Journal articles: '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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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 (April 13, 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 endocardial electrophysiological study were done. The control group included 59 patients (m/f – 25/34, Me (min; max) – 49.5 (14,85) years) with 720 wide QRS, including 467 (65%) ventricular and 253 (35%) supraventricular complexes represented by 86 forms of QRS. The criteria Drew B.J., Scheinman M.M. (1995); Wellens H.J. (1978), RWPT II (Pava LF, 2010) and the algorithms of Brugada P. (1991); Bayesian (2000); Vereckei A. (2008) were used to evaluate sensitivity, specificity and diagnostic accuracy of wide QRS complexes recognition one by one and together, using the method of Wald sequential automatic analysis (KT Result3, CJSC INCART, Russia) and method of artificial neural networks. Results. The best results for the detection of ventricular arrhythmias algorithms were demonstrated by the Brugada P., Drew B.J., Scheinman M.M. algorithm (sensitivity 86.43%, specificity 66.73%, diagnostic accuracy 82.14% in the study group, sensitivity 81.80%, specificity 73.12%, diagnostic accuracy 78.75% in the control group), and the Bayesian algorithm (sensitivity 87.81%, specificity 73.62%, diagnostic accuracy 84.72% in the study group, sensitivity 83.30%, specificity 77.08%, diagnostic accuracy 81.11% in the control group). A complex analysis of the Wald method recognized ventricular arrhythmias in the research group with sensitivity 83.11%, specificity 83.65%, diagnostic accuracy 83.23% and in the control group with a sensitivity 83.51%, specificity of 84.58% and diagnostic accuracy 83.89%. Artificial neural networks recognized ventricular arrhythmias with sensitivity 91.43%, specificity 91.30% and diagnostic accuracy 91.39% in the control group and with sensitivity 97.06%, specificity 99.39% and diagnostic accuracy 97.6% in the research group. Conclusion. Automatic analysis allows obtaining simultaneously the results of each algorithms/criteria and in combination. It significantly reduces the doctor’s work in assessing of amplitude-time characteristics of the complexes. Using artificial neural networks increases the accuracy of of ventricular and supraventricular arrhythmias recognition.

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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 (January 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 (September 2008): 245–54. http://dx.doi.org/10.1016/j.cmpb.2008.04.006.

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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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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 (July 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 rather than the values of other parts of signal. In the second method, a feed forward artificial neural network was implemented to detect all events of the ECG signal. In the third method, the first derivative of signal was computed using a new signal smoothing algorithm with corresponding statistical properties. For decreasing False Positive (FP) errors associated with P-wave detection, a discriminating border was introduced as the post processing stage specified by three QRS parameters: the duration of a QRS complex, the time distance from the former and latter QRS complexes, and the potential difference from former QRS complex J-location and the latter QRS complex fiducial location. The proposed methods were applied to DAY general hospital high resolution holter data.

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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 (October 1, 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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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 (July 2006): 468–75. http://dx.doi.org/10.1109/titb.2006.875662.

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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 (June 25, 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. When the QRS complexes were detected, the correlation coefficient and RR-interval were utilized to calculate the similarity of arrhythmias. The algorithm was tested using MIT-BIH arrhythmia database and every QRS complex was classified in the database. The total number of test data was 538, 9 and 24 for normal beats, APCs and PVCs, respectively. The results are presented in terms of, performance, positive predication and sensitivity. High overall performance (99.3%) for the classification of the different categories of arrhythmic beats was achieved. The positive prediction results of the system reach 99.44%, 100% and 95.35% for normal beats, APCs and PVCs, respectively. The sensitivity results of the system are 99.81%, 81.82% and 95.83% for normal beats, APCs and PVCs, respectively. Results revealed that the system is accurate and efficient to classify arrhythmias resulted from APC or PVC. The proposed arrhythmia detection algorithm is therefore helpful to the clinical diagnosis.

