The aim is to locate heart beats in continuous long-term data.
33 citations
Cites methods from "A Real-Time QRS Detection Algorithm..."
...Results: The algorithm was tested on the training data set for this challenge (one hundred 10-minute recordings) and on several freely available PhysioNet databases which were annotated by physicians....
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TL;DR: This work presents a multimodal sensor setup integrated into an armchair that combines capacitively coupled electrocardiography, reflective photoplethysmography, two high-frequency impedance sensors and two types of ballistocardiography sensors, and develops and evaluates an optimal combination of sensors and fusion methodology.
Abstract: Sensors integrated into objects of everyday life potentially allow unobtrusive health monitoring at home. However, since the coupling of sensors and subject is not as well-defined as compared to a clinical setting, the signal quality is much more variable and can be disturbed significantly by motion artifacts. One way of tackling this challenge is the combined evaluation of multiple channels via sensor fusion. For robust and accurate sensor fusion, analyzing the influence of motion on different modalities is crucial. In this work, a multimodal sensor setup integrated into an armchair is presented that combines capacitively coupled electrocardiography, reflective photoplethysmography, two high-frequency impedance sensors and two types of ballistocardiography sensors. To quantify motion artifacts, a motion protocol performed by healthy volunteers is recorded with a motion capture system, and reference sensors perform cardiorespiratory monitoring. The shape-based signal-to-noise ratio SNR S is introduced and used to quantify the effect on motion on different sensing modalities. Based on this analysis, an optimal combination of sensors and fusion methodology is developed and evaluated. Using the proposed approach, beat-to-beat heart-rate is estimated with a coverage of 99.5% and a mean absolute error of 7.9 ms on 425 min of data from seven volunteers in a proof-of-concept measurement scenario.
33 citations
Cites methods from "A Real-Time QRS Detection Algorithm..."
...In addition, an implementation of the Pan and Tompkins (P&T) algorithm [29] was used to localize R-peaks in the capacitive ECG and derive the respective intervals ∆rP&T....
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02 Sep 2013
TL;DR: Results show that the two coupling measures can be used to predict collaborative processes such as grounding and convergence, a major step toward the development of remote collaborative interfaces able to adapt to the users' social interactions.
Abstract: In this paper we propose a method to assess key collaborative processes during computer-supported group work based on physiological signals and eye-movements. Synchronous interpersonal multimodal signals from 30 dyads were recorded while collaborating remotely. Features measuring how much collaborators' eye-movements and physiology are coupled were extracted from the obtained time series and two regression models were trained to assess collaboration. Results show that the two coupling measures can be used to predict collaborative processes such as grounding and convergence. Assessing those processes is a major step toward the development of remote collaborative interfaces able to adapt to the users' social interactions.
33 citations
Cites methods from "A Real-Time QRS Detection Algorithm..."
...The ECG peaks were identified using the PanTompkins algorithm [24] and IBI (Inter-Beat Intervals) were computed by measuring the time elapsed between all pairs of consecutive peaks....
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References
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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
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
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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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
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