Relevant bibliographies by topics / Detection of QRS complexes

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
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  5. Conference papers

Academic literature on the topic 'Detection of QRS complexes'

Author: Grafiati
Published: 28 June 2021
Last updated: 18 February 2022

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Journal articles on the topic "Detection of QRS complexes"

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Al-Ghabban, Ahmed Saad. "Predominant Peak Detection of QRS Complexes." International Journal of Medical Imaging 2, no. 6 (2014): 133. http://dx.doi.org/10.11648/j.ijmi.20140206.12.

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Salih, Sameer Kleban, S. A. Aljunid, Oteh Maskon, Syed M. Aljunid, and Abid Yahya. "A Robust Approach for Detecting QRS Complexes of Electrocardiogram Signal with Different Morphologies." Key Engineering Materials 594-595 (December 2013): 972–79. http://dx.doi.org/10.4028/www.scientific.net/kem.594-595.972.

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In this paper a robust approach for detecting QRS complexes and computing related R-R intervals of ECG signals named (RDQR) has been proposed. It reliably recognizes QRS complexes based on the deflection occurred between R & S waves as a large positive and negative amplitude differences in comparison with respect to other ECG signal (P and T) waves. The proposed detection approach applies the new direct algorithm applied on the entire ECG itself without any additional transform like (wavelet, cosine, Walsh transform, etc.). According to the strategy based on positive and negative deflection it overcomes the problem of QRS direction positive (upright) or negative (inverted). Three different types of ECG online database with duration of 10 sec (MIT-BIH Arrhythmia, ST Change Database and Normal Sinus Rhythm) are used to validate the detection performance. The results are demonstrated that the proposed detection approach achieved (100%) accuracy for QRS detection also very high accuracy in evaluating related R-R intervals.
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Wei, Wei, Chun Xia Zhang, and Wei Lin. "A QRS Wave Detection Algorithm Based on Complex Wavelet Transform." Applied Mechanics and Materials 239-240 (December 2012): 1284–88. http://dx.doi.org/10.4028/www.scientific.net/amm.239-240.1284.

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Objective to introduce a method that use complex valued wavelet transform algorithm for QRS wave group detection in Electrocardiogram signal. It presents a method of marking the crest value and detecting QRS wave group by combining Fbsp wavelet with mexh wavelet. The method is proved to be precise and rapid by applied to detect 10 pieces of the QRS complexes of the ECG 30min-records provided by MIT-BIH Arrhythmia Database.
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SLIMANE, Z. E. HADJ, and F. BEREKSI REGUIG. "NEW ALGORITHM FOR QRS COMPLEX DETECTION." Journal of Mechanics in Medicine and Biology 05, no. 04 (December 2005): 507–15. http://dx.doi.org/10.1142/s0219519405001692.

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The Electrocardiogram (ECG), represents the electrical activity of the heart. It is characterized by a number of waves P, QRS, T which are correlated to the status of the heart activity. The most predominant wave set is the QRS complex. In this paper, we have developed a new algorithm for the detection of the QRS complexes. The algorithm consists of several steps: signal to noise enhancement, differentiation, first-order backward difference, non linear transform, moving window integrator and QRS detection. This algorithm is tested on ECG signals from the universal MIT-BIH arrhythmia database and compared with Pan and Tompkins' QRS detection method. The results we obtain show that our method performs better than the Pan and Tompkins' method. Our algorithm results in lower false positives and lower false negatives.
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Sharma, Tanushree, and Kamalesh K. Sharma. "A new method for QRS detection in ECG signals using QRS-preserving filtering techniques." Biomedical Engineering / Biomedizinische Technik 63, no. 2 (March 28, 2018): 207–17. http://dx.doi.org/10.1515/bmt-2016-0072.