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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-based procedure. This procedure flattens the ECG and enhances the QRS-complexes. The algorithm obtained an overall sensitivity of 99.60% and positive predictive value of 99.69% on the MIT/BIH arrhythmia database. Additionally, R-DECO includes support for several input data formats for ECG signals, three basic filters, the possibility to load other R-peak locations and intuitive methods to correct ectopic, wrong, or missed heartbeats. All functionalities can be accessed via the GUI and the analysis results can be exported as Matlab or Excel files. The software is publicly available. Through its easy-to-use GUI, R-DECO allows both clinicians and researchers to use all functionalities, without previous knowledge.

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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 (August 31, 2018): 77–79. http://dx.doi.org/10.26438/ijcse/v6i8.7779.

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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 (April 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 heart rate signals are essentially non-stationary and contain indicators of current disease or warnings about impending diseases. The indicators may be present at all times or may occur at random in the time scale. Double density discrete wavelet transform is easier to implement, provides multiresolution and also reduces the computational time. In the pre-processing step, the baseline wandering is removed from the ECG signal. Then the R peaks/QRS complexes are detected. From the location of the R peaks, the successive RR intervals and heart rate are calculated. Fifty-two records from the MIT-BIH arrhythmia database are used to evaluate the proposed method. Sensitivity and positive prediction are used as performance measures. This method detects the R peaks with 100% sensitivity and 99.95% positive prediction. The performance of the proposed method is better than other methods existing in the literature.

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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 (February 2010): 48–54. http://dx.doi.org/10.1016/j.irbm.2009.10.001.

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Hanser, F., B. Pfeifer, M. Seger, C. Hintermüller, R. Modre, B. Tilg, T. Trieb, 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 leads in the ECG map. A direct method was applied for signal classification. The algorithm was tuned for distinguishing beats with an adenosine induced AV-nodal block from baseline morphology in Wolff-Parkinson-White (WPW) patients. Furthermore, an automatic identification of the QRS-interval borders was implemented. Results: The software was tested with data from eight patients having overt ventricular preexcitation. The R-peak detector captured all QRS-complexes with no false positive detection. The automatic classification was verified by demonstrating adenosine-induced prolongation of ventricular activation with statistical significance (p <0.001) in all patients. This also demonstrates the performance of the automatic detection of QRS-interval borders. Furthermore, all ectopic or paced beats were automatically separated from sinus rhythm. Computed activation maps are shown for one patient localizing the accessory pathway with an accuracy of 1 cm. Conclusions: The implemented signal processing pipeline is a powerful tool for selecting target beats for noninvasive activation imaging in WPW patients. It robustly identifies and classifies beats. The small beat to beat variations in the automatic QRS-interval detection indicate accurate identification of the time window of interest.

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Park, Young-chul. "Fixed-point Optimization of a QRS complex Detection Algorithm Using Wavelet Transform." Journal of Korea Institute of Information, Electronics, and Communication Technology 7, no. 3 (September 30, 2014): 126–31. http://dx.doi.org/10.17661/jkiiect.2014.7.3.126.

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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 (September 6, 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 VLP detection method based on the the marginal component analysis and investigates its performance taking into account different ratios between QRS and VLP power. After a denoising phase, performed adopting the singular vector decomposition technique, heartbeats characterized by VLP onsets are identified and extracted taking into account the vector magnitude of each high resolution ECG (HR-ECG) record. To evaluate the proposed method performance, a 15-lead HR-ECG database consisting of real VLP-negative and simulated VLP-positive patterns was used. The achieved results highlight the method validity for VLP detection.

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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 (December 19, 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 MAECG. A reference template is then reconstructed based on MQRS locations and removed from the preprocessed signal resulting in the raw FECG. This extracted FECG is further corrected and enhanced before obtaining the Fetal R peaks. The detection of FQRS and calculation of FHR has been compared against the reference Fetal Scalp ECG. Results indicate that the approach achieved good accuracy.

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Yuen, Brosnan, Xiaodai Dong, and Tao Lu. "Detecting Noisy ECG QRS Complexes Using WaveletCNN Autoencoder and ConvLSTM." IEEE Access 8 (2020): 143802–17. http://dx.doi.org/10.1109/access.2020.3012904.

