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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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Journal ArticleDOI
TL;DR: In this study, physioNet ECG records have been considered for analysis and the R-peaks are detected using principal component analysis (PCA) which outperforms the existing state-of-the-art techniques.
Abstract: Electrocardiography (ECG) is a non-invasive test that is used for recording contraction and relaxation activities of the heart by using an electrocardiogram. Early detection of abnormalities of the heart through ECG is essential for reducing the prevalence of casualties due to cardiac arrests worldwide. In this study, physioNet ECG records have been considered for analysis. During recording, ECG signal is also affected by various noises, where analog filters fail due to the effect of temperature and drift, and digital filters fail due to inappropriate selection of passband and gain parameters. For adequate and frequent usage in the medical field, it demands correct and precise R-peak (QRS-complex) detection; which requires an appropriate combination of pre-processing, feature extraction and detection techniques. Therefore, independent component analysis (ICA) is used in the pre-processing stage due to nonlinear nature of the ECG signals and chaos analysis is applied for feature extraction for different ECG databases. The ICA method separates an individual signal from mixed signals by assuming that the original underlying source signals are mutually independently distributed. Chaos analysis examines the irregular attitude of the system and fits it into deterministic equations of motion. Chaos analysis is implemented by plotting different attractors against various time delay dimensions. R-peak detection is well known to be useful in diagnosing cardiac diseases. The R-peaks are detected using principal component analysis (PCA) which outperforms the existing state-of-the-art techniques.

52 citations

Journal ArticleDOI
02 Aug 2017-Sensors
TL;DR: This paper presents a validation study for a contactless HR estimation method exploiting RGB (Red, Green, Blue) data from a Microsoft Kinect v2 device that can achieve performance comparable to classical approaches exploiting wearable systems, under specific test conditions.
Abstract: Contactless detection is one of the new frontiers of technological innovation in the field of healthcare, enabling unobtrusive measurements of biomedical parameters. Compared to conventional methods for Heart Rate (HR) detection that employ expensive and/or uncomfortable devices, such as the Electrocardiograph (ECG) or pulse oximeter, contactless HR detection offers fast and continuous monitoring of heart activities and provides support for clinical analysis without the need for the user to wear a device. This paper presents a validation study for a contactless HR estimation method exploiting RGB (Red, Green, Blue) data from a Microsoft Kinect v2 device. This method, based on Eulerian Video Magnification (EVM), Photoplethysmography (PPG) and Videoplethysmography (VPG), can achieve performance comparable to classical approaches exploiting wearable systems, under specific test conditions. The output given by a Holter, which represents the gold-standard device used in the test for ECG extraction, is considered as the ground-truth, while a comparison with a commercial smartwatch is also included. The validation process is conducted with two modalities that differ for the availability of a priori knowledge about the subjects' normal HR. The two test modalities provide different results. In particular, the HR estimation differs from the ground-truth by 2% when the knowledge about the subject's lifestyle and his/her HR is considered and by 3.4% if no information about the person is taken into account.

52 citations


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

  • ...The algorithm proposed by Pan and Tompkins [51] is used to extract the R-R peaks and estimate the HR mean value....

    [...]

Journal ArticleDOI
01 Apr 2019
TL;DR: Comparisons with several state-of-the-art methods using standard criteria on three datasets demonstrate the superiority of MMNNS for improving detection of heartbeats and addressing imbalance in ECG heartbe beats classification.
Abstract: In this paper, a novel multi-module neural network system named MMNNS is proposed to solve the imbalance problem in electrocardiogram (ECG) heartbeats classification. Four submodules are designed to construct the system: preprocessing, imbalance problem processing, feature extraction and classification. Imbalance problem processing module mainly introduces three methods: BLSM, CTFM and 2PT, which are proposed from three aspects of resampling, data feature and algorithm respectively. BLSM is used to synthesize virtual samples linearly around the minority samples. CTFM consists of DAE-based feature extraction part and QRS-based feature selection part, in which selected features and complete features are applied to determine the heartbeat class simultaneously. The processed data are fed into a convolutional neural network (CNN) by applying 2PT to train and fine-tune. MMNNS is trained on MIT-BIH Arrhythmia Database following AAMI standard, using intra-patient and inter-patient scheme, especially the latter which is strongly recommended. The comparisons with several state-of-the-art methods using standard criteria on three datasets demonstrate the superiority of MMNNS for improving detection of heartbeats and addressing imbalance in ECG heartbeats classification.

52 citations

Patent
11 Jul 2008
TL;DR: In this paper, a method and system for cancelling body movement effect for non-contact vital sign detection is described, where a first reflected electromagnetic signal reflected back in response to the second electromagnetic wave on the second transceiver is received and a second baseband complex signal is extracted.
Abstract: A method and system for cancelling body movement effect for non-contact vital sign detection is described. The method begins with sending on a first electromagnetic wave transceiver a first electromagnetic signal with a first frequency to a first side of a body, such as a person or animal. Simultaneously using a second electromagnetic wave transceiver a second electromagnetic signal is sent with a second frequency to a second side of a body, wherein the first frequency and the second frequency are different frequencies. A first reflected electromagnetic signal reflected back in response to the first electromagnetic wave on the first transceiver is received and a first baseband complex signal is extracted. Likewise a second reflected electromagnetic signal reflected back in response to the second electromagnetic wave on the second transceiver is received and a second baseband complex signal is extracted. The first baseband complex signal is mathematically combined with the second baseband complex signal to cancel out a Doppler frequency drift therebetween to yield a periodic Doppler phase effect.

52 citations

Journal ArticleDOI
TL;DR: This paper presents a measurement system based on two System on Chip solutions and Raspberry PI, implementing both a full three-lead ECG recorder and an impedance cardiographer, for educational and research development purposes.
Abstract: Impedance cardiography (ICG) is a noninvasive method for monitoring cardiac dynamics using electrical bioimpedance (EBI) measurements. Since its appearance more than 40 years ago, ICG has been used for assessing hemodynamic parameters. This paper presents a measurement system based on two System on Chip (SoC) solutions and Raspberry PI, implementing both a full three-lead ECG recorder and an impedance cardiographer, for educational and research development purposes. Raspberry PI is a platform supporting Do-It-Yourself project and education applications across the world. The development is part of Biosignal PI, an open hardware platform focusing in quick prototyping of physiological measurement instrumentation. The SoC used for sensing cardiac biopotential is the ADAS1000, and for the EBI measurement is the AD5933. The recordings were wirelessly transmitted through Bluetooth to a PC, where the waveforms were displayed, and hemodynamic parameters such as heart rate, stroke volume, ejection time and cardiac output were extracted from the ICG and ECG recordings. These results show how Raspberry PI can be used for quick prototyping using relatively widely available and affordable components, for supporting developers in research and engineering education. The design and development documents will be available on www.BiosignalPI.com , for open access under a Non Commercial-Share A like 4.0 International License.

52 citations


Additional excerpts

  • ...This algorithm has a simple implementation and an accurate performance [54]....

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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