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AbstractDetection of QRS complexes in ECG signals is required for various purposes such as determination of heart rate, feature extraction and classification. The problem of automatic QRS detection in ECG signals is complicated by the presence of noise spectrally overlapping with the QRS frequency range. As a solution to this problem, we propose the use of least-squares-optimisation-based smoothing techniques that suppress the noise peaks in the ECG while preserving the QRS complexes. We also propose a novel nonlinear transformation technique that is applied after the smoothing operations, which equalises the QRS amplitudes without boosting the supressed noise peaks. After these preprocessing operations, the R-peaks can finally be detected with high accuracy. The proposed technique has a low computational load and, therefore, it can be used for real-time QRS detection in a wearable device such as a Holter monitor or for fast offline QRS detection. The offline and real-time versions of the proposed technique have been evaluated on the standard MIT-BIH database. The offline implementation is found to perform better than state-of-the-art techniques based on wavelet transforms, empirical mode decomposition, etc. and the real-time implementation also shows improved performance over existing real-time QRS detection techniques.
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Kotas, M., J. Jezewski, A. Matonia, and T. Kupka. "Towards noise immune detection of fetal QRS complexes." Computer Methods and Programs in Biomedicine 97, no. 3 (March 2010): 241–56. http://dx.doi.org/10.1016/j.cmpb.2009.09.005.

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Beyramienanlou, Hamed, and Nasser Lotfivand. "An Efficient Teager Energy Operator-Based Automated QRS Complex Detection." Journal of Healthcare Engineering 2018 (September 18, 2018): 1–11. http://dx.doi.org/10.1155/2018/8360475.

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Database. The efficiency and robustness of the proposed method has been tested on Fantasia Database (FTD), MIT-BIH Arrhythmia Database (MIT-AD), and MIT-BIH Normal Sinus Rhythm Database (MIT-NSD). Aim. Because of the importance of QRS complex in the diagnosis of cardiovascular diseases, improvement in accuracy of its measurement has been set as a target. The present study provides an algorithm for automatic detection of QRS complex on the ECG signal, with the benefit of energy and reduced impact of noise on the ECG signal. Method. The method is basically based on the Teager energy operator (TEO), which facilitates the detection of the baseline threshold and extracts QRS complex from the ECG signal. Results. The testing of the undertaken method on the Fanatasia Database showed the following results: sensitivity (Se) = 99.971%, positive prediction (P+) = 99.973%, detection error rate (DER) = 0.056%, and accuracy (Acc) = 99.944%. On MIT-AD involvement, Se = 99.74%, P+ = 99.97%, DER = 0.291%, and Acc = 99.71%. On MIT-NSD involvement, Se = 99.878%, P+ = 99.989%, DER = 0.134%, and Acc = 99.867%. Conclusion. Despite the closeness of the recorded peaks which inflicts a constraint in detection of the two consecutive QRS complexes, the proposed method, by applying 4 simple and quick steps, has effectively and reliably detected the QRS complexes which make it suitable for practical purposes and applications.
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Lee, Seungmin, Yoosoo Jeong, Daejin Park, Byoung-Ju Yun, and Kil Park. "Efficient Fiducial Point Detection of ECG QRS Complex Based on Polygonal Approximation." Sensors 18, no. 12 (December 19, 2018): 4502. http://dx.doi.org/10.3390/s18124502.

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Electrocardiogram signal analysis is based on detecting a fiducial point consisting of the onset, offset, and peak of each waveform. The accurate diagnosis of arrhythmias depends on the accuracy of fiducial point detection. Detecting the onset and offset fiducial points is ambiguous because the feature values are similar to those of the surrounding sample. To improve the accuracy of this paper’s fiducial point detection, the signal is represented by a small number of vertices through a curvature-based vertex selection technique using polygonal approximation. The proposed method minimizes the number of candidate samples for fiducial point detection and emphasizes these sample’s feature values to enable reliable detection. It is also sensitive to the morphological changes of various QRS complexes by generating an accumulated signal of the amplitude change rate between vertices as an auxiliary signal. To verify the superiority of the proposed algorithm, error distribution is measured through comparison with the QT-DB annotation provided by Physionet. The mean and standard deviation of the onset and the offset were stable as − 4.02 ± 7.99 ms and − 5.45 ± 8.04 ms, respectively. The results show that proposed method using small number of vertices is acceptable in practical applications. We also confirmed that the proposed method is effective through the clustering of the QRS complex. Experiments on the arrhythmia data of MIT-BIH ADB confirmed reliable fiducial point detection results for various types of QRS complexes.
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Huang, Sheng-Chieh, Hui-Min Wang, and Wei-Yu Chen. "A ±6 ms-Accuracy, 0.68 mm2, and 2.21 μW QRS Detection ASIC." VLSI Design 2012 (November 22, 2012): 1–13. http://dx.doi.org/10.1155/2012/809393.