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Mehta, S. S., and N. S. Lingayat. "Combined entropy based method for detection of QRS complexes in 12-lead electrocardiogram using SVM." Computers in Biology and Medicine 38, no. 1 (January 2008): 138–45. http://dx.doi.org/10.1016/j.compbiomed.2007.08.003.

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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 support online updating. Most normal ECG can be filtered out, and the abnormal ECG is further analyzed in the health cloud. Three experiments are conducted to evaluate the platform’s performance. The results demonstrate that the signal-to-noise ratio (SNR) of the smart clothes exceeds 37 dB, which is within the “very good signal” interval. The average of the QRS sensitivity and positive prediction is above 99.5%. Power-saving transmission is reduced by nearly 1980 times the power consumption in the best-case analysis.

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Yang, Wen-Hung, and Bernard C. Jiang. "An Integrated Statistical Process Control and Wavelet Transformation Model for Detecting QRS Complexes in ECG Signals." International Journal of Artificial Life Research 1, no. 2 (April 2010): 1–20. http://dx.doi.org/10.4018/jalr.2010040101.

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In this study, the authors propose an approach for detecting R-wave of electrocardiogram (ECG) signals. A statistical process control chart is successfully integrated with wavelet transformation (WT) to detect R-wave locations. This chart is a graphical display of the quality characteristic measured or computed from samples versus the sample number or time from the production line in a factory. This research performed WT at the signal preprocessing stage; the change points and control limits are then determined for each segment and the R-wave location is rechecked by spreading the points at the decision stage. The proposed procedures determine the change points and control limits for each segment. This method can be used to eliminate high-frequency noise, baseline shifts and artifacts from ECG signals, and R-waves can be effectively detected. In addition, there is flexibility in parameter value selection and robustness over wider noise ranges for the proposed QRS detection method.

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SHANTHA SELVA KUMARI, R., and V. SADASIVAM. "WAVELET-BASED BASE LINE WANDERING REMOVAL AND R PEAK AND QRS COMPLEX DETECTION." International Journal of Wavelets, Multiresolution and Information Processing 05, no. 06 (November 2007): 927–39. http://dx.doi.org/10.1142/s0219691307002129.

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Wavelet transform has emerged as a powerful tool for time-frequency analysis and signal coding favored for the interrogation of complex non-stationary signals such as the ECG signal. Measurement of timing intervals of ECG signal by automated system is highly superior to its subjective analysis. The timing interval is found from the onset and offset of the wave components of the ECG signal. Since the Daubechies wavelet is similar to the shape of the ECG signal, better detection is achieved. Discrete Wavelet Transform is easier to implement, provides multiresolution and also reduces the computational time, and thus, is used. In the pre-processing step, the base line wandering is removed from the ECG signal. Then the R peak and the QRS complexes are detected. Twenty five records from the MIT-BIH arrhythmia database are used to evaluate the proposed method. Sensitivity and positive prediction are used as performance measures. This method is very simple and detects all the R peaks (sensitivity = 100% and positive prediction = 99.86%). That is, false positive detection is very negligible and false negative detection is zero. The performance of the proposed method is better than other methods that exist in the literature.

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Alba, Alfonso, Martin O. Mendez, Miguel E. Rubio-Rincon, and Edgar R. Arce-Santana. "A consensus algorithm for approximate string matching and its application to QRS complex detection." International Journal of Modern Physics C 27, no. 03 (February 23, 2016): 1650029. http://dx.doi.org/10.1142/s0129183116500297.

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In this paper, a novel algorithm for approximate string matching (ASM) is proposed. The novelty resides in the fact that, unlike most other methods, the proposed algorithm is not based on the Hamming or Levenshtein distances, but instead computes a score for each symbol in the search text based on a consensus measure. Those symbols with sufficiently high scores will likely correspond to approximate instances of the pattern string. To demonstrate the usefulness of the proposed method, it has been applied to the detection of QRS complexes in electrocardiographic signals with competitive results when compared against the classic Pan-Tompkins (PT) algorithm. The proposed method outperformed PT in 72% of the test cases, with no extra computational cost.

