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: Kweimann et al. as mentioned in this paper used transfer learning to train deep convolutional neural networks (CNNs) to classify raw ECG recordings and finetune the networks on a small data set for classification of Atrial Fibrillation, which is the most common heart arrhythmia.
Abstract: Remote monitoring devices, which can be worn or implanted, have enabled a more effective healthcare for patients with periodic heart arrhythmia due to their ability to constantly monitor heart activity. However, these devices record considerable amounts of electrocardiogram (ECG) data that needs to be interpreted by physicians. Therefore, there is a growing need to develop reliable methods for automatic ECG interpretation to assist the physicians. Here, we use deep convolutional neural networks (CNN) to classify raw ECG recordings. However, training CNNs for ECG classification often requires a large number of annotated samples, which are expensive to acquire. In this work, we tackle this problem by using transfer learning. First, we pretrain CNNs on the largest public data set of continuous raw ECG signals. Next, we finetune the networks on a small data set for classification of Atrial Fibrillation, which is the most common heart arrhythmia. We show that pretraining improves the performance of CNNs on the target task by up to [Formula: see text], effectively reducing the number of annotations required to achieve the same performance as CNNs that are not pretrained. We investigate both supervised as well as unsupervised pretraining approaches, which we believe will increase in relevance, since they do not rely on the expensive ECG annotations. The code is available on GitHub at https://github.com/kweimann/ecg-transfer-learning .
58 citations
TL;DR: An approach is proposed to automatically annotate the desired peaks inSCG signal that are related to STI by utilizing the information of peak detected in the sliding template to narrow-down the search for the desired peak in actual SCG signal.
Abstract: Continuous and non-invasive monitoring of hemodynamic parameters through unobtrusive wearable sensors can potentially aid in early detection of cardiac abnormalities, and provides a viable solution for long-term follow-up of patients with chronic cardiovascular diseases without disrupting the daily life activities. Electrocardiogram (ECG) and siesmocardiogram (SCG) signals can be readily acquired from light-weight electrodes and accelerometers respectively, which can be employed to derive systolic time intervals (STI). For this purpose, automated and accurate annotation of the relevant peaks in these signals is required, which is challenging due to the inter-subject morphological variability and noise prone nature of SCG signal. In this paper, an approach is proposed to automatically annotate the desired peaks in SCG signal that are related to STI by utilizing the information of peak detected in the sliding template to narrow-down the search for the desired peak in actual SCG signal. Experimental validation of this approach performed in conventional/controlled supine and realistic/challenging seated conditions, containing over 5600 heart beat cycles shows good performance and robustness of the proposed approach in noisy conditions. Automated measurement of STI in wearable configuration can provide a quantified cardiac health index for long-term monitoring of patients, elderly people at risk and health-enthusiasts.
58 citations
TL;DR: The proposed ASIC together with the Android application is the most suitable for an energy efficient wearable cardiovascular disease detection system.
Abstract: A novel disease diagnostic algorithm for ECG signal processing based on forward search is implemented in Application Specific Integrated Circuit (ASIC) for cardiovascular disease diagnosis on smartphone. An ASIC is fabricated using 130-nm CMOS low leakage process technology. The area of our PQRST ASIC is 1.21 mm2. The energy dissipation of PQRST ASIC is 96 pJ with a supply voltage of 0.9 V. The outputs from the ASIC are fed to an Android application that generates diagnostic report and can be sent to a cardiologist via email. The ASIC and Android application are verified for the detection of bundle branch block, hypertrophy, arrhythmia and myocardial infarction using Physionet PTB diagnostic ECG database. The failed detection rate is 0.69%, 0.69%, 0.34% and 1.72% for bundle branch block, hypertrophy, arrhythmia and myocardial infarction respectively. The AV block is detected in all the three patients in the Physionet St. Petersburg arrhythmia database. Our proposed ASIC together with our Android application is the most suitable for an energy efficient wearable cardiovascular disease detection system.
57 citations
Cites background from "A Real-Time QRS Detection Algorithm..."
...In order to detect cardiovascular diseases, the ECG signal processing algorithms [1]–[3] are executed in smartphone [4], [5]....
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...As compared to slope based methods [1]–[3] in literature, our method of detecting QRS complex peak location based on envelope and adaptive threshold makes our technique less prone to noise....
