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Journal articles on the topic 'Detection of QRS complexes'

Author: Grafiati

Published: 28 June 2021

Last updated: 29 July 2025

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1

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 deflectio

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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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3

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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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 (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, whi

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Śmigiel, Sandra, Krzysztof Pałczyński, and Damian Ledziński. "Deep Learning Techniques in the Classification of ECG Signals Using R-Peak Detection Based on the PTB-XL Dataset." Sensors 21, no. 24 (2021): 8174. http://dx.doi.org/10.3390/s21248174.

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Deep Neural Networks (DNNs) are state-of-the-art machine learning algorithms, the application of which in electrocardiographic signals is gaining importance. So far, limited studies or optimizations using DNN can be found using ECG databases. To explore and achieve effective ECG recognition, this paper presents a convolutional neural network to perform the encoding of a single QRS complex with the addition of entropy-based features. This study aims to determine what combination of signal information provides the best result for classification purposes. The analyzed information included the raw

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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 (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 a

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Tariq, M. Younes, Alkhedher Mohammad, Al Khawaldeh Mohamad, Nawash Jalal, and Al-Abbas Ibrahim. "Less computational approach to detect QRS complexes in ECG rhythms." Computer Science and Information Technologies 2, no. 3 (2021): 113–20. https://doi.org/10.11591/csit.v2i3.p113-120.

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Electrocardiogram (ECG) signals are normally affected by artifacts that require manual assessment or use of other reference signals. Currently, Cardiographs are used to achieve basic necessary heart rate monitoring in real conditions. This work aims to study and identify main ECG features, QRS complexes, as one of the steps of a comprehensive ECG signal analysis. The proposed algorithm suggested an automatic recognition of QRS complexes in ECG rhythm. This method is designed based on several filter structure composes low pass, difference and summation filters. The filtered signal is fed to an

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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 (TE

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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 (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 n

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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 (2010): 241–56. http://dx.doi.org/10.1016/j.cmpb.2009.09.005.

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Budanova, M. A., M. P. Chmelevsky, T. V. Treshkur, A. V. Aseev, and V. M. Tikhonenko. "Automatic detection of ventricular and supraventricular wide QRS arrhythmias using complex of morphological criteria and algorithms." Kardiologiia 59, no. 3S (2019): 36–42. http://dx.doi.org/10.18087/cardio.2659.

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Aim. The aim of study is a detection of ventricular and supraventricular wide QRS arrhythmias using complex of morphological criteria and algorithms by method of automatic analysis. Materials and methods. For 100 patients (m/f – 61/39, Me (min; max) – 44.5 (10; 85) years) of researched group the analysis of 14306 single wide ectopic complexes (QRS 120–230 ms) has been done. Wide complexes include 11028 (77%) ventricular complexes and 3278 (23%) supraventricular complexes represented by 145 different forms of QRS. For verification of arrhythmias origin transesophageal ECG recording and endocard

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Guo, Zheng, Siqi Li, Kaicong Chen, and Xuehui Zang. "Robust QRS complex detection in noisy electrocardiogram based on underdamped periodic stochastic resonance." AIMS Bioengineering 10, no. 3 (2023): 283–99. http://dx.doi.org/10.3934/bioeng.2023018.

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<abstract> <p>Robust QRS detection is crucial for accurate diagnosis and monitoring of cardiovascular diseases. During the detection process, various types of noise and artifacts in the electrocardiogram (ECG) can degrade the accuracy of algorithm. Previous QRS detectors have employed various filtering methods to minimize the negative impact of noise. However, their performance still significantly deteriorates in large-noise environments. To further enhance the robustness of QRS detectors on noisy electrocardiograms (ECGs), we proposed a QRS detection algorithm based on an underdam

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Huang, Sheng-Chieh, Hui-Min Wang та Wei-Yu Chen. "A ±6 ms-Accuracy, 0.68 mm2, and 2.21 μW QRS Detection ASIC". VLSI Design 2012 (22 листопада 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 da

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Li, Zihao, Wenliang Zhu, Yiheng Xu, et al. "An Artificial Intelligence QRS Detection Algorithm for Wearable Electrocardiogram Devices." Micromachines 16, no. 6 (2025): 631. https://doi.org/10.3390/mi16060631.