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Xia, Yong, Junze Han, and Kuanquan Wang. "Quick detection of QRS complexes and R-waves using a wavelet transform and K-means clustering." Bio-Medical Materials and Engineering 26, s1 (August 17, 2015): S1059—S1065. http://dx.doi.org/10.3233/bme-151402.

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Shepoval’nikov, R. A., A. P. Nemirko, and A. N. Kalinichenko. "Algorithm for detection of the QRS complexes in the fetal ECG in the course of delivery." Pattern Recognition and Image Analysis 18, no. 1 (January 2008): 123–31. http://dx.doi.org/10.1134/s105466180801015x.

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Tirado-Martin, Paloma, Judith Liu-Jimenez, Jorge Sanchez-Casanova, and Raul Sanchez-Reillo. "QRS Differentiation to Improve ECG Biometrics under Different Physical Scenarios Using Multilayer Perceptron." Applied Sciences 10, no. 19 (October 1, 2020): 6896. http://dx.doi.org/10.3390/app10196896.

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Currently, machine learning techniques are successfully applied in biometrics and Electrocardiogram (ECG) biometrics specifically. However, not many works deal with different physiological states in the user, which can provide significant heart rate variations, being these a key matter when working with ECG biometrics. Techniques in machine learning simplify the feature extraction process, where sometimes it can be reduced to a fixed segmentation. The applied database includes visits taken in two different days and three different conditions (sitting down, standing up after exercise), which is not common in current public databases. These characteristics allow studying differences among users under different scenarios, which may affect the pattern in the acquired data. Multilayer Perceptron (MLP) is used as a classifier to form a baseline, as it has a simple structure that has provided good results in the state-of-the-art. This work studies its behavior in ECG verification by using QRS complexes, finding its best hyperparameter configuration through tuning. The final performance is calculated considering different visits for enrolling and verification. Differentiation in the QRS complexes is also tested, as it is already required for detection, proving that applying a simple first differentiation gives a good result in comparison to state-of-the-art similar works. Moreover, it also improves the computational cost by avoiding complex transformations and using only one type of signal. When applying different numbers of complexes, the best results are obtained when 100 and 187 complexes in enrolment, obtaining Equal Error Rates (EER) that range between 2.79–4.95% and 2.69–4.71%, respectively.

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Lynn, Htet Myet, Pankoo Kim, and Sung Bum Pan. "Data Independent Acquisition Based Bi-Directional Deep Networks for Biometric ECG Authentication." Applied Sciences 11, no. 3 (January 26, 2021): 1125. http://dx.doi.org/10.3390/app11031125.

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In this report, the study of non-fiducial based approaches for Electrocardiogram(ECG) biometric authentication is examined, and several excessive techniques are proposed to perform comparative experiments for evaluating the best possible approach for all the classification tasks. Non-fiducial methods are designed to extract the discriminative information of a signal without annotating fiducial points. However, this process requires peak detection to identify a heartbeat signal. Based on recent studies that usually rely on heartbeat segmentation, QRS detection is required, and the process can be complicated for ECG signals for which the QRS complex is absent. Thus, many studies only conduct biometric authentication tasks on ECG signals with QRS complexes, and are hindered by similar limitations. To overcome this issue, we proposed a data-independent acquisition method to facilitate highly generalizable signal processing and feature learning processes. This is achieved by enhancing random segmentation to avoid complicated fiducial feature extraction, along with auto-correlation to eliminate the phase difference due to random segmentation. Subsequently, a bidirectional recurrent neural network (RNN) with long short-term memory (LSTM) deep networks is utilized to automatically learn the features associated with the signal and to perform an authentication task. The experimental results suggest that the proposed data-independent approach using a BLSTM network achieves a relatively high classification accuracy for every dataset relative to the compared techniques. Moreover, it exhibited a significantly higher accuracy rate in experiments using ECG signals without the QRS complex. The results also revealed that data-dependent methods can only perform well for specified data types and amendments of data variations, whereas the presented approach can also be considered for generalization to other quasi-periodical biometric signal-based classification tasks in future studies.