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Healthcare issues arose from population aging. Meanwhile, electrocardiogram (ECG) is a powerful measurement tool. The first step of ECG is to detect QRS complexes. A state-of-the-art QRS detection algorithm was modified and implemented to an application-specific integrated circuit (ASIC). By the dedicated architecture design, the novel ASIC is proposed with 0.68 mm2 core area and 2.21 μW power consumption. It is the smallest QRS detection ASIC based on 0.18 μm technology. In addition, the sensitivity is 95.65% and the positive prediction of the ASIC is 99.36% based on the MIT/BIH arrhythmia database certification.
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BENOSMAN, M. M., F. BEREKSI-REGUIG, and E. GORAN SALERUD. "STRONG REAL-TIME QRS COMPLEX DETECTION." Journal of Mechanics in Medicine and Biology 17, no. 08 (December 2017): 1750111. http://dx.doi.org/10.1142/s0219519417501111.

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Heart rate variability (HRV) analysis is used as a marker of autonomic nervous system activity which may be related to mental and/or physical activity. HRV features can be extracted by detecting QRS complexes from an electrocardiogram (ECG) signal. The difficulties in QRS complex detection are due to the artifacts and noises that may appear in the ECG signal when subjects are performing their daily life activities such as exercise, posture changes, climbing stairs, walking, running, etc. This study describes a strong computation method for real-time QRS complex detection. The detection is improved by the prediction of the position of [Formula: see text] waves by the estimation of the RR intervals lengths. The estimation is done by computing the intensity of the electromyogram noises that appear in the ECG signals and known here in this paper as ECG Trunk Muscles Signals Amplitude (ECG-TMSA). The heart rate (HR) and ECG-TMSA increases with the movement of the subject. We use this property to estimate the lengths of the RR intervals. The method was tested using famous databases, and also with signals acquired when an experiment with 17 subjects from our laboratory. The obtained results using ECG signals from the MIT-Noise Stress Test Database show a QRS complex detection error rate (ER) of 9.06%, a sensitivity of 95.18% and a positive prediction of 95.23%. This method was also tested against MIT-BIH Arrhythmia Database, the result are 99.68% of sensitivity and 99.89% of positive predictivity, with ER of 0.40%. When applied to the signals obtained from the 17 subjects, the algorithm gave an interesting result of 0.00025% as ER, 99.97% as sensitivity and 99.99% as positive predictivity.
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Dissertations / Theses on the topic "Detection of QRS complexes"

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Koc, Bengi. "Detection And Classification Of Qrs Complexes From The Ecg Recordings." Master's thesis, METU, 2008. http://etd.lib.metu.edu.tr/upload/2/12610328/index.pdf.

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Electrocardiography (ECG) is the most important noninvasive tool used for diagnosing heart diseases. An ECG interpretation program can help the physician state the diagnosis correctly and take the corrective action. Detection of the QRS complexes from the ECG signal is usually the first step for an interpretation tool. The main goal in this thesis was to develop robust and high performance QRS detection algorithms, and using the results of the QRS detection step, to classify these beats according to their different pathologies. In order to evaluate the performances, these algorithms were tested and compared in Massachusetts Institute of Technology Beth Israel Hospital (MIT-BIH) database, which was developed for research in cardiac electrophysiology. In this thesis, four promising QRS detection methods were taken from literature and implemented: a derivative based method (Method I), a digital filter based method (Method II), Tompkin&rsquo
s method that utilizes the morphological features of the ECG signal (Method III) and a neural network based QRS detection method (Method IV). Overall sensitivity and positive predictivity values above 99% are achieved with each method, which are compatible with the results reported in literature. Method III has the best overall performance among the others with a sensitivity of 99.93% and a positive predictivity of 100.00%. Based on the detected QRS complexes, some features were extracted and classification of some beat types were performed. In order to classify the detected beats, three methods were taken from literature and implemented in this thesis: a Kth nearest neighbor rule based method (Method I), a neural network based method (Method II) and a rule based method (Method III). Overall results of Method I and Method II have sensitivity values above 92.96%. These findings are also compatible with those reported in the related literature. The classification made by the rule based approach, Method III, did not coincide well with the annotations provided in the MIT-BIH database. The best results were achieved by Method II with the overall sensitivity value of 95.24%.
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Malina, Ondřej. "Detekce začátku a konce komplexu QRS s využitím hlubokého učení." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2021. http://www.nusl.cz/ntk/nusl-442595.

