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
More filters
TL;DR: It is demonstrated that HRV can also be extracted from photoplethysmograms obtained by the camera of a smartphone, and results suggest that the smartphone might be used for HRV measurement.
Abstract: Heart rate variability (HRV) is a useful clinical tool for autonomic function assessment and cardiovascular diseases diagnosis. It is traditionally calculated from a dedicated medical electrocardiograph (ECG). In this paper, we demonstrate that HRV can also be extracted from photoplethysmograms (PPG) obtained by the camera of a smartphone. Sixteen HRV parameters, including time-domain, frequency-domain, and nonlinear parameters, were calculated from PPG captured by a smartphone for 30 healthy subjects and were compared with those derived from ECG. The statistical results showed that 14 parameters (AVNN, SDNN, CV, RMSSD, SDSD, TP, VLF, LF, HF, LF/HF, nLF, nHF, SD1, and SD2) from PPG were highly correlated (r > 0.7, P < 0.001) with those from ECG, and 7 parameters (AVNN, TP, VLF, LF, HF, nLF, and nHF) from PPG were in good agreement with those from ECG within the acceptable limits. In addition, five different algorithms to detect the characteristic points of PPG wave were also investigated: peak point (PP), valley point (VP), maximum first derivative (M1D), maximum second derivative (M2D), and tangent intersection (TI). The results showed that M2D and TI algorithms had the best performance. These results suggest that the smartphone might be used for HRV measurement.
132 citations
Cites methods from "A Real-Time QRS Detection Algorithm..."
...The ECG signals were first passed through a finite impulse response (FIR) low-pass filter with cutoff frequency of 11Hz and then a FIR high-pass filter with cutoff frequency of 5Hz to reduce most of the noise and interference [26]....
[...]
...R-wave peak detection was performed using Pan and Tompkins’ algorithm [26] and RRIs were obtained as the difference of successive R-wave peak locations....
[...]
TL;DR: An original ECG measurement system based on web-service-oriented architecture to monitor the heart health of cardiac patients and is a smart patient-adaptive system able to provide personalized diagnoses by using personal data and clinical history of the monitored patient.
Abstract: The opportunity for cardiac patients to have constantly monitored their health state at home is now possible by means of telemedicine applications. In fact, today, portable and simple-to-use devices allow one to get preliminary domestic diagnoses of the heart status. In this paper, the authors present an original ECG measurement system based on web-service-oriented architecture to monitor the heart health of cardiac patients. The projected device is a smart patient-adaptive system able to provide personalized diagnoses by using personal data and clinical history of the monitored patient. In the presence of a pathology occurrence, the system is able to call the emergency service for assistance. An ECG sensor has the task to acquire, condition, and sample the heart electrical impulses, whereas a personal digital assistant (PDA) performs the diagnosis according to the measurement uncertainty and, in case of a critical situation, calls the medical staff. The system has two removable and updatable memory devices: the first memory device stores the clinical and personal data of the patient, and the second memory device stores information on the metrological status of the measurement system. This way, according to the personal data and historical information of the patient, the measurement system adapts itself by selecting the best fitted ECG model as a reference to configure the computing algorithm. Further information on the measurement uncertainty is used to qualify the reliability of the final clinical response to reduce the occurrence of a faulty diagnosis. Through the PDA graphic interface, the user can display his personal data, observe the graph of his ECG signal, and read diagnosis information with the relative reliability level. Moreover, the patient can choose to print his ECG graph through a Bluetooth printer or to send it to a specialist by a General Packet Radio Service (GPRS) modem.
131 citations
Cites background from "A Real-Time QRS Detection Algorithm..."
...an R–R interval, the QRS duration and amplitude, the P wave duration and amplitude, the P–R interval, and the Q–T interval [22], [23]....
[...]
TL;DR: A deep review and discussion of 93 state-of-the-art publications on their proposed methods, signal datasets, and publicly available ECG collections is conducted to present the fundamentals and the evolution of ECG biometrics, describe the current state of the art, and draw conclusions on prior art approaches and current challenges.