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Mohd Apandi, Ziti Fariha, Ryojun Ikeura, Soichiro Hayakawa, and Shigeyoshi Tsutsumi. "An Analysis of the Effects of Noisy Electrocardiogram Signal on Heartbeat Detection Performance." Bioengineering 7, no. 2 (June 6, 2020): 53. http://dx.doi.org/10.3390/bioengineering7020053.

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Heartbeat detection for ambulatory cardiac monitoring is more challenging as the level of noise and artefacts induced by daily-life activities are considerably higher than monitoring in a hospital setting. It is valuable to understand the relationship between the characteristics of electrocardiogram (ECG) noises and the beat detection performance in the cardiac monitoring system. For this purpose, three well-known algorithms for the beat detection process were re-implemented. The beat detection algorithms were validated using two types of ambulatory datasets, which were the ECG signal from the MIT-BIH Arrhythmia Database and the simulated noise-contaminated ECG signal with different intensities of baseline wander (BW), muscle artefact (MA) and electrode motion (EM) artefact from the MIT-BIH Noise Stress Test Database. The findings showed that signals contaminated with noise and artefacts decreased the potential of beat detection in ambulatory signal with the poorest performance noted for ECG signal affected by the EM artefacts. In conclusion, none of the algorithms was able to detect all QRS complexes without any false detection at the highest level of noise. The EM noise influenced the beat detection performance the most in comparison to the MA and BW noises that resulted in the highest number of misdetections and false detections.

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Dembrani, Mahesh B., K. B. Khanchandani, and Anita Zurani. "Accurate Detection of ECG Signals in ECG Monitoring Systems by Eliminating the Motion Artifacts and Improving the Signal Quality Using SSG Filter with DBE." Journal of Circuits, Systems and Computers 29, no. 02 (May 16, 2019): 2050024. http://dx.doi.org/10.1142/s0218126620500243.

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The automatic recognition of QRS complexes in an Electrocardiography (ECG) signal is a critical step in any programmed ECG signal investigation, particularly when the ECG signal taken from the pregnant women additionally contains the signal of the fetus and some motion artifact signals. Separation of ECG signals of mother and fetus and investigation of the cardiac disorders of the mother are demanding tasks, since only one single device is utilized and it gets a blend of different heart beats. In order to resolve such problems we propose a design of new reconfigurable Subtractive Savitzky–Golay (SSG) filter with Digital Processor Back-end (DBE) in this paper. The separation of signals is done using Independent Component Analysis (ICA) algorithm and then the motion artifacts are removed from the extracted mother’s signal. The combinational use of SSG filter and DBE enhances the signal quality and helps in detecting the QRS complex from the ECG signal particularly the R peak accurately. The experimental results of ECG signal analysis show the importance of our proposed method.

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Mondelo, Víctor, María José Lado, Arturo José Méndez, Xosé Antón Vila, and Leandro Rodríguez-Liñares. "Combining 12-Lead ECG Information for a Beat Detection Algorithm." Journal on Advances in Theoretical and Applied Informatics 3, no. 1 (August 30, 2017): 5. http://dx.doi.org/10.26729/jadi.v3i1.2436.

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This paper proposes a beat detection algorithm specially tailored to be used with 12 channel ECG records. The algorithm first obtains beat positions on each channel, and then combines this information to get an improved estimate. The detection process involves two stages: 1) single-channel detection: implemented by improving one of the most popular methods (Pan-Tompkins) developed to detect beat positions; and 2) multichannel detection: an algorithm that combines the information of the beat positions obtained in each of the 12 channels. In this way, our results clearly improve those obtained with the single-channel detection method, discarding detection errors, false positives, and duplicated beats. Besides, our single-channel method significantly reduces the temporal error when estimating the positions of QRS complexes. In the multichannel detection, the assessment of our algorithm against one-channel based approaches shows a significant improvement in detection outcome (Se = 99.86%, P+ = 99.98%, RMS RR Interval Error = 25.98 ms, F-Score = 0.9992), making it a good starting point for automatic diagnosis of heart conditions.

