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
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TL;DR: The methodology of QRS detection, SDB analysis, calculation of ECG-derived respiration curves, and estimation of a sleep pattern is described in detail and underline the need for further testing in larger patient groups with different underlying diseases.
Abstract: The diagnosis of sleep-disordered breathing (SDB) usually relies on the analysis of complex polysomnographic measurements performed in specialized sleep centers. Automatic signal analysis is a promising approach to reduce the diagnostic effort. This paper addresses SDB and sleep assessment solely based on the analysis of a single-channel ECG recorded overnight by a set of signal analysis modules. The methodology of QRS detection, SDB analysis, calculation of ECG-derived respiration curves, and estimation of a sleep pattern is described in detail. SDB analysis detects specific cyclical variations of the heart rate by correlation analysis of a signal pattern and the heart rate curve. It was tested with 35 SDB-annotated ECGs from the Apnea-ECG Database, and achieved a diagnostic accuracy of 80.5%. To estimate sleep pattern, spectral parameters of the heart rate are used as stage classifiers. The reliability of the algorithm was tested with 18 ECGs extracted from visually scored polysomnographies of the SIESTA database; 57.7% of all 30 s epochs were correctly assigned by the algorithm. Although promising, these results underline the need for further testing in larger patient groups with different underlying diseases.
64 citations
TL;DR: This paper presents a real-time event-driven classification technique based on the random forest classification scheme, which uses a confidence-related decision-making process and outperforms the existing approaches in terms of energy consumption and battery lifetime.
Abstract: A considerable portion of government health-care spending is allocated to the continuous monitoring of patients suffering from cardiovascular diseases, particularly myocardial infarction (MI). Wearable devices present a cost-effective means of monitoring patients’ vital signs in ambulatory settings. A major challenge is to design such ultra-low energy devices for long-term patient monitoring. In this paper, we present a real-time event-driven classification technique based on the random forest classification scheme, which uses a confidence-related decision-making process. The main goal of this technique is to maintain a high classification accuracy while reducing the complexity of the classification algorithm. We validate our approach on a well-established and complete MI database (Physiobank, PTB Diagnostic ECG database [1] ). Our experimental evaluation demonstrates that our real-time classification scheme outperforms the existing approaches in terms of energy consumption and battery lifetime by a factor of 2.60, with no classification quality loss.
64 citations
Cites methods from "A Real-Time QRS Detection Algorithm..."
...Pan-Tompkin’s algorithm is used for ECG R-peaks detection [36]....
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TL;DR: The method is compared to the discrete cosine transform and is found to yield a significantly higher data compression for a given signal quality (quantified by mean squared error and peak error).
Abstract: A method for the compression of ECG (electrocardiogram) data is presented. The method is based on high-degree polynomial expansions. Data rules of about 350 b/s are achievable at an acceptable signal quality. The high compression is obtained by a carefully selected subdivision of the ECG signal into intervals that make optimal use of the special properties of the polynomial base functions. Each interval corresponds to one ECG period. The method is compared to the discrete cosine transform and is found to yield a significantly higher data compression for a given signal quality (quantified by mean squared error and peak error). >
63 citations
TL;DR: The stress-inducing protocol, data acquisition, preprocessing, feature extraction and classification are the major steps involved to detect the stress, and experimental results indicate that the proposed methodology for short-term ECG and HRV signal can achieve the overall average classification accuracy.
Abstract: This paper introduces a method for resolving the problem of human stress detection through short-term (less than 5 min) electrocardiogram (ECG) and heart rate variability (HRV) signals. The explored methodology helps to improve the stress detection rate and reliability through multiple evidences originated in same sensor. In this work, stress-inducing protocol, data acquisition, preprocessing, feature extraction and classification are the major steps involved to detect the stress. In total, 60 subjects (30 males and 30 females) participated in the Stroop color word-based stress-inducing task and ECG signal was acquired simultaneously. The wavelet denoising algorithm was applied to remove high frequency, baseline wander and power line noises. Discrete wavelet transform (DWT)-based heart rate (HR) detection algorithm is used for deriving HRV signal from the preprocessed ECG signal. The ectopic beat removal method is employed to eliminate the ectopic beat and noise peaks in the HRV signal. In order to detect the stress, the issue of uneven sampling with the HRV signal has been successfully rectified using the Lomb-Scargle periodogram (LSP). The application of LSP in short-term HRV signals (32 s), uneven sampling issue, and power spectral information issue has been rectified and the trustworthiness of the short-term HRV signal has been proved by hypothesis as well as experimental results. Theoretical analysis suggested that a minimum 25 s of online or offline ECG data is required to analyze the autonomous nervous system (ANS) activity related to stress. In addition to the HRV signal, ECG-based stress assessment has been proposed to detect the stress through optimum features using fast Fourier transform (FFT). Various features extracted from the ECG and HRV signal have been classified into normal and stress using PNN and kNN classifiers with different smoothing factor and k values. The experimental results indicate that the proposed methodology for short-term ECG and HRV signal can achieve the overall average classification accuracy of 91.66% and 94.66% in the subject-independent mode.
63 citations
03 Aug 2018
TL;DR: This review deciphers the most significant machine-learning algorithms for the futuristic biosensors along with the internet of things, computational techniques and microchip-based essential cardiac biomarkers for real-time health monitoring and improving patient compliance.
Abstract: Biosensor-based devices are pioneering in the modern biomedical applications and will be the future of cardiac health care. The coupling of artificial intelligence (AI) for cardiac monitoring-based biosensors for the point of care (POC) diagnostics is prominently reviewed here. This review deciphers the most significant machine-learning algorithms for the futuristic biosensors along with the internet of things, computational techniques and microchip-based essential cardiac biomarkers for real-time health monitoring and improving patient compliance. The present review also discusses the recently developed cardiac biosensors along with technical strategies involved in their mechanism of working and their applications in healthcare. Additionally, it provides a key for the ontogeny of an effective and supportive hierarchical protocol for clinical decision-making about personalized medicine through combinatory information analysis, and integrated multidisciplinary AI approaches.
63 citations
Cites methods from "A Real-Time QRS Detection Algorithm..."
...Also notch filter and Pan–Tompkins methods are used to eliminate 50-Hz cutoff frequency and identify R-peaks separately (Pan and Tompkins 1985)....
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References
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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]....
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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