A real-time microprocessor QRS detector system with a 1-ms timing accuracy for the measurement of ambulatory HRV
01 Mar 1997-IEEE Transactions on Biomedical Engineering (IEEE Trans Biomed Eng)-Vol. 44, Iss: 3, pp 159-167
TL;DR: The design, test methods, and results of an ambulatory QRS detector are presented and the aim of the design work was to achieve high QRS detection performance in terms of timing accuracy and reliability, without compromising the size and power consumption of the device.
Abstract: The design, test methods, and results of an ambulatory QRS detector are presented. The device is intended for the accurate measurement of heart rate variability (HRV) and reliable QRS detection in both ambulatory and clinical use. The aim of the design work was to achieve high QRS detection performance in terms of timing accuracy and reliability, without compromising the size and power consumption of the device. The complete monitor system consists of a host computer and the detector unit. The detector device is constructed of a commonly available digital signal processing (DSP) microprocessor and other components. The QRS detection algorithm uses optimized prefiltering in conjunction with a matched filter and dual edge threshold detection. The purpose of the prefiltering is to attenuate various noise components in order to achieve improved detection reliability. The matched filter further improves signal-to-noise ratio (SNR) and symmetries the QRS complex for the threshold detection, which is essential in order to achieve the desired performance. The decision for detection is made in real-time and no search-back method is employed. The host computer is used to configure the detector unit, which includes the setting of the matched filter impulse response, and in the retrieval and postprocessing of the measurement results. The QRS detection timing accuracy and detection reliability of the detector system was tested with an artificially generated electrocardiogram (EGG) signal corrupted with various noise types and a timing standard deviation of less than 1 ms was achieved with most noise types and levels similar to those encountered in real measurements. A QRS detection error rate (ER) of 0.1 and 2.2% was achieved with records 103 and 105 from the MIT-BIH Arrhythmia database, respectively.
Citations
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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
Cites background or methods from "A real-time microprocessor QRS dete..."
...Matched Filters Besides the neural-network-based matched filtering approach in [122], there are linear matched filtering approaches as, for example, reported in [94, 27, 69, 25]....
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...In [94], after some analog preprocessing steps such as an automatic gain control, the ECG signal is digitized and further processed by a comb filter (low pass) with a notch at 50 Hz and a bandpass filter with cut-off frequencies at 15 Hz and 40 Hz....
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...It is reported in [94] that the matched filter also improves the timing accuracy of the detected R-wave....
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TL;DR: There appears to be a small day-to-day variability in HR and a steady increase during exercise has been observed in most studies, and the effects of overreaching on submaximal HR are controversial, with some studies showing decreased rates and others no difference.
Abstract: Over the last 20 years, heart rate monitors (HRMs) have become a widely used training aid for a variety of sports. The development of new HRMs has also evolved rapidly during the last two decades. In addition to heart rate (HR) responses to exercise, research has recently focused more on heart rate variability (HRV). Increased HRV has been associated with lower mortality rate and is affected by both age and sex. During graded exercise, the majority of studies show that HRV decreases progressively up to moderate intensities, after which it stabilises. There is abundant evidence from cross-sectional studies that trained individuals have higher HRV than untrained individuals. The results from longitudinal studies are equivocal, with some showing increased HRV after training but an equal number of studies showing no differences. The duration of the training programmes might be one of the factors responsible for the versatility of the results. HRMs are mainly used to determine the exercise intensity of a training session or race. Compared with other indications of exercise intensity, HR is easy to monitor, is relatively cheap and can be used in most situations. In addition, HR and HRV could potentially play a role in the prevention and detection of overtraining. The effects of overreaching on submaximal HR are controversial, with some studies showing decreased rates and others no difference. Maximal HR appears to be decreased in almost all ‘overreaching’ studies. So far, only few studies have investigated HRV changes after a period of intensified training and no firm conclusions can be drawn from these results. The relationship between HR and oxygen uptake (VO2) has been used to predict maximal oxygen uptake (VO2max). This method relies upon several assumptions and it has been shown that the results can deviate up to 20% from the true value. The HR-VO2 relationship is also used to estimate energy expenditure during field conditions. There appears to be general consensus that this method provides a satisfactory estimate of energy expenditure on a group level, but is not very accurate for individual estimations. The relationship between HR and other parameters used to predict and monitor an individual’s training status can be influenced by numerous factors. There appears to be a small day-to-day variability in HR and a steady increase during exercise has been observed in most studies. Furthermore, factors such as dehydration and ambient temperature can have a profound effect on the HR-VO2 relationship.
901 citations
TL;DR: A computer program for advanced heart rate variability analysis that calculates all the commonly used time- and frequency-domain measures of HRV as well as the nonlinear Poincaré plot and parametric and nonparametric spectrum estimates are calculated.
751 citations
Cites methods from "A real-time microprocessor QRS dete..."
...The timing accuracy of the build-in QRS detection system was studied in [8]....
