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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01 Aug 2011
TL;DR: A QRS detection method for wearable Electrocardiogram (ECG) sensor in body sensor networks using single-scale mathematical morphological filter and approximated envelope based on the combination of two computationally efficient procedures.
Abstract: Emerging body sensor networks (BSN) provide solutions for continuous health monitoring at anytime and from anywhere. The implementation of these monitoring solutions requires wearable sensor devices and thus creates new technology challenges in both software and hardware. This paper presents a QRS detection method for wearable Electrocardiogram (ECG) sensor in body sensor networks. The success of proposed method is based on the combination of two computationally efficient procedures, i.e., single-scale mathematical morphological (MM) filter and approximated envelope. The MM filter removes baseline wandering, impulsive noise and the offset of DC component while the approximated envelope enhances the QRS complexes. The performance of the algorithm is verified with standard MIT-BIH arrhythmia database as well as exercise ECG data. It achieves a low detection error rate of 0.42% based on the MIT-BIH database.
40 citations
TL;DR: The proposed method was an efficient, reliable, and accurate approach to identify the respiratory phases of SCG cycles and can be employed to enhance the extraction of clinically valuable information such as systolic time intervals.
Abstract: Goal: the objective of this study was to develop a method to identify respiratory phases (i.e., inhale or exhale) of seismocardiogram (SCG) cycles. An SCG signal is obtained by placing an accelerometer on the sternum to capture cardiac vibrations. Methods: SCGs from 19 healthy subjects were collected, preprocessed, segmented, and labeled. To extract the most important features, each SCG cycle was divided to equal-sized bins in time and frequency domains, and the average value of each bin was defined as a feature. Support vector machines was employed for feature selection and identification. The features were selected based on the total accuracy. The identification was performed in two scenarios: leave-one-subject-out (LOSO), and subject-specific (SS). Results: time-domain features resulted in better performance. The time-domain features that had higher accuracies included the characteristic points correlated with aortic-valve opening, aortic-valve closure, and the length of cardiac cycle. The average total identification accuracies were 88.1% and 95.4% for LOSO and SS scenarios, respectively. Conclusion: the proposed method was an efficient, reliable, and accurate approach to identify the respiratory phases of SCG cycles. Significance: The results obtained from this study can be employed to enhance the extraction of clinically valuable information such as systolic time intervals.
40 citations
Cites methods from "A Real-Time QRS Detection Algorithm..."
...[19] R. Bailón et al., “A robust method for ECG-based estimation of the respiratory frequency during stress testing,” IEEE Trans....
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...The Pan–Tompkins algorithm was used to detect the R-peaks of ECG [26]....
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...[12] R. P. Paiva et al., “Beat-to-beat systolic time-interval measurement from heart sounds and ECG,” Physiol....
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...0018-9294 © 2016 Canadian Crown Copyright There exist a body of work using electrocardiogram (ECG) or SCG to extract respiratory information such as respiration rate, but none of them have investigated the phase identification of cardiac cycles [18]–[21]....
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...The ECG, SCG, and respiratory signals were all measured simultaneously at 1000 Hz, while the subjects were in the resting supine position....
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Patent•
02 Feb 2004
TL;DR: In this article, the authors present a method for managing biological signals that includes information describing events, determining a merit of each event based on one or more of a severity of a cardiac condition associated with the event and a quality of the event, and handling a subset of the events that meet a merit criterion.
Abstract: Systems and techniques for managing biological signals. In one implementation, a method includes receiving a cardiac biological signal that includes information describing events, determining a merit of each event based on one or more of a severity of a cardiac condition associated with the event and a quality of the event, and handling a subset of the events that meet a merit criterion. The subset can be handled for medical purposes.
40 citations
TL;DR: A novel approach to detect sleep apnea using both HBI and EDR signals and the combination of the statistical features and SVM classifier has the sensitivity and specificity values of 82.45% and 79.72%, respectively using the 10-fold cross-validation based selection of training and test instances from the apnea-ECG database.
40 citations
TL;DR: In this paper, heart rate variability (HRV) is used as an alternative indicator of thermal comfort status to predict human thermal states (cold, neutral, and hot) with up to 93.7% accuracy.
Abstract: Thermal comfort is an assessment of one’s satisfaction with the surroundings; yet, most mechanisms that are used to provide thermal comfort are based on approaches that preclude physiological, psychological and personal psychophysics that are precursors to thermal comfort. This leads to many people feeling either cold or hot in an environment that was supposed to be thermally comfortable to most users. To address this problem, this paper proposes to use heart rate variability (HRV) as an alternative indicator of thermal comfort status. Since HRV is linked to homeostasis, we conjectured that people’s thermal comfort could be more accurately estimated based on their heart rate variability (HRV). To test our hypothesis, we analyzed statistical, spectral, and nonlinear HRV indices of 17 human subjects doing light office work in a cold, a neutral, and a hot environment. The resulting HRV indices were used as inputs to machine learning classification algorithms. We observed that HRV is distinctively different depending on the thermal environment and that it is possible to reliably predict each subject’s thermal state (cold, neutral, and hot) with up to a 93.7% accuracy. The result of this study suggests that it could be possible to design automatic real-time thermal comfort controllers based on people’s HRV.
40 citations
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