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This thesis deals with the issue of automatic measurement of the duration of QRS complexes in ECG signals. Special emphasis is then placed on the possibility of automatic detection of QRS complexes while exciting cardiac tissue with a pacemaker. The content of this work is divided into four logical units, where the first part deals with the heart as an organ. It describes the origin and spread of excitement in the heart, its possible pathologies and their manifestations in ECG recording, it also deals with pacing and measuring ECG recording during simultaneous pacing. The second part of the thesis contains a brief introduction to the topic of machine and deep learning. The third part of the thesis contains a search of current approaches using methods based on deep learning to solve the detection of QRSd. The fourth part deals with the design and implementation of its own model of deep learning, able to detect the beginnings and ends of QRS complexes from ECG recordings. It describes the data preprocessing implemented in the MATLAB programming environment. The actual implementation of the model was performed in the Python using the PyTorch and NumPy moduls.
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Hráček, Roman. "Softwarový balík pro frekvenční metody detekce QRS komplexu." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2015. http://www.nusl.cz/ntk/nusl-221390.

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Engström, Magnus, and Nadia Soheily. "EKG-analys och presentation." Thesis, KTH, Data- och elektroteknik, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-154539.

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Tolkningen av EKG är en viktig metod vid diagnostisering av onormala hjärttillstånd och kan användas i förebyggande syfte att upptäcka tidigare okända hjärtproblem. Att enkelt kunna mäta sitt EKG och få det analyserat och presenterat på ett pedagogiskt sätt utan att behöva rådfråga en läkare är något det finns ett konsumentbehov av. Denna rapport beskriver hur en EKG-signal behandlas med olika algoritmer och metoder i syfte att detektera hjärtslag och dess olika parametrar. Denna information används till att klassificera varje hjärtslag för sig och därmed avgöra om användaren har en normal eller onormal hjärtfunktion. För att nå dit har en mjukvaruprototyp utvecklats där algoritmerna implementerats. En enkätundersökning gjordes i syfte att undersöka hur utdata från mjukvaruprototypen skulle presenteras för en vanlig användare utan medicinsk utbildning. Sju filer med EKG-signaler från MIT-BIH Arrhythmia Database användes för testning av mjukvaruprototypen. Resultatet visade att prototypen kunde detektera en rad olika hjärtfel som låg till grund vid fastställning om hjärtat slog normalt eller onormalt. Resultatet presenterades på en mobilapp baserad på enkätundersökningen.
The interpretation of the ECG is an important method in the diagnosis of abnormal heart conditions and can be used proactively to discover previ-ously unknown heart problems. Being able to easily measure the ECG and get it analyzed and presented in a clear manner without having to consult a doctor is improtant to satisfy consumer needs. This report describes how an ECG signal is treated with different algo-rithms and methods to detect the heartbeat and its various parameters. This information is used to classify each heartbeat separately and thus determine whether the user has a normal or abnormal cardiac function. To achieve this a software prototype was developed in which the algorithms were implemented. A questionnaire survey was done in order to examine how the output of the software prototype should be presented for a user with no medical training. Seven ECG files from MIT-BIH Arrhythmia database were used for validation of the algorithms. The developed algorithms could detect of if any abnormality of heart function occurred and informed the users to consult a physician. The presentation of the heart function was based on the result from the questioner.
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Klingspor, Måns. "Hilbert Transform : Mathematical Theory and Applications to Signal processing." Thesis, Linköpings universitet, Matematik och tillämpad matematik, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-122736.