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Zhang, Zhou, Zeyu Li, and Zhangyong Li. "An Improved Real-Time R-Wave Detection Efficient Algorithm in Exercise ECG Signal Analysis." Journal of Healthcare Engineering 2020 (July 29, 2020): 1–7. http://dx.doi.org/10.1155/2020/8868685.

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R-wave detection is a prerequisite for the extraction and recognition of ECG signal feature parameters. In the analysis and diagnosis of exercise electrocardiograms, accurate and real-time detection of QRS complexes is very important for the prevention and monitoring of heart disease. This paper proposes a lightweight R-wave real-time detection method for exercise ECG signals. After real-time denoising of the exercise ECG signal, the median line is used to correct the baseline, and the first-order difference processing is performed on the differential square signal. Max-Min Threshold (MMT) is used to realize real-time R-wave detection of the exercise ECG signal. The abovementioned method was verified by using the measured data in the MIT-BIH ECG database of the Massachusetts Institute of Technology and the exercise plate experiment. The R-wave detection rates were 99.93% and 99.98%, respectively. Experimental results show that this method has high accuracy and low computational complexity and is suitable for wearable devices and motion process monitoring.

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Mourad, Kholkhal, and Bereksi Reguig Fethi. "Efficient automatic detection of QRS complexes in ECG signal based on reverse biorthogonal wavelet decomposition and nonlinear filtering." Measurement 94 (December 2016): 663–70. http://dx.doi.org/10.1016/j.measurement.2016.09.014.

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PATIL, G. M., K. SUBBA RAO, U. C. NIRANJAN, and K. SATYANARAYAN. "EVALUATION OF QRS COMPLEX BASED ON DWT COEFFICIENTS ANALYSIS USING DAUBECHIES WAVELETS FOR DETECTION OF MYOCARDIAL ISCHAEMIA." Journal of Mechanics in Medicine and Biology 10, no. 02 (June 2010): 273–90. http://dx.doi.org/10.1142/s0219519410003356.

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This paper presents a new approach in the field of electrocardiogram (ECG) feature extraction system based on the discrete wavelet transform (DWT) coefficients using Daubechies Wavelets. Real ECG signals recorded in lead II configuration are chosen for processing. The ECG signal was acquired by a battery operated, portable ECG data acquisition and signal processing module. In the second step the ECG signal was denoised using soft thresholding with Symlet4 wavelet. Further denoising was achieved by removing the corresponding wavelet coefficients at higher levels of decomposition. Later the ECG data files were converted to .txt files and subsequently to. mat files before being imported into the Matlab 7.4.0 environment for the computation of the decomposition coefficients. The QRS complexes were grouped as normal or myocardial ischaemic ones based on these decomposition coefficients. The algorithm developed by us was evaluated with control database comprising 120 records and validated using 60 records making up test database. By using the DWT coefficients, we have successfully achieved the myocardial ischaemia detection rates up to 97.5% with the technique developed by us for control data and up to 100% for validation test data.

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Reljin, Natasa, Jesus Lazaro, Md Billal Hossain, Yeon Sik Noh, Chae Ho Cho, and Ki H. Chon. "Using the Redundant Convolutional Encoder–Decoder to Denoise QRS Complexes in ECG Signals Recorded with an Armband Wearable Device." Sensors 20, no. 16 (August 17, 2020): 4611. http://dx.doi.org/10.3390/s20164611.