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TL;DR: Assessment of PRD is a promising tool to identify post-MI patients with reduced LVEF who might benefit from intensified treatment and to improve identification of patients that profit from ICD-implantation.
Abstract: Aims To test the value of Periodic Repolarization Dynamics (PRD), a recently validated electrocardiographic marker of sympathetic activity, as a novel approach to predict sudden cardiac death (SCD) and non-sudden cardiac death (N-SCD) and to improve identification of patients that profit from ICD-implantation. Methods and results We included 856 post-infarction patients with left-ventricular ejection fraction (LVEF) <= 30% of the MADIT-II trial in sinus rhythm. Of these, 507 and 348 patients were randomized to ICD or conventional treatment. PRD was assessed from multipolar 10-min baseline ECGs. Primary and secondary endpoints were total mortality, SCD and N-SCD. Multivariable analyses included treatment group, QRS-duration, New York Heart Association classification, blood-urea nitrogen, diabetes mellitus, beta-blocker therapy and LVEF. During follow-up of 20.4 months, 119 patients died (53 SCD and 36 N-SCD). On multivariable analyses, increased PRD was a significant predictor of mortality (standardized coefficient 1.37[1.19-1.59];P < 0.001) and SCD (1.40 [1.13-1.75];P = 0.003) but also predicted N-SCD (1.41[1.10-1.81];P = 0.006). While increased PRD predicted SCD in conventionally treated patients (1.61[1.23-2.11];P < 0.001), it was predictive of N-SCD (1.63[1.28-2.09];P < 0.001) and adequate ICD-therapies (1.20[1.03-1.39];P = 0.017) in ICD-treated patients. ICD-treatment substantially reduced mortality in the lowest three PRD-quartiles by 53% (P = 0.001). However, there was no effect in the highest PRD-quartile (mortality increase by 29%;P = 0.412;P < 0.001 for difference) as the reduction of SCD was compensated by an increase of N-SCD. Conclusion In post-infarction patients with impaired LVEF, PRD is a significant predictor of SCD and N-SCD. Assessment of PRD is a promising tool to identify post-MI patients with reduced LVEF who might benefit from intensified treatment.
57 citations
Cites methods from "A Real-Time QRS Detection Algorithm..."
...The beginning and ending of each T-wave are identified using previously published algorithms.(7,8) Figure 1 Consort flow-diagram for the MADIT-II population....
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01 Dec 2015
TL;DR: A machine learning-based approach for computer-assisted detection of five classes of ECG arrhythmia beats using Discrete Wavelet Transform (DWT) features and is an efficient tool, which has intensive applications in early diagnosis and mass screening of cardiac health.
Abstract: Electrocardiogram (ECG) remains the most reliable and low-cost diagnostic tool to evaluate the patients with cardiac arrhythmias. Manual diagnosis of arrhythmia beats is very tedious due to the nonlinear and complex nature of ECG. Likewise, minute variations in time-domain features viz. amplitude, segments and intervals are difficult to interpret by naked eye. The current paper, describes a machine learning-based approach for computer-assisted detection of five classes of ECG arrhythmia beats using Discrete Wavelet Transform (DWT) features. Further, methodology comprises dimensionality reduction using Independent Component Analysis (ICA), ten-fold cross-validation and classification using Support Vector Machine (SVM) kernel functions. Using ANOVA significant features are selected and reliability of accuracy is measured by Cohen's kappa statistic. Large dataset of 110,093 heartbeats from 48 records of MIT-BIH arrhythmia database recommended by ANSI/AAMI EC57:1998, which are grouped into five classes of arrhythmia beats viz. Non-ectopic (N), Supraventricular ectopic (S), Ventricular ectopic (V), Fusion (F) and Unknown (U) are classified with class specific accuracy of 99.57%, 97.91%, 92.18%, 76.54% and 97.22% respectively and an overall average accuracy of 98.49%, using SVM quadratic kernel. The developed methodology is an efficient tool, which has intensive applications in early diagnosis and mass screening of cardiac health.
57 citations
Cites methods from "A Real-Time QRS Detection Algorithm..."
...Further, denoised ECG R–peak is detected using Pan–Tompkins algorithm [19]....
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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