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Patent•
12 May 2003
TL;DR: In this paper, an implantable signal generator, one or more implantable electrodes for electrically stimulating a predetermined stimulation site of the vagus nerve, and a sensor for sensing characteristics of the heart such as heart rate are used to determine whether the vagu nerve stimulation is adversely affecting the heart.
Abstract: The present invention uses electrical stimulation of the vagus nerve to treat epilepsy with minimized or no effect on the heart. Treatment is carried out by an implantable signal generator, one or more implantable electrodes for electrically stimulating a predetermined stimulation site of the vagus nerve, and a sensor for sensing characteristics of the heart such as heart rate. The heart rate information from the sensor can be used to determine whether the vagus nerve stimulation is adversely affecting the heart. Once threshold parameters are met, the vagus nerve stimulation may be stopped or adjusted. In an alternative embodiment, the invention may include a modified pacemaker to maintain the heart in desired conditions during the vagus nerve stimulation. In yet another embodiment, the invention may be simply a modified pacemaker having circuitry that determines whether a vagus nerve is being stimulated. In the event that the vagus nerve is being stimulated, the modified pacemaker may control the heart to maintain it within desired conditions during the vagus nerve stimulation.
602 citations
TL;DR: Narrow LoA, good correlations, and small effect sizes support the validity of the Polar S810 HRM to measure R-R intervals and make the subsequent HRV analysis in supine position.
Abstract: This study was conducted to compare R-R intervals and the subsequent analysis of heart rate variability (HRV) obtained from the Polar S810 heart rate monitor (HRM) (Polar Electro Oy) with an electrocardiogram (ECG) (Physiotrace, Estaris, Lille, France) during an orthostatic test. A total of 18 healthy men (age: 27.1 +/- 1.9 yr; height: 1.82 +/- 0.06 m; mass 77.1 +/- 7.7 kg) performed an active orthostatic test during which R-R intervals were simultaneously recorded with the HRM and the ECG recorder The two signals were synchronized and corrected before a time domain analysis, the fast Fourier transform (FFT) and a Poincare plot analysis. Bias and limits of agreement (LoA), effect size (ES), and correlation coefficients were calculated. R-R intervals were significantly different in the supine and standing position between the ECG and the HRM uncorrected and corrected signal (P 0.05) and well correlated (r > 0.97, P < 0.05), except root mean square of difference (RMSSD) and SD1 in standing position (P < 0.05, ES = 0.052 and 0.057; r = 0.99 and 0.98, respectively). Narrow LoA, good correlations, and small effect sizes support the validity of the Polar S810 HRM to measure R-R intervals and make the subsequent HRV analysis in supine position. Caution must be taken in standing position for the parameters sensitive to the short-term variability (i.e., RMSSD and SD1).
528 citations
References
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
"A real-time microprocessor QRS dete..." refers methods in this paper
...2% and number of failed detections (FD) of 56 was better than with linear bandpass filtering method [15] as expected (ER 3....
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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
"A real-time microprocessor QRS dete..." refers methods in this paper
...9%, FD 75), even when a search-back algorithm was used [14]....
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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.
Abstract: 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. The percentage of QRS complexes detected, the number of false positives, and the detection delay were measured. None of the algorithms were able to detect all QRS complexes without any false positives for all of the noise types at the highest noise level. Algorithms based on amplitude and slope had the highest performance for EMG-corrupted ECG. An algorithm using a digital filter had the best performance for the composite-noise-corrupted data. >
1,083 citations
"A real-time microprocessor QRS dete..." refers methods in this paper
...In [9] a sampled ECG signal was used to test the QRS detection performance of nine published algorithms....
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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
"A real-time microprocessor QRS dete..." refers methods in this paper
...This is not the case, however, with noisy ECG signals where the noise characteristics, mainly motion artifacts, can vary considerably and adaptive filter probably fails to be the optimal filtering method [ 16 ]....
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Book•
31 Jan 1993
TL;DR: 1 Probability 2 The Random Variable 3 Operations on one Random Variable--Expectation 4 Multiple Random Variables 5 Operations of Multiple Randomvariables 6 Random Processes-Temporal Characteristics 7 Random processes-Spectral Characteristics 8 Linear Systems with Random Inputs 9 Optimum Linear Systems 10 Some Practical Applications of the Theory.
Abstract: 1 Probability 2 The Random Variable 3 Operations on one Random Variable--Expectation 4 Multiple Random Variables 5 Operations of Multiple Random Variables 6 Random Processes-Temporal Characteristics 7 Random Processes-Spectral Characteristics 8 Linear Systems with Random Inputs 9 Optimum Linear Systems 10 Some Practical Applications of the Theory Appendix A Review of the Impulse Function Appendix B Gaussian Distribution Function Appendix C Useful Mathematical Quantities Appendix D Review of Fourier Transforms Appendix E Table of Useful Fourier Transforms Appendix F Some Probability Densities and Distributions Appendix G Some Mathematical Topics of Interest
685 citations
"A real-time microprocessor QRS dete..." refers background in this paper
...The matched filter frequency response for a signal corrupted with normal distributed white noise (Gaussian) is [3]...
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...The matched filter frequency response for a signal corrupted with colored noise is [3]...
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