A comparison of the noise sensitivity of nine QRS detection algorithms
01 Jan 1990-IEEE Transactions on Biomedical Engineering (IEEE Trans Biomed Eng)-Vol. 37, Iss: 1, pp 85-98
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. >
Citations
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Abstract: Components of heart rate variability have attracted considerable attention in psychology and medicine and have become important dependent measures in psychophysiology and behavioral medicine. Quantification and interpretation of heart rate variability, however, remain complex issues and are fraught with pitfalls. The present report (a) examines the physiological origins and mechanisms of heart rate variability, (b) considers quantitative approaches to measurement, and (c) highlights important caveats in the interpretation of heart rate variability. Summary guidelines for research in this area are outlined, and suggestions and prospects for future developments are considered.
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1,490 citations
Additional excerpts
...Older detectors are reviewed in [2]–[4]....
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Cites background from "A comparison of the noise sensitivi..."
...It is the intention of the authors to provide an overview of these recent developments as well as of formerly proposed algorithms that were already reviewed in [34, 51, 84]....
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TL;DR: A finger-based ECG biometric system, that uses signals collected at the fingers, through a minimally intrusive 1-lead ECG setup recurring to Ag/AgCl electrodes without gel as interface with the skin, is proposed.
Abstract: The ECG signal has been shown to contain relevant information for human identification. Even though results validate the potential of these signals, data acquisition methods and apparatus explored so far compromise user acceptability, requiring the acquisition of ECG at the chest. In this paper, we propose a finger-based ECG biometric system, that uses signals collected at the fingers, through a minimally intrusive 1-lead ECG setup recurring to Ag/AgCl electrodes without gel as interface with the skin. The collected signal is significantly more noisy than the ECG acquired at the chest, motivating the application of feature extraction and signal processing techniques to the problem. Time domain ECG signal processing is performed, which comprises the usual steps of filtering, peak detection, heartbeat waveform segmentation, and amplitude normalization, plus an additional step of time normalization. Through a simple minimum distance criterion between the test patterns and the enrollment database, results have revealed this to be a promising technique for biometric applications.
783 citations
Cites methods from "A comparison of the noise sensitivi..."
...The QRS detection is performed following an adaptation of the Englese and Zeelenberg algorithm [19], found to be one of the more robust for this purpose [20]....
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...Instead of using the ones proposed in [20], we calculated thresholds through experimental analysis of the data....
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References
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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
Book•
01 Jan 1992
TL;DR: Basic Concepts of Medical Instrumentation (W. Olson).
Abstract: Basic Concepts of Medical Instrumentation Basic Sensors and Principles Amplifiers and Signal Processing The Origin of Biopotentials Biopotential Electrodes Biopotential Amplifiers Blood Pressure and Sound Measurement of Flow and Volume of Blood Measurements of the Respiratory System Chemical Biosensors Clinical Laboratory Instrumentation Medical Imaging Systems Therapeutic and Prosthetic Devices Electrical Safety.
1,674 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: The power spectral analysis shows that the QRS complex could be separated from other interfering signals, and it is observed that a bandpass filter with a center frequency of 17 Hz and a Q of 5 yields the best signal-to-noise ratio.
Abstract: We present power spectral analysis of ECG waveforms as well as isolated QRS complexes and episodes of noise and artifact. The power spectral analysis shows that the QRS complex could be separated from other interfering signals. A bandpass filter that maximizes the signal (QRS complex)-to-noise (T-waves, 60 Hz, EMG, etc.) ratio would be of use in many ECG monitoring instruments. We calculate the coherence function and, from that, the signal-to-noise ratio. Upon carrying out this analysis on experimentaly obtained ECG data, we observe that a bandpass filter with a center frequency of 17 Hz and a Q of 5 yields the best signal-to-noise ratio.
437 citations
TL;DR: Linear phase filtering is proposed for the removal of baseline wander and power-line frequency components in electrocardiograms with a considerably reduced number of impulse response coefficients.
Abstract: Linear phase filtering is proposed for the removal of baseline wander and power-line frequency components in electrocardiograms. In order to reduce the large number of computations involved in the digital filtering that are necessary, the desired filter spectrum was defined periodically. Making use of the property that the spectrum period is 50 Hz, the spectrum can be realized with a considerably reduced number of impulse response coefficients. This, in combination with the necessary impulse response symmetry, leads to a reduction in the number of multiplications per output sample by a factor of 10. A suitable impulse response is designed with a pass-band ripple of less than 0.5 dB and a high stop-band attenuation. The applicability is demonstrated by applying the filtering to exercise electrocardiograms.
365 citations