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At the core of AI-driven electrocardiogram diagnosis lies the precise localization of the QRS complex. While QRS detection methods for multiple leads have been researched adequately in the last few decades, their multi-lead strategies still need to be designed manually. Therefore, a QRS detector that can fuse multiple leads automatically is still worth investigating. Methods: The proposed QRS detector comprises a leads-distillation module (LDM) and a QRS detection module. The LDM can distill multi-lead signals into single-lead ones. This procedure minimizes the weight proportions assigned to n

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Dr., S. S. Mehta, and Kulshrestha Shubhi. "Automated QRS Detection using Empirical Mode Decomposition and K-Means." International Journal of Innovative Science and Research Technology 8, no. 2 (2023): 419–26. https://doi.org/10.5281/zenodo.7655902.

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This paper proposes an algorithm using Empirical Mode Decomposition (EMD) and k-means for the detection of QRS complexes present in the ECG signal. EMD is an innovative method for decomposing any time varying, nonlinear and non-stationerysignal into a set of intrinsic mode functions (IMF). This automated algorithm is applied to the filtered ECG signal for its decomposition into its intrinsic components and further its classification is done using k-means classifier. Dataset-3 of the CSE multi-lead measurement library is used for validating the performance of the algorithm. Detection rate of th

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Rajeswari, Satuluri Venkata Kanaka Raja, and Ponnusamy Vijayakumar. "Survey on electrocardiography signal analysis and diabetes mellitus: unraveling the complexities and complications." International Journal of Electrical and Computer Engineering (IJECE) 14, no. 2 (2024): 1565–71. https://doi.org/10.11591/ijece.v14i2.pp1565-1571.

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Electrocardiography (ECG) is crucial in the medical field to assess cardiovascular diseases. ECG signal generates information, i.e., QRS complexes that imply the cardiac health of the human body. It is depicted in the form of a graph with voltage versus time interval. A distorted, inverted, lagged, small waveform implies an abnormality in a cardiac system. This study highlights the generation of an ECG signal, QRS complexes undertoned towards different diseases, event detection, and signal processing methods. It has become crucial to highlight the possibilities and advances that can be derived

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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 (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 impr

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T, Thulasimani, VenkataRamana K, Sudhakar D, Kalai Selvi T, and Satheesh Kumar D. "BREAKING BARRIERS: ADVANCED SIGNAL PROCESSING IN EMBEDDED SYSTEMS WITH STATE-OF-THE-ART ALGORITHMS." ICTACT Journal on Microelectronics 10, no. 2 (2024): 1795–99. https://doi.org/10.21917/ijme.2024.0310.

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Advanced signal processing techniques are critical in the early detection and classification of cardiac abnormalities. This study addresses the challenge of detecting QRS-complexes and classifying arrhythmias in embedded systems. Traditional methods often struggle with high false detection rates and computational inefficiencies. Our approach leverages Long Short-Term Memory (LSTM) networks to enhance detection accuracy and classification performance by integrating hybridized features from electrocardiogram (ECG) signals. We propose a novel framework that combines time-domain features with freq

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Raja Rajeswari, Satuluri Venkata Kanaka, and Ponnusamy Vijayakumar. "Survey on electrocardiography signal analysis and diabetes mellitus: unraveling the complexities and complications." International Journal of Electrical and Computer Engineering (IJECE) 14, no. 2 (2024): 1565. http://dx.doi.org/10.11591/ijece.v14i2.pp1565-1571.

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Electrocardiography (ECG) is crucial in the medical field to assess cardiovascular diseases. ECG signal generates information, i.e., QRS complexes that imply the cardiac health of the human body. It is depicted in the form of a graph with voltage versus time interval. A distorted, inverted, lagged, small waveform implies an abnormality in a cardiac system. This study highlights the generation of an ECG signal, QRS complexes undertoned towards different diseases, event detection, and signal processing methods. It has become crucial to highlight the possibilities and advances that can be derived

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20

Homaeinezhad, Mohammad Reza, Seyyed Amir Hoseini Sabzevari, Ali Ghaffari, and Mohammad Daevaeiha. "High-Accuracy Characterization of Ambulatory Holter Electrocardiogram Events." International Journal of Systems Biology and Biomedical Technologies 1, no. 3 (2012): 40–71. http://dx.doi.org/10.4018/ijsbbt.2012070102.