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Long-term electrocardiogram (ECG) recordings while performing normal daily routines are often corrupted with motion artifacts, which in turn, can result in the incorrect calculation of heart rates. Heart rates are important clinical information, as they can be used for analysis of heart-rate variability and detection of cardiac arrhythmias. In this study, we present an algorithm for denoising ECG signals acquired with a wearable armband device. The armband was worn on the upper left arm by one male participant, and we simultaneously recorded three ECG channels for 24 h. We extracted 10-s sequences from armband recordings corrupted with added noise and motion artifacts. Denoising was performed using the redundant convolutional encoder–decoder (R-CED), a fully convolutional network. We measured the performance by detecting R-peaks in clean, noisy, and denoised sequences and by calculating signal quality indices: signal-to-noise ratio (SNR), ratio of power, and cross-correlation with respect to the clean sequences. The percent of correctly detected R-peaks in denoised sequences was higher than in sequences corrupted with either added noise (70–100% vs. 34–97%) or motion artifacts (91.86% vs. 61.16%). There was notable improvement in SNR values after denoising for signals with noise added (7–19 dB), and when sequences were corrupted with motion artifacts (0.39 dB). The ratio of power for noisy sequences was significantly lower when compared to both clean and denoised sequences. Similarly, cross-correlation between noisy and clean sequences was significantly lower than between denoised and clean sequences. Moreover, we tested our denoising algorithm on 60-s sequences extracted from recordings from the Massachusetts Institute of Technology-Beth Israel Hospital (MIT-BIH) arrhythmia database and obtained improvement in SNR values of 7.08 ± 0.25 dB (mean ± standard deviation (sd)). These results from a diverse set of data suggest that the proposed denoising algorithm improves the quality of the signal and can potentially be applied to most ECG measurement devices.

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Roudijk, Rob W., Laurens P. Bosman, Jeroen F. van der Heijden, Jacques M. T. de Bakker, Richard N. W. Hauer, J. Peter van Tintelen, Folkert W. Asselbergs, Anneline S. J. M. te Riele, and Peter Loh. "Quantitative Approach to Fragmented QRS in Arrhythmogenic Cardiomyopathy: From Disease towards Asymptomatic Carriers of Pathogenic Variants." Journal of Clinical Medicine 9, no. 2 (February 17, 2020): 545. http://dx.doi.org/10.3390/jcm9020545.

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Fragmented QRS complexes (fQRS) are common in patients with arrhythmogenic cardiomyopathy (ACM). A new method of fQRS quantification may aid early disease detection in pathogenic variant carriers and assessment of prognosis in patients with early stage ACM. Patients with definite ACM (n = 221, 66%), carriers of a pathogenic ACM-associated variant without a definite ACM diagnosis (n = 57, 17%) and control subjects (n = 58, 17%) were included. Quantitative fQRS (Q-fQRS) was defined as the total amount of deflections in the QRS complex in all 12 electrocardiography (ECG) leads. Q-fQRS was scored by a single observer and reproducibility was determined by three independent observers. Q-fQRS count was feasible with acceptable intra- and inter-observer agreement. Q-fQRS count is significantly higher in patients with definite ACM (54 ± 15) and pathogenic variant carriers (55 ± 10) compared to controls (35 ± 5) (p < 0.001). In patients with ACM, Q-fQRS was not associated with sustained ventricular arrhythmia (p = 0.701) at baseline or during follow-up (p = 0.335). Both definite ACM patients and pathogenic variant carriers not fulfilling ACM diagnosis have a higher Q-fQRS than controls. This may indicate that increased Q-fQRS is an early sign of disease penetrance. In concealed and early stages of ACM the role of Q-fQRS for risk stratification is limited.

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Lasanen, K., and J. Ko. "A 1-V analog CMOS front-end for detecting QRS complexes in a cardiac signal." IEEE Transactions on Circuits and Systems I: Regular Papers 52, no. 12 (December 2005): 2584–94. http://dx.doi.org/10.1109/tcsi.2005.857872.

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Vo, Khuong, Tai Le, Amir M. Rahmani, Nikil Dutt, and Hung Cao. "An Efficient and Robust Deep Learning Method with 1-D Octave Convolution to Extract Fetal Electrocardiogram." Sensors 20, no. 13 (July 4, 2020): 3757. http://dx.doi.org/10.3390/s20133757.

