Software QRS detection in ambulatory monitoring--a review.
01 Jul 1984-Medical & Biological Engineering & Computing (Kluwer Academic Publishers)-Vol. 22, Iss: 4, pp 289-297
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.
Citations
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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.
6,686 citations
TL;DR: A robust single-lead electrocardiogram (ECG) delineation system based on the wavelet transform (WT), outperforming the results of other well known algorithms, especially in determining the end of T wave.
Abstract: In this paper, we developed and evaluated a robust single-lead electrocardiogram (ECG) delineation system based on the wavelet transform (WT). In a first step, QRS complexes are detected. Then, each QRS is delineated by detecting and identifying the peaks of the individual waves, as well as the complex onset and end. Finally, the determination of P and T wave peaks, onsets and ends is performed. We evaluated the algorithm on several manually annotated databases, such as MIT-BIH Arrhythmia, QT, European ST-T and CSE databases, developed for validation purposes. The QRS detector obtained a sensitivity of Se=99.66% and a positive predictivity of P+=99.56% over the first lead of the validation databases (more than 980,000 beats), while for the well-known MIT-BIH Arrhythmia Database, Se and P+ over 99.8% were attained. As for the delineation of the ECG waves, the mean and standard deviation of the differences between the automatic and manual annotations were computed. The mean error obtained with the WT approach was found not to exceed one sampling interval, while the standard deviations were around the accepted tolerances between expert physicians, outperforming the results of other well known algorithms, especially in determining the end of T wave.
1,490 citations
Cites background from "Software QRS detection in ambulator..."
...Older detectors are reviewed in [2]–[4]....
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...A generalized scheme [2] that matches most nonsyntactic QRS detectors presents a two-stage structure: a preprocessing stage, usually including linear filtering followed by a nonlinear transformation, and the decision rule(s)....
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TL;DR: The authors provide an overview of these recent developments as well as of formerly proposed algorithms for QRS detection, which reflects the electrical activity within the heart during the ventricular contraction.
Abstract: The QRS complex is the most striking waveform within the electrocardiogram (ECG). Since it reflects the electrical activity within the heart during the ventricular contraction, the time of its occurrence as well as its shape provide much information about the current state of the heart. Due to its characteristic shape it serves as the basis for the automated determination of the heart rate, as an entry point for classification schemes of the cardiac cycle, and often it is also used in ECG data compression algorithms. In that sense, QRS detection provides the fundamentals for almost all automated ECG analysis algorithms. Software QRS detection has been a research topic for more than 30 years. The evolution of these algorithms clearly reflects the great advances in computer technology. Within the last decade many new approaches to QRS detection have been proposed; for example, algorithms from the field of artificial neural networks genetic algorithms wavelet transforms, filter banks as well as heuristic methods mostly based on nonlinear transforms. The authors provide an overview of these recent developments as well as of formerly proposed algorithms.
1,307 citations
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.
Abstract: We have investigated the quantitative effects of a number of common elements of QRS detection rules using the MIT/BIH arrhythmia database. A previously developed linear and nonlinear filtering scheme was used to provide input to the QRS detector decision section. We used the filtering to preprocess the database. This yielded a set of event vectors produced from QRS complexes and noise. After this preprocessing, we tested different decision rules on the event vectors. This step was carried out at processing speeds up to 100 times faster than real time. The role of the decision rule section is to discriminate the QRS events from the noise events. We started by optimizing a simple decision rule. Then we developed a progressively more complex decision process for QRS detection by adding new detection rules. We implemented and tested a final real-time QRS detection algorithm, using the optimized decision rule process. The resulting QRS detection algorithm has a sensitivity of 99.69 percent and positive predictivity of 99.77 percent when evaluated with the MIT/BIH arrhythmia database.
1,137 citations
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.
Abstract: Several adaptive filter structures are proposed for noise cancellation and arrhythmia detection. The adaptive filter essentially minimizes the mean-squared error between a primary input, which is the noisy electrocardiogram (ECG), and a reference input, which is either noise that is correlated in some way with the noise in the primary input or a signal that is correlated only with ECG in the primary input. Different filter structures are presented to eliminate the diverse forms of noise: baseline wander, 60 Hz power line interference, muscle noise, and motion artifact. An adaptive recurrent filter structure is proposed for acquiring the impulse response of the normal QRS complex. The primary input of the filter is the ECG signal to be analyzed, while the reference input is an impulse train coincident with the QRS complexes. This method is applied to several arrhythmia detection problems: detection of P-waves, premature ventricular complexes, and recognition of conduction block, atrial fibrillation, and paced rhythm. >
902 citations
References
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TL;DR: In this article, a five-step digital filter was developed which removes components other than those of QRS complex from the recorded electrocardiogram (ECG), and the final step of the filter produces a square wave whose on-intervals correspond to the segments with QRS complexes in the original wave.
Abstract: The five step digital filter has been developed which removes components other than those of QRS complex from the recorded electrocardiogram (ECG). The final step of the filter produces a square wave whose on-intervals correspond to the segments with QRS complexes in the original wave.
205 citations
TL;DR: A new algorithm for QRS delineation has been developed and the stability of the method is demonstrated for transitions between different waveform morphologies.
Abstract: A new algorithm for QRS delineation has been developed. Based on the envelope of the e.c.g. signal a delineation function is defined, which yields a single positive pulse for each complex. From this function the onset and end of the QRS or, alternatively, a fiducial point is determined. To remove low-frequency component such as S-T abnormalities without distortion of the QRS complex, a filter with time-varying characteristics is used. The accuracy of the method has been evaluated in a test set of different QRS complexes obtained from coronary care patients. For QRS onset, the standard deviation of the difference between automated and manual determination was 7 ms in normal beats and 14 ms in ectopic beats. With simulated noise added to each waveform an average dispersion of 7 ms was observed in the recognition of the QRS onset at a signal-to-noise ratio of 15 dB. The corresponding dispersion in the location of a fiducial point was 2 ms. Using simulated e.c.g. data, the stability of the method is demonstrated for transitions between different waveform morphologies.
152 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: A preprocessor that detects in real time an electrocardiogram QRS complex and computes the R-R interval is described, which is encouraging as an ultrareliable means for locating the QRS.
Abstract: Digital preprocessors can ease the increasing data collection demands placed on real-time computers in patient monitoring. This paper describes a preprocessor that detects in real time an electrocardiogram QRS complex and computes the R-R interval. Detection is performed using multiple digital differentiation, which is encouraging as an ultrareliable means for locating the QRS. Inherent in the technique is a dependable control that can automatically compensate for signal-level variations. Clinical data demonstrate that detection is insensitive to low- and high-frequency noise, from baseline drift to muscle artifact and cautery bursts. The device can be connected directly to a patient, whose safety is guaranteed by optoelectronic isolation and interelectrode current limiting. Preprocessor operation has been human-engineered to a simple on/off procedure.
116 citations