Relevant bibliographies by topics / R-peak detection

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  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers

Academic literature on the topic 'R-peak detection'

Author: Grafiati
Published: 4 June 2021
Last updated: 1 February 2022

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Journal articles on the topic "R-peak detection"

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Yadav, Amana, and Naresh Grover. "R Peak Detection using Wavelet." International Journal of Computer Applications 169, no. 3 (2017): 40–43. http://dx.doi.org/10.5120/ijca2017914635.

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Trivedi, Pankhuri, and Shahanaz Ayub. "Detection of R Peak in Electrocardiogram." International Journal of Computer Applications 97, no. 20 (2014): 10–13. http://dx.doi.org/10.5120/17122-7711.

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Wu, Lu, Xiaoyun Xie, and Yinglong Wang. "ECG Enhancement and R-Peak Detection Based on Window Variability." Healthcare 9, no. 2 (2021): 227. http://dx.doi.org/10.3390/healthcare9020227.

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In ECG applications, the correct recognition of R-peaks is extremely important for detecting abnormalities, such as arrhythmia and ventricular hypertrophy. In this work, a novel ECG enhancement and R-peak detection method based on window variability is presented, and abbreviated as SQRS. Firstly, the ECG signal corrupted by various high or low-frequency noises is denoised by moving-average filtering. Secondly, the window variance transform technique is used to enhance the QRS complex and suppress the other components in the ECG, such as P/T waves and noise. Finally, the signal, converted by wi
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Yadav, Amana, and Naresh Grover. "A Robust Approach for R-Peak Detection." International Journal of Information Engineering and Electronic Business 9, no. 6 (2017): 43–50. http://dx.doi.org/10.5815/ijieeb.2017.06.06.

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Mayapur, Miss Priyanka. "Detection and Processing of the R Peak." IJIREEICE 6, no. 11 (2018): 36–44. http://dx.doi.org/10.17148/ijireeice.2018.6116.

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Jose, Neenu, and Nandakumar Paramparambath. "ECG Signal Compression and R Peak Detection." International Journal of Engineering Trends and Technology 38, no. 6 (2016): 316–19. http://dx.doi.org/10.14445/22315381/ijett-v38p258.

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Cha, Won-Jun, Gang-Soo Ryu, Jong-Hak Lee, Woong-Ho Cho, YouSoo Jung, and Kil-Houm Park. "R-Peak Detection Algorithm in ECG Signal Based on Multi-Scaled Primitive Signal." Journal of Korea Multimedia Society 19, no. 5 (2016): 818–25. http://dx.doi.org/10.9717/kmms.2016.19.5.818.

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Park, Jeong-Seon, Sang-Woong Lee, and Unsang Park. "R Peak Detection Method Using Wavelet Transform and Modified Shannon Energy Envelope." Journal of Healthcare Engineering 2017 (2017): 1–14. http://dx.doi.org/10.1155/2017/4901017.

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Rapid automatic detection of the fiducial points—namely, the P wave, QRS complex, and T wave—is necessary for early detection of cardiovascular diseases (CVDs). In this paper, we present an R peak detection method using the wavelet transform (WT) and a modified Shannon energy envelope (SEE) for rapid ECG analysis. The proposed WTSEE algorithm performs a wavelet transform to reduce the size and noise of ECG signals and creates SEE after first-order differentiation and amplitude normalization. Subsequently, the peak energy envelope (PEE) is extracted from the SEE. Then, R peaks are estimated fro
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Zhu, Honghai, and Jun Dong. "An R-peak detection method based on peaks of Shannon energy envelope." Biomedical Signal Processing and Control 8, no. 5 (2013): 466–74. http://dx.doi.org/10.1016/j.bspc.2013.01.001.

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Kew, Hsein-Ping, and Do-Un Jeong. "Variable Threshold Method for ECG R-peak Detection." Journal of Medical Systems 35, no. 5 (2011): 1085–94. http://dx.doi.org/10.1007/s10916-011-9745-7.

