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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TL;DR: A novel system which exploits multi-stage features from a trained convolutional neural network and precisely combines these features with a selection of handcrafted features which achieves a superior classification accuracy compared to all of the state-of-the-art ECG classification methods.
Abstract: Fusion of feature descriptors extracted from a signal through different methods is an important issue for the exploitation of representational power of each descriptor. In this research work, a novel system which exploits multi-stage features from a trained convolutional neural network (CNN) and precisely combines these features with a selection of handcrafted features is proposed. The set of handcrafted features consists of three subsets namely, wavelet transform based morphological features representing localized signal behaviour, statistical features exhibiting overall variational characteristics of the signal and temporal features representing the signal's behaviour on the time axis. The proposed system utilizes a novel decision-level fusion of features for ECG classification by three different approaches; the first one uses normalized feature-level fusion of handcrafted global statistical and local temporal features by uniting these features into one set, the second one uses the morphological feature subset, and the third one combines features extracted from multiple layers of a CNN through using a score-level based refinement procedure. The main impact of the proposed approach is the score-level based fusion of automatically learned features extracted from multiple layers of trained CNN and the decision-level fusion of features characterising the signal in totally different representational spaces. The individual decisions of the three different classifiers are fused together based on the majority voting and a unified decision is reached for the input ECG signal classification. The results over the MIT-BIH arrhythmia benchmarks database exhibited that the proposed system achieves a superior classification accuracy compared to all of the state-of-the-art ECG classification methods.
74 citations
TL;DR: The 15th annual PhysioNet/CinC Challenge aims to encourage the exploration of robust methods for locating heart beats in continuous long-term data from bedside monitors and similar devices that record not only ECG but usually other physiologic signals as well, including pulsatile signals that directly reflect cardiac activity and other signals that may have few or no observable markers of heart beats.
Abstract: This editorial reviews the background issues, the design, the key achievements, and the follow-up research generated as a result of the PhysioNet/Computing in Cardiology (CinC) Challenge 2014, published in the concurrent focus issue of Physiological Measurement. Our major focus was to accelerate the development and facilitate the comparison of robust methods for locating heart beats in long-term multi-channel recordings. A public (training) database consisting of 151 032 annotated beats was compiled from records that contained ECGs as well as pulsatile signals that directly reflect cardiac activity, and other signals that may have few or no observable markers of heart beats. A separate hidden test data set (consisting of 152 478 beats) is permanently stored at PhysioNet, and a public framework has been developed to provide researchers with the ability to continue to automatically score and compare the performance of their algorithms. A scoring criteria based on the averaging of gross sensitivity, gross positive predictivity, average sensitivity, and average positive predictivity is proposed. The top three scores (as of March 2015) on the hidden test data set were 93.64%, 91.50%, and 90.70%.
74 citations
Cites background or methods from "A Real-Time QRS Detection Algorithm..."
...The typical approach (and that of Pan and Tompkins (1985)) is the sequential application of a difference filter (to amplify steep waveforms i.e. the QR and RS slopes), a squaring operation (to amplify peaks and act as a full wave rectifier), and finally a windowed average (to reduce noise)....
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...Open source peak detectors have been available for the ECG for decades Pan and Tompkins (1985), and similarly for the ABP Zong, Heldt, Moody and Mark (2003)....
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...Finally, Pangerc and Jager (2014) used a similar approach to Pan and Tompkins (1985), with an addition of morphological smoothing to improve robustness against noise....
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...…waveforms has a long history of accomplishments (see, for instance, Pahlm and Sörnmo (1984); Hamilton and Tompkins (1986); Portet et al. (2005); Pan and Tompkins (1985); Kohler et al. (2002); Zong, Heldt, Moody and Mark (2003); Zong, Moody and Jiang (2003); Starmer et al. (1973); Okada (1979);…...
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TL;DR: The results indicate that the assessment of the quality of single-lead ECG recordings, acquired in unsupervised telehealth environments, is entirely feasible and may help to promote the acceptance and utility of future decision support systems for remotely managing chronic disease conditions.