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The Hilbert transform is a widely used transform in signal processing. In this thesis we explore its use for three different applications: electrocardiography, the Hilbert-Huang transform and modulation. For electrocardiography, we examine how and why the Hilbert transform can be used for QRS complex detection. Also, what are the advantages and limitations of this method? The Hilbert-Huang transform is a very popular method for spectral analysis for nonlinear and/or nonstationary processes. We examine its connection with the Hilbert transform and show limitations of the method. Lastly, the connection between the Hilbert transform and single-sideband modulation is investigated.
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Brandejs, Jakub. "Detekce parametrů repolarizace ze signálu EKG." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2014. http://www.nusl.cz/ntk/nusl-220848.

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A T wave peak and offset detector based on an unpublished lead transformation that can be briefly described as multilead linear regression was proposed and implemented afterwards. Potential of the transformation as a useful QRS detection tool was revealed later on. Proposed QRS detector was put to the test of CSE database. Results were compared with work of other authors. Results of T wave peak and offset detector were introduced in visual way.
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Hanzelka, Adam. "Rozměřování experimentálních záznamů EKG." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2013. http://www.nusl.cz/ntk/nusl-220063.

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This master's thesis deals with an analysis of principles of ECG signals detection and delineation. The theoretical part describes heart electrophysiology and electrocardiography basics. Next, the most important QRS detection and ECG delineation algorithms are introduced. Especially the wavelet transform methods are described. In the practical part proper delineation algoriythm was realized. It was tested on the standard CSE database, then it was modified on data of isolated rabbit heartsand the results are published in the conclusion.
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Bajgar, Jiří. "Detekce P vlny v EKG signálech." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2015. http://www.nusl.cz/ntk/nusl-221317.

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The aim of this diploma thesis is to introduce methods of detection of the QRS complex and the subsequent detection of P waves. The intention is to create a program by specified method in the software Matlab which will be able to implement this method. The thesis describes the basic and important methods of detection and subsequent algorithm to detect P waves. Solution of the algorithm is tested on real data. It also describes the automatic signal evaluation and the results of this automatic function.
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Bucsuházy, Kateřina. "Rozměření experimentálních záznamů EKG." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2015. http://www.nusl.cz/ntk/nusl-221318.

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This master thesis deals with QRS complex detection and ECG delineation. The theoretical part of this work describes wavelet transform, some of QRS detection approaches and some of ECG delineation approaches. For algorithm realization in Matlab is used redundant dyadic discrete wavelet transform. Algorithm is designed for experimental electrocardiograms of isolated rabbit hearts and it is evaluated through manually determined references.
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Zaeid, Jabar, and Andreas Lind. "Utveckling av ny teknik för hjärtpulsdetektion." Thesis, KTH, Skolan för informations- och kommunikationsteknik (ICT), 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-211571.

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In this thesis we suggest a technique for detecting pulses by signal processing of a raw ECG signal registered from 4 electrodes located on the left upper arm. The signal processing is performed in Matlab and consists of normalization, lowpass filtering, highpass filtering, derivation, squaring and a moving average window to reduce interference. The technology is capable of extracting periods between heartbeats after an implemented detection algorithm. The thesis also includes reflections on the types of interferences that may affect an electrical development equipment and also methods of how major parts of the interference can be reduced by different shields. Before the technique is applied in a final product, further tests may need to be performed during the monitoring of a person's pulse. Finally, we believe that our development of pulse detection is the beginning of a new technology that in the future can save lives.
I den här rapporten föreslår vi en teknik för att detektera pulser med hjälp av att signalbehandla en rå EKG-signal registrerad från 4 elektroder placerade på vänster överarm. En signalbehandling utförd i Matlab som bland annat består av normering, lågpassfiltrering, högpassfiltrering, derivering, kvadrering samt ett glidande medelvärdesfönster för att reducera störningar. Tekniken är kapabel till att utvinna tider mellan hjärtslag efter en implementerad detekteringsalgoritm. Rapporten innefattar även reflektioner kring vilka typer av störningar som kan påverka en elektrisk utvecklingsutrustning samt metoder för hur större delar av störningarna kan reduceras med hjälp av olika skärmningar. Innan tekniken appliceras i en slutlig produkt kan ytterligare tester behöva utföras under monitorering av en persons puls. Slutligen anser vi att våran utveckling av pulsdetektion är en början på en ny teknik för att kunna rädda liv.
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Books on the topic "Detection of QRS complexes"

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Jones, Michael, Norman Qureshi, and Kim Rajappan. Atrioventricular nodal re-entrant tachycardia. Edited by Patrick Davey and David Sprigings. Oxford University Press, 2018. http://dx.doi.org/10.1093/med/9780199568741.003.0114.

