A robust-digital QRS-detection algorithm for arrhythmia monitoring
TL;DR: In this paper a new robust single lead QRS-detection algorithm is presented, allowing real-time applications and results are presented.
About: This article is published in Computers and Biomedical Research.The article was published on 1983-06-01. It has received 101 citations till now. The article focuses on the topics: Differentiator & QRS complex.
Citations
More filters
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: 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
Cites background or methods from "A robust-digital QRS-detection algo..."
...[53][54], Ligtenberg & Kunt [67], Poli et al....
[...]
...Algorithms Based on Digital Filters Algorithms based on more sophisticated digital filters were published in [12, 26, 29, 30, 41, 55, 65, 67, 81, 83, 85, 101, 106, 107, 123]....
[...]
...In [67] the feature signal z n ( ) is computed in a way similar to [41] and [85] but using different filters....
[...]
TL;DR: A real-time detection method for QRS and ventricular beat detection based on comparison between absolute values of summed differentiated electrocardiograms of one of more ECG leads and adaptive threshold, which is higher than, or comparable to, those cited in the scientific literature.
Abstract: QRS and ventricular beat detection is a basic procedure for electrocardiogram (ECG) processing and analysis. Large variety of methods have been proposed and used, featuring high percentages of correct detection. Nevertheless, the problem remains open especially with respect to higher detection accuracy in noisy ECGs A real-time detection method is proposed, based on comparison between absolute values of summed differentiated electrocardiograms of one of more ECG leads and adaptive threshold. The threshold combines three parameters: an adaptive slew-rate value, a second value which rises when high-frequency noise occurs, and a third one intended to avoid missing of low amplitude beats. Two algorithms were developed: Algorithm 1 detects at the current beat and Algorithm 2 has an RR interval analysis component in addition. The algorithms are self-adjusting to the thresholds and weighting constants, regardless of resolution and sampling frequency used. They operate with any number L of ECG leads, self-synchronize to QRS or beat slopes and adapt to beat-to-beat intervals. The algorithms were tested by an independent expert, thus excluding possible author's influence, using all 48 full-length ECG records of the MIT-BIH arrhythmia database. The results were: sensitivity Se = 99.69 % and specificity Sp = 99.65 % for Algorithm 1 and Se = 99.74 % and Sp = 99.65 % for Algorithm 2. The statistical indices are higher than, or comparable to those, cited in the scientific literature.
426 citations
Cites methods from "A robust-digital QRS-detection algo..."
...They reported Se = 99.04% and Sp = 99.62%, obtained with two channel recordings from AHA and MIT-BIH Arrhythmia Database Moraes et al. [1] combined logically two different algorithms working in parallel – the first has been taken from the work of Englese and Zeelenberg [7] and the other was based on Pan and Tompkins [8], and Ligtenberg and Kunt [9]....
[...]
...[1] combined logically two different algorithms working in parallel – the first has been taken from the work of Englese and Zeelenberg [7] and the other was based on Pan and Tompkins [8], and Ligtenberg and Kunt [9]....
[...]
TL;DR: The modified Hamilton-Tompkins algorithm as well as the Hilbert transform-based algorithms had comparable, though slightly lower, accuracy; yet these automated algorithms present an advantage for real-time applications by avoiding human intervention in threshold determination.
Abstract: Accurate QRS detection is an important first step for the analysis of heart rate variability Algorithms based on the differentiated ECG are computationally efficient and hence ideal for real-time analysis of large datasets Here, we analyze traditional first-derivative based squaring function (Hamilton-Tompkins) and Hilbert transform-based methods for QRS detection and their modifications with improved detection thresholds On a standard ECG dataset, the Hamilton-Tompkins algorithm had the highest detection accuracy (9968% sensitivity, 9963% positive predictivity) but also the largest time error The modified Hamilton-Tompkins algorithm as well as the Hilbert transform-based algorithms had comparable, though slightly lower, accuracy; yet these automated algorithms present an advantage for real-time applications by avoiding human intervention in threshold determination The high accuracy of the Hilbert transform-based method compared to detection with the second derivative of the ECG is ascribable to its inherently uniform magnitude spectrum For all algorithms, detection errors occurred mainly in beats with decreased signal slope, such as wide arrhythmic beats or attenuated beats For best performance, a combination of the squaring function and Hilbert transform-based algorithms can be applied such that differences in detection will point to abnormalities in the signal that can be further analyzed
407 citations
Cites methods from "A robust-digital QRS-detection algo..."
...The five-point derivative prevents high-frequency noise amplification [25]; in the present implementation high-frequency noise is further attenuated by the Kaiser Window filter....
[...]
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
References
More filters
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
01 Oct 1972
TL;DR: Computer analysis of the ECG has been directed toward morphological and rhythm diagnosis, having great potential utility in clinical heart stations, and toward rhythm monitoring, a most practical application arising in coronary intensive care units.
Abstract: The electroencephalogram (EEG), the blood pressure wave, and the electrocardiogram (ECG), produce patterns that the eye of the physician has empirically correlated with important aspects of health. Digital computer techniques for the recognition of these patterns are made particularly difficult by the realities of pattern context sensitivity, frequent signal artifact, real-time operation, finite storage limitations, and reasonable cost. Evaluations of these techniques are handicapped by the absence of absolute standards, the wide signal variability associated with pathologic states, and the sheer mechanics of comparison with human analysis. Computer analysis of the EEG has been directed toward monitoring sleep and certain pathologic states, leaving the more difficult problem of diagnosis to the trained neurologist. Automatic pattern recognition of the blood pressure wave has been implemented with straightforward techniques for diagnostic use in the cardiac catheterization laboratory and for monitoring in the intensive care unit. Computer analysis of the ECG has been directed toward morphological and rhythm diagnosis, having great potential utility in clinical heart stations, and toward rhythm monitoring, a most practical application arising in coronary intensive care units. Promising systems are emerging, but years of evaluation and adjustment will be necessary to meet the need for both accuracy and economy.
121 citations
TL;DR: The article describes a detector for physiological phenomena, e.g., the QRS-complex, having a trigger accuracy of 0.5 ms and having two amplifiers representing the upper and lower contour-limit derived from the actual signal.
Abstract: The article describes a detector for physiological phenomena, e.g., the QRS-complex, having a trigger accuracy of 0.5 ms. The configuration to be recognized first is preprocessed then fed into a pair of amplifiers with adjustable gain and offset, representing the upper and lower contour-limit derived from the actual signal. These signals are sampled and A/D converted, then stored in two memories during the ``read''-operation.
54 citations
TL;DR: A new scheme is proposed for the detection of premature ventricular beats, which is a vital function in rhythm monitoring of cardiac patients, and results of application to ECG of two arrhythmia patients are presented.
Abstract: A new scheme is proposed for the detection of premature ventricular beats, which is a vital function in rhythm monitoring of cardiac patients. A transformation based on the first difference of the digitized electrocardiogram (ECG) signal is developed for the detection and delineation of QRS complexes. The method for classifying the abnormal complexes from the normal ones is based on the concepts of minimum phase and signal length. The parameters of a linear discriminant function obtained from a training feature vector set are used to classify the complexes. Results of application of the scheme to ECG of two arrhythmia patients are presented.
49 citations
IBM1
TL;DR: The logic of the measurement program determines from the waveform the identity and nature of the various electrocardiogram complexes and identifies their amplitude and time-duration characteristics.
48 citations