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Journal ArticleDOI

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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Citations
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Proceedings ArticleDOI
01 Jan 2005
TL;DR: An algorithm of filtering the noisy real ECG signal and removing the noise interfering R waves at the 4th level detail sequence based on the Donoho et al. algorithm is presented.
Abstract: We present in this paper an algorithm of filtering the noisy real ECG signal. The classical wavelet denoising process, based on the Donoho et al. algorithm, at the 4th level, appears clearly the P and T waves whereas the R waves undergo considerable distortion. This is due to the interference of the WGN and the free noise ECG detail sequences at level 4. To overcome this drawback, our key idea is to estimate the corrupted WGN and consequently remove the noise interfering R waves at the 4th level detail sequence. Our denoising algorithm was applied to a set of the MIT-BIH arrhythmia database ECG records corrupted with a 0 dB WGN which provided an output SNR of around 6 dB and an MSE value of around 0.0011. A comparative analysis using the low pass Butterworth filter and the 4th level classical wavelet denoising provides the output SNR values of around 3 dB and MSE value of around 0.0018; which demonstrates the superior performance of our proposed denoising algorithm

77 citations


Cites background from "A Real-Time QRS Detection Algorithm..."

  • ...Different works have been established to design filtering algorithms aimed to improve the SNR (signal to noise) values and recovering the ECG waves in different noisy environment [4-7]....

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DOI
30 Jan 2010
TL;DR: This research focused on short-term, resting, Lead-I ECG signals recorded from the palms, which included template matching and distance classification methods to generate ECG databases and templates for reducing the noise recorded with palm ECG signals.
Abstract: The electrocardiogram (ECG) is not only a very useful diagnostic tool for clinical purposes, but also is a potential new biometric tool for human identification. TheECG may be useful as a biometric in the future, since it can easily be combined with other biometrics to provide a liveness check with little additional cost. This research focused on short-term, resting, Lead-I ECG signals recorded from the palms. A total of 168 young college volunteers were investigated for identification as a predetermined group. Fifty persons were randomly selected from this ECG biometric database as the development dataset. Then, the identification algorithm developed from this group was tested on the entire database. In this research, two algorithms were evaluated for ECG identification during system development. The algorithms included template matching and distance classification methods. Signal averaging was applied to generate ECG databases and templates for reducing the noise recorded with palm ECG signals. When a single algorithm was applied to the development dataset, the identification rate (that is, rank one probability) was up to 98% (49 out of 50 persons). However, when the prescreening process was added to construct a combined system model, the identification rate increased to 100% accuracy on the development dataset. The combined model formed our ECG biometric system model based on results from the development dataset. The identification rate was 95.3% when the same combined system model was tested on the entire ECG biometric database. Key words: Biometrics, biometric liveness tests, electrocardiogram (ECG), ECG features, identification, template matching, distance classification

77 citations

Journal ArticleDOI
TL;DR: Spatial filling index and Renyi's entropy has distinct regions for various diseases with an accuracy of more than 95% and the contours of scalogram visually show the features of the diseases.
Abstract: Analysis of heart rate variation (HRV) has become a popular noninvasive tool for assessing the activities of the autonomic nervous system (ANS). HRV analysis is based on the concept that fast fluctuations may specifically reflect changes of sympathetic and vagal activity. It shows that the structure generating the signal is not simply linear, but also involves nonlinear contributions. These signals are essentially non-stationary; may contain indicators of current disease, or even warnings about impending diseases. The indicators may be present at all times or may occur at random in the time scale. However, to study and pinpoint abnormalities in voluminous data collected over several hours is strenuous and time consuming. This paper presents the spatial filling index and time-frequency analysis of heart rate variability signal for disease identification. Renyi's entropy is evaluated for the signal in the Wigner-Ville and Continuous Wavelet Transformation (CWT) domain. This Renyi's entropy gives lower 'p' value for scalogram than Wigner-Ville distribution and also, the contours of scalogram visually show the features of the diseases. And in the time-frequency analysis, the Renyi's entropy gives better result for scalogram than the Wigner-Ville distribution. Spatial filling index and Renyi's entropy has distinct regions for various diseases with an accuracy of more than 95%.

77 citations


Cites methods from "A Real-Time QRS Detection Algorithm..."

  • ...The R peaks of ECG were detected using Tompkins's algorithm [ 20 ]....

