Journal ArticleDOI
Optimal QRS detector.
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TLDR
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).read more
Citations
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Journal ArticleDOI
A Real-Time QRS Detection Algorithm
Jiapu Pan,Willis J. Tompkins +1 more
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.
Journal ArticleDOI
Quantitative Investigation of QRS Detection Rules Using the MIT/BIH Arrhythmia Database
TL;DR: This work implemented and tested a final real-time QRS detection algorithm, using the optimized decision rule process, which has a sensitivity of 99.69 percent and positive predictivity of 98.77 percent when evaluated with the MIT/BIH arrhythmia database.
Journal ArticleDOI
A comparison of the noise sensitivity of nine QRS detection algorithms
TL;DR: The noise sensitivities of nine different QRS detection algorithms were measured for a normal, single-channel, lead-II, synthesized ECG corrupted with five different types of synthesized noise: electromyographic interference, 60-Hz power line interference, baseline drift due to respiration, abrupt baseline shift, and a composite noise constructed from all of the other noise types.
Journal ArticleDOI
Applications of adaptive filtering to ECG analysis: noise cancellation and arrhythmia detection
Nitish V. Thakor,Y.-S. Zhu +1 more
TL;DR: Several adaptive filter structures are proposed for noise cancellation and arrhythmia detection and an adaptive recurrent filter structure is proposed for acquiring the impulse response of the normal QRS complex.
Journal ArticleDOI
ECG beat detection using filter banks
TL;DR: A multirate digital signal processing algorithm to detect heartbeats in the electrocardiogram (ECG) which incorporates a filter bank which decomposes the ECG into subbands with uniform frequency bandwidths and inherently lends itself to a computationally efficient structure.
References
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Journal ArticleDOI
High-resolution determination of the R-R interval
TL;DR: A preprocessor is described that determines the time interval between successive R waves of the electrocardiogram with an error of less than 0.2 ms, making it possible to detect abnormal heart rate patterns characterized by exceedingly small beat-to-beat variability.
Cardiac R-wave detector with automatic sensitivity control
V. D. Gebben,J. A. Webb +1 more
TL;DR: An electronic circuit that automatically changes its sensitivity was developed for detecting the bioelectric signal resulting from activation of the heart's ventricles with two feedback channels that maintain the sensitivity level between an upper and a lower limit proportional to the R-wave amplitude.