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Atrioventricular nodal re-entrant tachycardia (abbreviated as AVNRT) is one of the five subtypes of supraventricular tachycardia, manifesting most commonly as a regular, narrow QRS complex tachycardia, rate 150–250 min−1 (usually 160–180 min−1), occurring paroxysmally, with P waves either not apparent, or seen to follow the QRS complexes.
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Jones, Michael, Norman Qureshi, and Kim Rajappan. Ventricular tachyarrhythmias: Ventricular tachycardia and ventricular fibrillation. Edited by Patrick Davey and David Sprigings. Oxford University Press, 2018. http://dx.doi.org/10.1093/med/9780199568741.003.0118.

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Ventricular tachyarrhythmias are abnormal patterns of electrical activity arising from the ventricular tissue (myocardium and conduction tissue). Ventricular tachycardia (VT) is an abnormal rapid heart rhythm originating from the ventricles. The rhythm may arise from the ventricular myocardium and/or from the distal conduction system. The normal heart rate is usually regular, between 60 and 100 bpm, and there is synchronized atrial and ventricular contraction. In VT, the ventricles contract at a rate greater than 120 bpm and typically from 150 to 300 bpm, and are no longer coordinated with the atria. There is still organized contraction of the ventricles in VT, with discrete QRS complexes. It is a potentially life-threatening arrhythmia, with the risk of degenerating into ventricular fibrillation and resulting in sudden cardiac death. It is characterized by a broad-complex tachycardia on ECG.
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Jones, Michael, Norman Qureshi, and Kim Rajappan. Atrial flutter. Edited by Patrick Davey and David Sprigings. Oxford University Press, 2018. http://dx.doi.org/10.1093/med/9780199568741.003.0117.

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Atrial flutter is the term given to one of the four types of supraventricular tachycardia; in it, atrial activation occurs as a consequence of a continuous ‘short circuit’: a defined and fixed anatomical route, resulting in a fairly uniform atrial rate, and uniform atrial flutter waves on the ECG. The ventricles are not a part of this arrhythmia circuit, and ventricular activation is variable, dependent on atrioventricular (AV) nodal conduction. Given that the atrial rate is essentially uniform (e.g. 300 min−1), ventricular activation tends to be regular (i.e. 150 min−1, 100 min−1, 75 min−1, etc., if the atrial rate is 300 mins−1), or regularly irregular if changes are occurring in the fraction of conducted impulses to the ventricles. When AV nodal conduction permits only 4:1 conduction or less, atrial flutter is usually obvious, but when ventricular rates are higher (150 min−1 or more) the flutter waves can be obscured by the QRS complexes, making diagnosis more difficult. Atrial flutter is of two types, typical and atypical. Typical atrial flutter is a right atrial tachycardia, with electrical activation proceeding around the tricuspid valve annulus. This arrhythmia is dependent on a zone of slow electrical conduction through the cavotricuspid isthmus (the tissue lying between the origin of the inferior vena cava and the posterior tricuspid valve). The resulting circuit can be either anticlockwise (activation proceeds up the inter-atrial septum, across the atrial roof, down the free wall, and then through the cavotricuspid isthmus to the basal septum) or clockwise (down the inter-atrial septum and around the circuit in the opposite direction). Anticlockwise typical atrial flutter is more common. Atypical atrial flutter refers to all other atrial flutters, and this includes other right atrial flutters (e.g. pericristal flutter), left atrial flutters, post-ablation or post-surgical flutters, and pulmonary vein flutters. The feature common to all types of flutter and which differentiates flutter from other types of supraventricular tachycardia is the presence of a macro-re-entrant anatomical circuit around which the electrical impulse travels continuously and repeatedly, thereby generating the flutter. Even though typical atrial flutter has a fairly obvious and specific appearance on the ECG, atypical flutters do not, and often it is only possible to differentiate atypical flutter from atrial tachycardias by invasive electrophysiology studies, as the ECG alone may be insufficient.
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Muche, Marion, and Seema Baid-Agrawal. Hepatitis B. Edited by Vivekanand Jha. Oxford University Press, 2018. http://dx.doi.org/10.1093/med/9780199592548.003.0185_update_001.