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In this paper, three noise-robust high-accuracy methods aiming at the detection and delineation of the electrocardiogram (ECG) events (QRS complex, P-wave, T-wave) were developed. The ECG signal was initially appropriately preprocessed by application of a bandpass FIR filter and Discrete Wavelet Transform (DWT). The first detection-delineation method was the Walsh-Hadamard Transform (WHT). The WHT coefficients were divided into two groups and the signal was reconstructed using the second group coefficients. By this reconstruction, the values of first derivative of events are made stronger rath

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Kurniawan, Arief, Eko Mulyanto Yuniarno, Eko Setijadi, Mochamad Yusuf Alsagaff, Gijsbertus Jacob Verkerke, and I. Ketut Eddy Purnama. "Detection of multi-class arrhythmia using heuristic and deep neural network on edge device." International Journal of Advances in Intelligent Informatics 9, no. 3 (2023): 429. http://dx.doi.org/10.26555/ijain.v9i3.1061.

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Heart disease is a heart condition that sometimes causes a person to die suddenly. One indication is a rhythm disorder known as arrhythmia. Multi-class Arrhythmia Detection has followed: QRS complex detection procedure and arrhythmia classification based on the QRS complex morphology. We proposed an edge device that detects QRS complexes based on variance analysis (QVAT) and the arrhythmia classification based on the QRS complex spectrogram. The classifier uses two-dimensional convolutional neural network (2D CNN) deep learning. We use a single board computer and neural network compute stick t

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Moeyersons, Jonathan, Matthew Amoni, Sabine Van Huffel, Rik Willems, and Carolina Varon. "R-DECO: an open-source Matlab based graphical user interface for the detection and correction of R-peaks." PeerJ Computer Science 5 (October 21, 2019): e226. http://dx.doi.org/10.7717/peerj-cs.226.

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Many of the existing electrocardiogram (ECG) toolboxes focus on the derivation of heart rate variability features from RR-intervals. By doing so, they assume correct detection of the QRS-complexes. However, it is highly likely that not all detections are correct. Therefore, it is recommended to visualize the actual R-peak positions in the ECG signal and allow manual adaptations. In this paper we present R-DECO, an easy-to-use graphical user interface (GUI) for the detection and correction of R-peaks. Within R-DECO, the R-peaks are detected by using a detection algorithm which uses an envelope-

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CHIU, CHUANG-CHIEN, TONG-HONG LIN, and BEN-YI LIAU. "USING CORRELATION COEFFICIENT IN ECG WAVEFORM FOR ARRHYTHMIA DETECTION." Biomedical Engineering: Applications, Basis and Communications 17, no. 03 (2005): 147–52. http://dx.doi.org/10.4015/s1016237205000238.

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Arrhythmia is one kind of diseases that gives rise to the death and possibly forms the immedicable danger. The most common cardiac arrhythmia is the ventricular premature beat. The main purpose of this study is to develop an efficient arrhythmia detection algorithm based on the morphology characteristics of arrhythmias using correlation coefficient in ECG signal. Subjects for experiments included normal subjects, patients with atrial premature contraction (APC), and patients with ventricular premature contraction (PVC). So and Chan's algorithm was used to find the locations of QRS complexes. W

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Mehta, S. S., and N. S. Lingayat. "Detection of QRS complexes in electrocardiogram using support vector machine." Journal of Medical Engineering & Technology 32, no. 3 (2008): 206–15. http://dx.doi.org/10.1080/03091900701507183.

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Yeh, Yun-Chi, and Wen-June Wang. "QRS complexes detection for ECG signal: The Difference Operation Method." Computer Methods and Programs in Biomedicine 91, no. 3 (2008): 245–54. http://dx.doi.org/10.1016/j.cmpb.2008.04.006.

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Zhong, Wei, Li Mao, and Wei Du. "A signal quality assessment method for fetal QRS complexes detection." Mathematical Biosciences and Engineering 20, no. 5 (2023): 7943–56. http://dx.doi.org/10.3934/mbe.2023344.