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Journal ArticleDOI
TL;DR: Experimental results show that the proposed algorithm outperforms other existing algorithms in case of different QRS complex morphologies (negative, low-amplitude, wide), very big change in amplitudes of adjacent R-peaks, irregular heart rates, and noisy ECG signals.
Abstract: In this paper, we present a reliable and efficient automatic R-wave detection based on new nonlinear transformation and simple peak-finding strategy. The detection algorithm consists of four stages. In the first stage, the bandpass filtering and differentiation operations are used to enhance QRS complexes and to reduce out-of-band noise. In the second stage, we introduce a new nonlinear transformation based on energy thresholding, Shannon energy computation, and smoothing processes to obtain a positive-valued feature signal which includes large candidate peaks corresponding to the QRS complex regions. The energy thresholding reduces the effect of spurious noise spikes from muscle artifacts. The Shannon energy transformation amplifies medium amplitudes and results in small deviations between successive peaks. Therefore, the proposed nonlinear transformation is capable of reducing the number of false-positives and false-negatives under small-QRS and wide-QRS complexes and noisy ECG signals. In the third stage, we propose a simple peak-finding strategy based on the first-order Gaussian differentiator (FOGD) that accurately identifies locations of candidate R-peaks in a feature signal. This stage computes convolution of the smooth feature signal and FOGD operator. The resultant convolution output has negative zero-crossings (ZCs) around the candidate peaks of feature signal due to the anti-symmetric nature of the FOGD operator. Thus, these negative ZCS are detected and used as guides to find locations of real R-peaks in an original signal at the fourth stage. Unlike other existing algorithms, the proposed algorithm does not use search back algorithm and learning phase. The proposed algorithm is validated using the standard MIT-BIH arrhythmia database and achieves an average sensitivity of 99.94% and a positive predictivity of 99.96%. Experimental results show that the proposed algorithm outperforms other existing algorithms in case of different QRS complex morphologies (negative, low-amplitude, wide), very big change in amplitudes of adjacent R-peaks, irregular heart rates, and noisy ECG signals.

77 citations

Journal ArticleDOI
TL;DR: A new nonlinear method based on empirical mode decomposition (EMD) is proposed to discriminate between diabetic and normal RR-interval signals and results indicate that these features provide the statistically significant difference between diabetes and normal classes.
Abstract: We propose new features for analysis of normal and diabetic RR-interval signals.Features are extracted from intrinsic mode functions of RR-interval signals.Two unique visual plots are proposed for diagnosis of diabetes.Proposed features are suitable for discrimination of normal and diabetic classes. Large number of people are affected by Diabetes Mellitus (DM) which is difficult to cure due to its chronic nature and genetic link. The uncontrolled diabetes may lead to heart related problems. Therefore, the diagnosis and monitoring of diabetes is of great importance. The automatic detection of diabetes can be performed using RR-interval signals. The RR-interval signals are nonlinear and non-stationary in nature. Hence linear methods may not be able to capture the hidden information present in the signal. In this paper, a new nonlinear method based on empirical mode decomposition (EMD) is proposed to discriminate between diabetic and normal RR-interval signals. The mean frequency parameter using Fourier-Bessel series expansion ( MF FB ) and the two bandwidth parameters namely, amplitude modulation bandwidth ( B AM ) and frequency modulation bandwidth ( B FM ) extracted from the intrinsic mode functions (IMFs) obtained from the EMD of RR-interval signals are used to discriminate the two groups. Unique representations such as analytic signal representation (ASR) and second order difference plot (SODP) for IMFs of RR-interval signals are also proposed to differentiate the two groups. The area parameters are computed from ASR and SODP of IMFs of RR-interval signals. Area computed from these representation as area corresponding to the 95% central tendency measure (CTM) of ASR of IMFs ( A ASR ) and 95% confidence ellipse area of SODP of IMF ( A SODP ) are also proposed to discriminate diabetic and normal RR-interval signals. Overall, five features are extracted from IMFs of RR-interval signals namely MF FB , B AM , B FM , A ASR and A SODP . Kruskal-Wallis statistical test is used to measure the discrimination ability of the proposed features for detection of diabetic RR-interval signals. Results obtained from proposed methodology indicate that these features provide the statistically significant difference between diabetic and normal classes.

76 citations


Cites methods from "A Real-Time QRS Detection Algorithm..."

  • ...Then, the Pan-Tompkins algorithm (Pan & Tompkins, 1985) is used to find the QRS complexes from the processed ECG signal....

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References
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Journal ArticleDOI
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

Journal ArticleDOI
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

Journal ArticleDOI
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]....

    [...]

Journal ArticleDOI
P. A. Lynn1
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

Journal ArticleDOI
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