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Hepatitis B virus (HBV) has been causally linked to a variety of renal diseases, the most common being glomerular diseases and systemic autoimmune disease. Membranous nephropathy (MN) is the commonest HBV-associated glomerulonephritis (HBV-GN), followed by membranoproliferative glomerulonephritis (MPGN), mesangial proliferative glomerulonephritis, immunoglobulin (Ig)-A nephropathy, and focal segmental glomerulosclerosis (FSGS). Polyarteritis nodosa is a rare manifestation. The incidence of HBV-associated renal diseases seems to be decreasing with the introduction of vaccination programmes.HBV-MN is the most frequent cause of nephrotic syndrome in children in countries with high endemicity of HBV infection. The clinical course is usually benign in children with high rates of spontaneous remission rates and low risk of progression to renal failure. The prognosis is worse in adults. Of the systemic autoimmune disorders associated with HBV infection that involve the kidneys, the strongest link has been found with polyarteritis nodosa (PAN), a lesion that causes arteritis of medium-sized renal vessels. HBV-associated PAN (HBV-PAN) usually manifests in the first year after infection, and is clinically indistinguishable from classic PAN.Diagnosis of HBV-GN or -PAN is based on the clinical picture, histological findings, evidence of viral replication in serum and/or liver and detection of HBV antigens or DNA in the tissue. Besides deposition of immune complexes, other mechanisms such as virus-induced cytopathic damage have been proposed to explain the pathogenesis.HBV-GN and HBV-PAN appear to respond to antiviral treatment. Both show remission after HBeAg seroconversion. The available studies predominantly employed first-generation agents like interferon alpha and lamivudine, which showed suppression of viral replication and clinical remission of HBV-associated renal disease. Immunosuppressive therapy appears to be inevitable for the control of severe HBV-PAN and could be helpful in addition to antiviral treatment for cases of HBV-GN not responding clinically to antiviral treatment.
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Book chapters on the topic "Detection of QRS complexes"

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Yoo, Kil-sang, and Won-hyung Lee. "QRS Complexes Detection in Electrocardiogram Signals Based on Multiresolution Analysis." In Lecture Notes in Electrical Engineering, 153–58. Dordrecht: Springer Netherlands, 2012. http://dx.doi.org/10.1007/978-94-007-5064-7_23.

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Gupta, Lalita. "QRS Complex Detection Algorithm for Wearable Devices." In Lecture Notes in Electrical Engineering, 167–75. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-7031-5_16.

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Liao, Lijuan, Wei Zhong, Xuemei Guo, and Guoli Wang. "A Mixed Approach for Fetal QRS Complex Detection." In Proceedings of 2018 Chinese Intelligent Systems Conference, 387–95. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-2288-4_38.

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Elmansouri, Khalifa, Rachid Latif, and Fadel Maoulainine. "Difference Spectrum Energy Applied for Fetal QRS Complex Detection." In Lecture Notes in Electrical Engineering, 419–28. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-30301-7_44.

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Henzel, Norbert. "QRS Complex Detection Based on Ensemble Empirical Mode Decomposition." In Innovations in Biomedical Engineering, 286–93. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-47154-9_33.

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Scherz, Wilhelm Daniel, Juan Antonio Ortega, Ralf Seepold, and Natividad Martínez Madrid. "Stress Determent via QRS Complex Detection, Analysis and Pre-processing." In Mobile Networks for Biometric Data Analysis, 225–34. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-39700-9_18.