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<abstract> <sec><title>Objective</title><p>Non-invasive fetal ECG (NI-FECG) provides a non-invasive method to monitor the health of the fetus. However, the NI-FECG is easily interfered by noise, which makes the signal quality decline, leading to the fetal heart rate (FHR) monitoring becoming a challenging task.</p> </sec> <sec><title>Methods</title><p>In this work, an algorithm for dynamic evaluation of signal quality is proposed to improve the multi-channel FHR monitoring. The innovation of the method is to assess the signal qu

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Viunytskyi, Oleh, Vyacheslav Shulgin, Alexander Totsky, and Valery Sharonov. "FETAL QRS-COMPLEXES DETECTECTIONS IN ABDOMINAL SIGNAL BY USING WAVELET-BISPECTRUM." ГРААЛЬ НАУКИ, no. 6 (July 4, 2021): 164–69. http://dx.doi.org/10.36074/grail-of-science.25.06.2021.028.

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Fetal hypoxia or distress is a physical stress experienced by a fetus due to a lack of oxygen. Intrauterine hypoxia and the resultant perinatal brain damages may lead to extraordinary effects, including continuous lifelong treatments. One of the ways for detecting symptoms of hypoxia is monitoring of the fetus heart activity. At present, the basic method of monitoring the condition of unborn baby is the ultrasound cardiotocography (CTG). Considerably more information for early detection of the fetal hypoxia may be obtained by analyzing fetal electrocardiogram (FECG).

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Kristof, Florian, Maximilian Kapsecker, Leon Nissen, et al. "QRS detection in single-lead, telehealth electrocardiogram signals: Benchmarking open-source algorithms." PLOS Digital Health 3, no. 8 (2024): e0000538. http://dx.doi.org/10.1371/journal.pdig.0000538.

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Background and objectives A key step in electrocardiogram (ECG) analysis is the detection of QRS complexes, particularly for arrhythmia detection. Telehealth ECGs present a new challenge for automated analysis as they are noisier than traditional clinical ECGs. The aim of this study was to identify the best-performing open-source QRS detector for use with telehealth ECGs. Methods The performance of 18 open-source QRS detectors was assessed on six datasets. These included four datasets of ECGs collected under supervision, and two datasets of telehealth ECGs collected without clinical supervisio

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Pałczyński, Krzysztof, Sandra Śmigiel, Damian Ledziński, and Sławomir Bujnowski. "Study of the Few-Shot Learning for ECG Classification Based on the PTB-XL Dataset." Sensors 22, no. 3 (2022): 904. http://dx.doi.org/10.3390/s22030904.

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The electrocardiogram (ECG) is considered a fundamental of cardiology. The ECG consists of P, QRS, and T waves. Information provided from the signal based on the intervals and amplitudes of these waves is associated with various heart diseases. The first step in isolating the features of an ECG begins with the accurate detection of the R-peaks in the QRS complex. The database was based on the PTB-XL database, and the signals from Lead I–XII were analyzed. This research focuses on determining the Few-Shot Learning (FSL) applicability for ECG signal proximity-based classification. The study was

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Meyer, C., J. F. Gavela, and M. Harris. "Combining Algorithms in Automatic Detection of QRS Complexes in ECG Signals." IEEE Transactions on Information Technology in Biomedicine 10, no. 3 (2006): 468–75. http://dx.doi.org/10.1109/titb.2006.875662.

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Zhong, Wei, Xuemei Guo, and Guoli Wang. "QRStree: A prefix tree-based model to fetal QRS complexes detection." PLOS ONE 14, no. 10 (2019): e0223057. http://dx.doi.org/10.1371/journal.pone.0223057.

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Van, G. V., and K. V. Podmasteryev. "Algorithm for detection the QRS complexes based on support vector machine." Journal of Physics: Conference Series 929 (November 2017): 012041. http://dx.doi.org/10.1088/1742-6596/929/1/012041.

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Ehab, Abdul Razzaq Hussein, Shaban Hassooni Ali, and Al-Libawy Hilal. "Detection of electrocardiogram QRS complex based on modified adaptive threshold." International Journal of Electrical and Computer Engineering (IJECE) 9, no. 5 (2019): 3512–21. https://doi.org/10.11591/ijece.v9i5.pp3512-3521.