Abstract: The use of telehealth paradigms for the remote management of patients suffering from chronic conditions has become more commonplace with the advancement of Internet connectivity and enterprise software systems. To facilitate clinicians in managing large numbers of telehealth patients, and in digesting the vast array of data returned from the remote monitoring environment, decision support systems in various guises are often utilized. The success of decision support systems in interpreting patient conditions from physiological data is dependent largely on the quality of these recorded data. This paper outlines an algorithm to determine the quality of single-lead electrocardiogram (ECG) recordings obtained from telehealth patients. Three hundred short ECG recordings were manually annotated to identify movement artifact, QRS locations and signal quality (discrete quality levels) by a panel of three experts, who then reconciled the annotation as a group to resolve any discrepancies. After applying a published algorithm to remove gross movement artifact, the proposed method was then applied to estimate the remaining ECG signal quality, using a Parzen window supervised statistical classifier model. The three-class classifier model, using a number of time-domain features and evaluated using cross validation, gave an accuracy in classifying signal quality of 78.7% (κ = 0.67) when using fully automated preprocessing algorithms to remove gross motion artifact and detect QRS locations. This is a similar level of accuracy to the reported human inter-scorer agreement when generating the gold standard annotation (accuracy = 70-89.3%, κ = 0.54-0.84). These results indicate that the assessment of the quality of single-lead ECG recordings, acquired in unsupervised telehealth environments, is entirely feasible and may help to promote the acceptance and utility of future decision support systems for remotely managing chronic disease conditions.
74 citations
TL;DR: It is predicted that large-scale data-driven analytics could lead to huge benefits in health care; in the United States, where healthcare spending is 18% of gross domestic product, up to US$600 per person could be saved annually.
Abstract: W ith the digitization of all records and processes, and prevalence of cloud-driven services and Internet of Things, today’s era can truly be considered as an era of data. Machine learning (ML) and artificial intelligence (AI) skills are among the most sought-after skills today. McKinsey Global Institute research suggests that 45% of workplace activities in corporations could be automated with current technologies; 80% of that is attributable to existing ML capabilities, and breakthroughs in natural language processing could further the impact. Gartner forecasts that large-scale data-driven analytics could lead to huge benefits in health care; in the United States, where healthcare spending is 18% of gross domestic product, up to US$600 per person could be saved annually. Gartner also forecasts that data-driven insights for demand-supply matching could create an economic impact of $850 billion to $2.5 trillion. International Data Corporation forecasts that spending on AI and ML will grow to $79.2 billion by 2022, with a compound annual growth rate of 38% between the 2018 and 2022 period.
73 citations
TL;DR: This review discusses the physiological significance of heart rate variations in non-mammalian vertebrates by discussing the different steps of the technique, its limitations and the ways to overcome them.
Abstract: Heart rate variations reflect the output of the complex control of the heart mediated by the autonomic nervous system. Because of that, they also encode different types of information, namely the efferent outflow of reflex mechanisms involved in the beat-to-beat control of cardiac function, the efferent activity of neurohumoral elements involved in the control of other cardiovascular parameters and random noise resulting from the hysteresis of the different controllers. The degree to which power spectrum estimation methods will uncover the periodic component of heart rate variations is in direct relation with the status of the system under study. Although the utility of spectral methods is now established in mammalian research, very little is known on the utility of these techniques in non-mammalian cardiovascular research. This review covers this space by discussing the physiological significance of heart rate variations in non-mammalian vertebrates. A detailed account of the different steps of the technique, its limitations and the ways to overcome these problems are also presented. These are: the recording of the cardiac event signal, the detection and digital processing methods, the satisfaction of stationarity conditions, the problem of spectral leakage and the different methods to estimate the power spectrum.
73 citations
Cites background from "A Real-Time QRS Detection Algorithm..."
...Real-time detection (on-line) with analog or digital tachographs [56] is convenient in terms of storage capacity because only the time of each event is stored....
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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