Abstract: Face and fingerprint are, currently, the most thoroughly explored biometric traits, promising reliable recognition in diverse applications. Commercial products using these traits for biometric identification or authentication are increasingly widespread, from smartphones to border control. However, increasingly smart techniques to counterfeit such traits raise the need for traits that are less vulnerable to stealthy trait measurement or spoofing attacks. This has sparked interest on the electrocardiogram (ECG), most commonly associated with medical diagnosis, whose hidden nature and inherent liveness information make it highly resistant to attacks. In the last years, the topic of ECG-based biometrics has quickly evolved toward the commercial applications, mainly by addressing the reduced acceptability and comfort by proposing new off-the-person, wearable, and seamless acquisition settings. Furthermore, researchers have recently started to address the issues of spoofing prevention and data security in ECG biometrics, as well as the potential of deep learning methodologies to enhance the recognition accuracy and robustness. In this paper, we conduct a deep review and discussion of 93 state-of-the-art publications on their proposed methods, signal datasets, and publicly available ECG collections. The extracted knowledge is used to present the fundamentals and the evolution of ECG biometrics, describe the current state of the art, and draw conclusions on prior art approaches and current challenges. With this paper, we aim to delve into the current opportunities as well as inspire and guide future research in ECG biometrics.
131 citations
Cites methods from "A Real-Time QRS Detection Algorithm..."
...The Pan-Tompkins algorithm [135] was the most frequent choice for fiducial detection [42], [47], [57], [90], [121], and was developed specifically for real-time QRS detection in ECG signals....
[...]
TL;DR: A novel method for QRS detection in electrocardiograms (ECG) based on the S-Transform, a new time frequency representation (TFR), which provides frequency-dependent resolution while maintaining a direct relationship with the Fourier spectrum.
131 citations
Cites background or methods from "A Real-Time QRS Detection Algorithm..."
...On the other hand, we performed a comparison with several other approaches cited in literature, classical algorithm [2], Digital filter based method [5], wavelet based methods [15], a zero crossing method [9], combined adaptive thresholds [10] and Combining algorithms [22] (see table 2)....
[...]
...These records are those for which most algorithms found in literature obtained worst results [1, 2, 5, 9, 15]....
[...]
...References [2, 3, 4] represent original works on this subject....
[...]
TL;DR: Overall, the test results indicate that the newly developed approach outperforms traditional methods using these databases under assessed various experimental situations, and suggest the technique could be of practical use for clinicians in the future.
Abstract: Atrial fibrillation (AF) is the most common and debilitating abnormalities of the arrhythmias worldwide, with a major impact on morbidity and mortality. The detection of AF becomes crucial in preventing both acute and chronic cardiac rhythm disorders. Our objective is to devise a method for real-time, automated detection of AF episodes in electrocardiograms (ECGs). This method utilizes RR intervals, and it involves several basic operations of nonlinear/linear integer filters, symbolic dynamics and the calculation of Shannon entropy. Using novel recursive algorithms, online analytical processing of this method can be achieved. Four publicly-accessible sets of clinical data (Long-Term AF, MIT-BIH AF, MIT-BIH Arrhythmia, and MIT-BIH Normal Sinus Rhythm Databases) were selected for investigation. The first database is used as a training set; in accordance with the receiver operating characteristic (ROC) curve, the best performance using this method was achieved at the discrimination threshold of 0.353: the sensitivity (Se), specificity (Sp), positive predictive value (PPV) and overall accuracy (ACC) were 96.72%, 95.07%, 96.61% and 96.05%, respectively. The other three databases are used as testing sets. Using the obtained threshold value (i.e., 0.353), for the second set, the obtained parameters were 96.89%, 98.25%, 97.62% and 97.67%, respectively; for the third database, these parameters were 97.33%, 90.78%, 55.29% and 91.46%, respectively; finally, for the fourth set, the Sp was 98.28%. The existing methods were also employed for comparison. Overall, in contrast to the other available techniques, the test results indicate that the newly developed approach outperforms traditional methods using these databases under assessed various experimental situations, and suggest our technique could be of practical use for clinicians in the future.
131 citations
Cites methods from "A Real-Time QRS Detection Algorithm..."
...Therefore, with a method available elsewhere for real-time R-wave detection [31], this newly proposed method could be used in intensive care units....
[...]
References
More filters
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]....
[...]
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