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Dissertations / Theses on the topic "R-peak detection"

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Dizon, Lucas, and Martin Johansson. "Atrial Fibrillation Detection Algorithm Evaluation and Implementation in Java." Thesis, KTH, Skolan för teknik och hälsa (STH), 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-158878.

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Atrial fibrillation is a common heart arrhythmia which is characterized by a missing or irregular contraction of the atria. The disease is a risk factor for other more serious diseases and the total medical costs in society are extensive. Therefore it would be beneficial to improve and optimize the prevention and detection of the disease.   Pulse palpation and heart auscultation can facilitate the detection of atrial fibrillation clinically, but the diagnosis is generally confirmed by an ECG examination. Today there are several algorithms that detect atrial fibrillation by analysing an ECG. A
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Denecker, Thomas. "Bioinformatique et analyse de données multiomiques : principes et applications chez les levures pathogènes Candida glabrata et Candida albicans Functional networks of co-expressed genes to explore iron homeostasis processes in the pathogenic yeast Candida glabrata Efficient, quick and easy-to-use DNA replication timing analysis with START-R suite FAIR_Bioinfo: a turnkey training course and protocol for reproducible computational biology Label-free quantitative proteomics in Candida yeast species: technical and biological replicates to assess data reproducibility Rendre ses projets R plus accessibles grâce à Shiny Pixel: a content management platform for quantitative omics data Empowering the detection of ChIP-seq "basic peaks" (bPeaks) in small eukaryotic genomes with a web user-interactive interface A hypothesis-driven approach identifies CDK4 and CDK6 inhibitors as candidate drugs for treatments of adrenocortical carcinomas Characterization of the replication timing program of 6 human model cell lines." Thesis, université Paris-Saclay, 2020. http://www.theses.fr/2020UPASL010.

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Plusieurs évolutions sont constatées dans la recherche en biologie. Tout d’abord, les études menées reposent souvent sur des approches expérimentales quantitatives. L’analyse et l’interprétation des résultats requièrent l’utilisation de l’informatique et des statistiques. Également, en complément des études centrées sur des objets biologiques isolés, les technologies expérimentales haut débit permettent l’étude des systèmes (caractérisation des composants du système ainsi que des interactions entre ces composants). De très grandes quantités de données sont disponibles dans les bases de données
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Wang, Yi-Neng, and 王奕能. "A Study of ECG R-peak Detection Methods." Thesis, 2016. http://ndltd.ncl.edu.tw/handle/83109914607668776375.

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碩士<br>國立臺灣科技大學<br>電子工程系<br>105<br>Among all heart rate detection algorithms, the one developed by Pan-Tompkins is the most commonly used. However, its filtering system can not reserve enough content of QRS complex, its searchback mechanism can not detect bigeminy and trigeminy, and its non-linear transform can not adapt sudden changes in amplitude very well. There are only two open source R-peak detection algorithms that can be obtained from the Internet, i.e. WFDB （waveform database）of PhysioNet and OSEA（open source ECG analysis）of Hamilton at EP Limited corporation. OSEA was cited by more pa
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Book chapters on the topic "R-peak detection"

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El Bouny, Lahcen, Mohammed Khalil, and Abdellah Adib. "R Peak Detection Based on Wavelet Transform and Nonlinear Energy Operator." In Smart Data and Computational Intelligence. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-11914-0_11.

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Kozia, Christina, Randa Herzallah, and David Lowe. "ICA-Derived Respiration Using an Adaptive R-Peak Detector." In Contributions to Statistics. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-26036-1_25.

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Velychko, Olga, Oleh Datsok, and Iryna Perova. "Essential R Peak Detector Based on the Polynomial Fitting." In Lecture Notes in Computational Intelligence and Decision Making. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-82014-5_10.

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Navin, Om, Gautam Kumar, Nirmal Kumar, Kuldeep Baderia, Ranjeet Kumar, and Anil Kumar. "R-Peaks Detection Using Shannon Energy for HRV Analysis." In Lecture Notes in Electrical Engineering. Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-2553-3_39.