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It is essential for medical diagnoses to analyze Electrocardiogram (ECG signal). The core of this analysis is to detect the QRS complex. A modified approach is suggested in this work for QRS detection of ECG signals using existing database of arrhythmias. The proposed approach starts with the same steps of previous approaches by filtering the ECG. The filtered signal is then fed to a differentiator to enhance the signal. The modified adaptive threshold method which is suggested in this work, is used to improve QRS complex detection rate. This method uses a new approach for adapting threshold l

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G., K. Singh, Sharma A., and Velusami S. "Automatic Detection of diagnostic features using real-time ECG signals: Application to patients prone to Cardiac Arrhythmias." International Journal of BioSciences and Technology (IJBST) ISSN: 0974-3987 2, no. 7 (2009): 96–125. https://doi.org/10.5281/zenodo.1436599.

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<strong>ABSTRACT</strong> A composite method for the automatic detection of diagnostic features related to the depolarization sequence (P-QRS complex) of the heart, for arrhythmia classification, using single lead ECG is presented. The non-syntactic approach based upon slope and amplitude thresholds along with a set of empirical criteria is employed for segmenting QRS complexes from a variety of noisy ECG recordings acquired from the MIT/BIH arrhythmia database. The background noise is removed from the non-QRS portions using an appropriate filtering method that causes no change in the amplitud

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Śmigiel, Sandra. "ECG Classification Using Orthogonal Matching Pursuit and Machine Learning." Sensors 22, no. 13 (2022): 4960. http://dx.doi.org/10.3390/s22134960.

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Health monitoring and related technologies are a rapidly growing area of research. To date, the electrocardiogram (ECG) remains a popular measurement tool in the evaluation and diagnosis of heart disease. The number of solutions involving ECG signal monitoring systems is growing exponentially in the literature. In this article, underestimated Orthogonal Matching Pursuit (OMP) algorithms are used, demonstrating the significant effect of concise representation parameters on improving the performance of the classification process. Cardiovascular disease classification models based on classical Ma

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Sema, Yildirim. "An Overview of ECG Artifact Detection in EEG Signals." Journal of Cardiovascular Medicine and Cardiology 12, no. 2 (2025): 017–21. https://doi.org/10.17352/2455-2976.000222.

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Electroencephalography (EEG) is an important technique for recording brain signals and is particularly used in the diagnosis and treatment of neurological diseases such as epilepsy. However, due to the complex nature of EEG signals, their interpretation is difficult and time-consuming. In EEG recordings, physiological noises such as eye movements (EOG) and electrocardiography (ECG) can affect the signals and hinder accurate diagnosis. This study emphasizes the importance of removing noise from EEG signals, with a focus on the impact of ECG-induced noise. The detection of QRS complexes in the E

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Mathur, P., and V. S. Chouhan. "Implementation of K-Nearest Neighbor (KNN) algorithm for detection of QRS Complexes." International Journal of Computer Sciences and Engineering 6, no. 8 (2018): 77–79. http://dx.doi.org/10.26438/ijcse/v6i8.7779.

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Researcher. "DETECTION AND CLASSIFICATION OF ECG BY USING BAYESIAN REGULARIZATION NEURAL NETWORK." International Journal of Advanced Research in Engineering and Technology (IJARET) 15, no. 5 (2024): 82–88. https://doi.org/10.5281/zenodo.13901918.

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Automatic Detection and classification of Cardiac abnormalities and Arrhythmias from a limited number of ECG signals is of considerable importance in critical care or operating room patient monitoring. We propose a method to accurately classify the heartbeat of ECG signals through the Neural Networks. Feature sets are based on QRS complex of the ECG signal. It is difficult to detect P and T wave due to the overlaps and variations in amplitudes of other signals. In this paper we propose a method for Automatic Detection and classification of the P, QRS and T wave. Bayesian regularization neural

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Haq, Tashreque Mohammed, Safkat Arefin, Shamiur Rahman, and Tanzilur Rahman. "Extraction of Fetal Heart Rate from Maternal ECG—Non Invasive Approach for Continuous Monitoring during Labor." Proceedings 2, no. 13 (2018): 1009. http://dx.doi.org/10.3390/proceedings2131009.