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The invasive method of fetal electrocardiogram (fECG) monitoring is widely used with electrodes directly attached to the fetal scalp. There are potential risks such as infection and, thus, it is usually carried out during labor in rare cases. Recent advances in electronics and technologies have enabled fECG monitoring from the early stages of pregnancy through fECG extraction from the combined fetal/maternal ECG (f/mECG) signal recorded non-invasively in the abdominal area of the mother. However, cumbersome algorithms that require the reference maternal ECG as well as heavy feature crafting makes out-of-clinics fECG monitoring in daily life not yet feasible. To address these challenges, we proposed a pure end-to-end deep learning model to detect fetal QRS complexes (i.e., the main spikes observed on a fetal ECG waveform). Additionally, the model has the residual network (ResNet) architecture that adopts the novel 1-D octave convolution (OctConv) for learning multiple temporal frequency features, which in turn reduce memory and computational cost. Importantly, the model is capable of highlighting the contribution of regions that are more prominent for the detection. To evaluate our approach, data from the PhysioNet 2013 Challenge with labeled QRS complex annotations were used in the original form, and the data were then modified with Gaussian and motion noise, mimicking real-world scenarios. The model can achieve a F1 score of 91.1% while being able to save more than 50% computing cost with less than 2% performance degradation, demonstrating the effectiveness of our method.

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CHAWLA, MANENDRAPAL SINGH. "A COMBINED PCA–ICA STATISTICAL APPROACH AND QUADRATIC SPLINE WAVELETS FOR DETECTION OF R-PEAKS AND HEART RATE ESTIMATIONS IN ELECTROCARDIOGRAMS." Journal of Mechanics in Medicine and Biology 11, no. 03 (June 2011): 625–42. http://dx.doi.org/10.1142/s0219519411003855.

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The need for the possible improvements in the proposed algorithm is felt toward more effective filtering in the principal component analysis (PCA) preprocessing stage itself, as well for better variance threshold adjustment. Using composite wavelet transform (WT)-based PCA–ICA methods helps for redundant data reduction as well for better feature extraction. This article discusses some of the conditions of ICA that could affect the reliability of the separation and evaluation of issues related to the properties of the signals and number of sources. In this analysis, a new statistical algorithm is proposed, based on the use of combined PCA–ICA for the three correlated channels of 12-channel electrocardiographic (ECG) data. This study also deals with the detection of QRS complexes in electrocardiograms using combined PCA–ICA algorithm. The efficacy of the combined PCA–ICA algorithm lies in the fact that the location of the R-peaks is accurately determined, and none of the peaks are ignored or missed, as quadratic spline wavelet is also used. With (WT)-based methods, PCA and ICA are used not only for preprocessing, but may also be used for postprocessing based on the requirements, whether ICA is used first then PCA or vice versa.

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Marchon, Niyan, and Gourish Naik. "Linear phase FIR filter to compute fetal heart rate variability." International Journal of Engineering & Technology 7, no. 4.5 (September 22, 2018): 492. http://dx.doi.org/10.14419/ijet.v7i4.5.21141.

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Continuous monitoring of fetal heart rate (FHR) can detect the well-being of the fetus and thus indicates non-reassuring fetal status. In- vasive fetal electrocardiography (FECG) using the fetal scalp electrode applied to the fetus scalp allows accurate detection of fetal QRS (FQRS) complexes, however with a risk of infection to the fetus. We have proposed a non-invasive fetal heart rate (NIFHR) filtering technique employing finite impulse response (FIR) filters. We applied Fast Fourier Transform (FFT) to the Physionet abdominal ECG (aECG) records and derived the fiduciary edges of the spectrum of the FECG. A FIR band pass filter (BPF) is designed which is a com- posite filter consisting of a high pass filter (HPF) followed by a low pass filter (LPF) in that order. The cut off frequencies of these com- posite filters are the fiduciary edges of the fetal electrocardiography spectrum. A FQRS detector to obtain fetal heart rate variability (FHRV) processes the FQRS signal filtered through these composite FIR filters. It is observed that channel 4 from records r01 and r08 obtained 100% results for sensitivity, positive predictive value and accuracy while, the overall accuracy was 92.21%. This design can also be extended to compute maternal heart rate.

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Journal articles on the topic 'Detection of QRS complexes'

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