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Śmigiel, Sandra, and Tomasz Marciniak. "Detection of QRS Complex with the Use of Matched Filtering." In Innovations in Biomedical Engineering, 310–22. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-47154-9_36.

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Mohamed, Hamdi M., and Akula Rajani. "Electrocardiogram QRS Complex Detection Based on Quantization-Level Population Analysis." In Advances in Intelligent Systems and Computing, 967–88. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-15-8443-5_82.

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Yu, Hang, Lixiao Ma, Ru Wang, Lai Jiang, Yan Li, Zhen Ji, Yan Pingkun, and Wang Fei. "A FPGA-Based Real Time QRS Complex Detection System Using Adaptive Lifting Scheme." In Lecture Notes in Electrical Engineering, 497–504. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-28807-4_69.

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Hu, Xiao, Jingjing Liu, Jiaqing Wang, and Zhong Xiao. "Detection of Onset and Offset of QRS Complex Based a Modified Triangle Morphology." In Lecture Notes in Electrical Engineering, 2893–901. Dordrecht: Springer Netherlands, 2013. http://dx.doi.org/10.1007/978-94-007-7618-0_367.

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Conference papers on the topic "Detection of QRS complexes"

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Kotas, Marian, Janusz Jezewski, Tomasz Kupka, and Krzysztof Horoba. "Detection of low amplitude fetal QRS complexes." In 2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE, 2008. http://dx.doi.org/10.1109/iembs.2008.4650278.

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Paralic, Martin. "Detection of QRS Complexes Using Convolutional Neural Network." In 2019 42nd International Conference on Telecommunications and Signal Processing (TSP). IEEE, 2019. http://dx.doi.org/10.1109/tsp.2019.8768867.

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Shuyan, Wang. "Automatic Detection of QRS Complexes using Quantum Neural Networks." In 2008 International Conference on Biomedical Engineering And Informatics (BMEI). IEEE, 2008. http://dx.doi.org/10.1109/bmei.2008.19.

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Xiuyu Zheng, Zhen Li, LinLin Shen, and Zhen Ji. "Detection of QRS Complexes Based on Biorthogonal Spline Wavelet." In 2008 International Symposium on Information Science and Engineering (ISISE). IEEE, 2008. http://dx.doi.org/10.1109/isise.2008.61.

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Hassen, Amina El, Aymeric Histace, Mehdi Terosiet, and Olivier Romain. "FPGA-based detection of QRS complexes in ECG signal." In 2015 Conference on Design and Architectures for Signal and Image Processing (DASIP). IEEE, 2015. http://dx.doi.org/10.1109/dasip.2015.7367244.

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Choudhary, Shikha, and S. T. Hamde. "A simple and robust algorithm for the detection of QRS complexes." In 2015 International Conference on Industrial Instrumentation and Control (ICIC). IEEE, 2015. http://dx.doi.org/10.1109/iic.2015.7150865.

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Chen, Wenli, Zhiwen Mo, and Wen Guo. "Detection of QRS Complexes Using Wavelet Transforms and Golden Section Search." In International Conference on Intelligent Systems and Knowledge Engineering 2007. Paris, France: Atlantis Press, 2007. http://dx.doi.org/10.2991/iske.2007.32.

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Das, Manab Kr, Samit Ari, and Swagatika Priyadharsini. "On an algorithm for detection of QRS complexes in noisy electrocardiogram signal." In 2011 Annual IEEE India Conference (INDICON). IEEE, 2011. http://dx.doi.org/10.1109/indcon.2011.6139345.

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Li, Yan, Hang Yu, Lai Jiang, Lixiao Ma, and Zhen Ji. "Adaptive Lifting Scheme for ECG QRS complexes detection and its FPGA implementation." In 2010 3rd International Conference on Biomedical Engineering and Informatics (BMEI). IEEE, 2010. http://dx.doi.org/10.1109/bmei.2010.5640073.

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Vulaj, Zoja, Milos Brajovic, Andela Draganic, and Irena Orovic. "Detection of irregular QRS complexes using Hermite transform and support vector machine." In 2017 International Symposium ELMAR. IEEE, 2017. http://dx.doi.org/10.23919/elmar.2017.8124435.

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