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Here, we propose a signal processing based approach for the extraction of the fetal heart rate (FHR) from Maternal Abdominal ECG (MAECG) in a non-invasive way. Datasets from a Physionet database has been used in this study for evaluating the performance of the proposed model that performs three major tasks; preprocessing of the MAECG signal, separation of Fetal QRS complexes from that of maternal and estimation of Fetal R peak positions. The MAECG signal is first preprocessed with improved multistep filtering techniques to detect the Maternal QRS (MQRS) complexes, which are dominant in the MAE

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Hanser, F., B. Pfeifer, M. Seger, et al. "A Signal Processing Pipeline for Noninvasive Imaging of Ventricular Preexcitation." Methods of Information in Medicine 44, no. 04 (2005): 508–15. http://dx.doi.org/10.1055/s-0038-1634001.

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Summary Objectives: Noninvasive imaging of the cardiac activation sequence in humans could guide interventional curative treatment of cardiac arrhythmias by catheter ablation. Highly automated signal processing tools are desirable for clinical acceptance. The developed signal processing pipeline reduces user interactions to a minimum, which eases the operation by the staff in the catheter laboratory and increases the reproducibility of the results. Methods: A previously described R-peak detector was modified for automatic detection of all possible targets (beats) using the information of all l

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Kumari, Shantha Selva, and V. Sadasivam. "QRS COMPLEX DETECTION USING DOUBLE DENSITY DISCRETE WAVELET TRANSFORM." Biomedical Engineering: Applications, Basis and Communications 20, no. 02 (2008): 65–73. http://dx.doi.org/10.4015/s1016237208000660.

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In this paper, an offline double density discrete wavelet transform based QRS complex detection of the electrocardiogram signal is discussed. Baseline wandering present in the signal is removed by using the double density discrete wavelet transformed approximation coefficients of the signal. The results are more accurate than other methods with less effort. This is an unsupervised method allowing the process to be used in offline automatic analysis of electrocardiogram. The measurement of timing intervals of ECG signal by automated system is highly superior to its subjective analysis. The hear

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Huang, Zhuya, Junsheng Yu, Ying Shan, and Xiangqing Wang. "A Non-Invasive Fetal QRS Complex Detection Method Based on a Multi-Feature Fusion Neural Network." Applied Sciences 14, no. 19 (2024): 8987. http://dx.doi.org/10.3390/app14198987.

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Fetal heart monitoring, as a crucial part of fetal monitoring, can accurately reflect the fetus’s health status in a timely manner. To address the issues of high computational cost, inability to observe fetal heart morphology, and insufficient accuracy associated with the traditional method of calculating the fetal heart rate using a four-channel maternal electrocardiogram (ECG), a method for extracting fetal QRS complexes from a single-channel non-invasive fetal ECG based on a multi-feature fusion neural network is proposed. Firstly, a signal entropy data quality detection algorithm based on

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Guaragnella, Cataldo, Maria Rizzi, and Agostino Giorgio. "Marginal Component Analysis of ECG Signals for Beat-to-Beat Detection of Ventricular Late Potentials." Electronics 8, no. 9 (2019): 1000. http://dx.doi.org/10.3390/electronics8091000.

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Heart condition diagnosis based on electrocardiogram signal analysis is the basic method used in prevention of cardiovascular diseases, which are recognized as the leading cause of death globally. To anticipate the occurrence of ventricular arrhythmia, the detection of Ventricular Late Potentials (VLPs) is clinically worthwhile. VLPs are low-amplitude and high-frequency signals appearing at the end part of QRS complexes in the electrocardiogram, which can be considered as a robust feature for arrhythmia risk stratification in patients with cardiac diseases. This paper proposes a beat-to-beat V

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Fatima, Yasmeen, A. Mallick M., and U. Khan Y. "Detection of Real Time QRS Complex Using Wavelet Transform." International Journal of Electrical and Computer Engineering (IJECE) 8, no. 5 (2018): 2857–63. https://doi.org/10.11591/ijece.v8i5.pp2857-2863.

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This paper presents a novel method for QRS detection. To accomplish this task ECG signal was first filtered by using a third order Savitzky Golay filter. The filtered ECG signal was then preprocessed by a Wavelet based denoising in a real-time fashion to minimize the undefined noise level. R-peak was then detected from denoised signal after wavelet denoising. Windowing mechanism was also applied for finding any missing R-peaks. All the 48 records have been used to test the proposed method. During this testing, 99.97% sensitivity and 99.99% positive predictivity is obtained for QRS complex dete

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Hu, Xiao, Jingjing Liu, Jiaqing Wang, Zhong Xiao, and Jing Yao. "Automatic detection of onset and offset of QRS complexes independent of isoelectric segments." Measurement 51 (May 2014): 53–62. http://dx.doi.org/10.1016/j.measurement.2014.01.011.

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Mehta, S. S., D. A. Shete, N. S. Lingayat, and V. S. Chouhan. "K-means algorithm for the detection and delineation of QRS-complexes in Electrocardiogram." IRBM 31, no. 1 (2010): 48–54. http://dx.doi.org/10.1016/j.irbm.2009.10.001.

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Ben Slama, Amine, Hanene Sahli, Ramzi Maalmi, and Hedi Trabelsi. "ConvNet: 1D-Convolutional Neural Networks for Cardiac Arrhythmia Recognition Using ECG Signals." Traitement du Signal 38, no. 6 (2021): 1737–45. http://dx.doi.org/10.18280/ts.380617.

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In healthcare, diagnostic tools of cardiac diseases are commonly known by the electrocardiogram (ECG) analysis. Atypical electrical activity can produce a cardiac arrhythmia. Various difficulties can be imposed to clinicians e.g., myocardial infarction arrhythmia via the non-stationarity and irregularity heart beat signals. Through the assistance of computer-aided diagnosis methods, timely specification of arrhythmia diseases reduces the mortality rate of affected patients. In this study, a 1 Lead QRS complex -layer deep convolutional neural network is proposed for the recognition of arrhythmi

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Wang, Jie, Chung-Chih Lin, Yan-Shuo Yu, and Tsang-Chu Yu. "Wireless Sensor-Based Smart-Clothing Platform for ECG Monitoring." Computational and Mathematical Methods in Medicine 2015 (2015): 1–8. http://dx.doi.org/10.1155/2015/295704.

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The goal of this study is to use wireless sensor technologies to develop a smart clothes service platform for health monitoring. Our platform consists of smart clothes, a sensor node, a gateway server, and a health cloud. The smart clothes have fabric electrodes to detect electrocardiography (ECG) signals. The sensor node improves the accuracy of QRS complexes detection by morphology analysis and reduces power consumption by the power-saving transmission functionality. The gateway server provides a reconfigurable finite state machine (RFSM) software architecture for abnormal ECG detection to s

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Rauf, Muhammad Abdur, Sarmad Raza, Shoaib Iqbal Safi, Yasir Arafat, Jehandad Khan, and Noman Khan. "Frequency of cardiac events in patients admitted with dengue fever at Kuwait Teaching Hospital Peshawar." International journal of health sciences 7, S1 (2023): 85–92. http://dx.doi.org/10.53730/ijhs.v7ns1.14156.

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Objectives: To determine the frequency of various cardiac events in patients admitted with dengue fever at Kuwait Teaching Hospital, Peshawar. Materials and methods: NS1 viral antigen detection method was used for serologic confirmation of dengue virus infection. All patients provided their informed permission before being assigned to a sample or having their data used in study. The researcher took the history and confirmed the presence of risk factors like hypertension and diabetes and along with that ECG and echocardiographic evaluation of all the patients at the time of admission and discha

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De Marco, Fabiola, Filomena Ferrucci, Michele Risi, and Genoveffa Tortora. "Classification of QRS complexes to detect Premature Ventricular Contraction using machine learning techniques." PLOS ONE 17, no. 8 (2022): e0268555. http://dx.doi.org/10.1371/journal.pone.0268555.

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Detection of Premature Ventricular Contractions (PVC) is of crucial importance in the cardiology field, not only to improve the health system but also to reduce the workload of experts who analyze electrocardiograms (ECG) manually. PVC is a non-harmful common occurrence represented by extra heartbeats, whose diagnosis is not always easily identifiable, especially when done by long-term manual ECG analysis. In some cases, it may lead to disastrous consequences when associated with other pathologies. This work introduces an approach to identify PVCs using machine learning techniques without feat

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