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Madhu Keerthana, Y., and M. Kiran Reddy. "Detecting R-Peaks in Electrocardiogram Signal Using Hilbert Envelope." In Advances in Intelligent Systems and Computing. Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-0923-6_1.

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Gautam, Alka, Hoon-Jae Lee, and Wan-Young Chung. "ECG Signal De-Noising with Asynchronous Averaging and Filtering Algorithm." In Advancing Technologies and Intelligence in Healthcare and Clinical Environments Breakthroughs. IGI Global, 2012. http://dx.doi.org/10.4018/978-1-4666-1755-1.ch014.

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In this study, a new algorithm is proposed—Asynchronous Averaging and Filtering (AAF) for ECG signal de-noising. R-peaks are detected with another proposed algorithm—Minimum Slot and Maximum Point selecting method (MSMP). AAF algorithm reduces random noise (major component of EMG noise) from ECG signal and provides comparatively good results for baseline wander noise cancellation. Signal to noise ratio (SNR) improves in filtered ECG signal, while signal shape remains undistorted. The authors conclude that R-peak detection with MSMP method gives comparable results from existing algorithm like Pan-Tomkins algorithm. AAF algorithm is advantageous over adaptation algorithms like Wiener and LMS algorithm. Overall performance of proposed algorithms is comparatively good.
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Conference papers on the topic "R-peak detection"

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Sufi, F., Q. Fang, and I. Cosic. "ECG R-R Peak Detection on Mobile Phones." In 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE, 2007. http://dx.doi.org/10.1109/iembs.2007.4353134.

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Arteaga-Falconi, Juan, Hussein Al Osman, and Abdulmotaleb El Saddik. "R-peak detection algorithm based on differentiation." In 2015 IEEE 9th International Symposium on Intelligent Signal Processing (WISP). IEEE, 2015. http://dx.doi.org/10.1109/wisp.2015.7139157.

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Yangdong Liao, Ru-Xin Na, and Derek Rayside. "Accurate ECG R-peak detection for telemedicine." In 2014 IEEE Canada International Humanitarian Technology Conference (IHTC). IEEE, 2014. http://dx.doi.org/10.1109/ihtc.2014.7147524.

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Laitala, Juho, Mingzhe Jiang, Elise Syrjälä, et al. "Robust ECG R-peak detection using LSTM." In SAC '20: The 35th ACM/SIGAPP Symposium on Applied Computing. ACM, 2020. http://dx.doi.org/10.1145/3341105.3373945.

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Peng, Shichen, Jiajing Fan, and Zhen Zhu. "R-peak Detection for ECG Biomedical Monitoring." In 2021 IEEE International Conference on Power, Intelligent Computing and Systems (ICPICS). IEEE, 2021. http://dx.doi.org/10.1109/icpics52425.2021.9524155.

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Mohanty, Mohan Debarchan, Bibhuprasad Mohanty, and Mihir N. Mohanty. "R-peak detection using efficient technique for tachycardia detection." In 2017 2nd International Conference on Man and Machine Interfacing (MAMI). IEEE, 2017. http://dx.doi.org/10.1109/mami.2017.8307877.

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Palaniappan, Y., V. A. Vishanth, N. Santhosh, R. Karthika, and M. Ganesan. "R - Peak Detection using Altered Pan-Tompkins Algorithm." In 2020 International Conference on Communication and Signal Processing (ICCSP). IEEE, 2020. http://dx.doi.org/10.1109/iccsp48568.2020.9182298.

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Parihar, Nilesh. "R — Peak detection using Bayesian regularization neural network." In 2017 International Conference on Information, Communication, Instrumentation and Control (ICICIC). IEEE, 2017. http://dx.doi.org/10.1109/icomicon.2017.8279025.

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Aurobinda, Anushree, Bibhu Prasad Mohanty, and Mihir Narayan Mohanty. "R-peak detection of ECG using adaptive thresholding." In 2016 International Conference on Communication and Signal Processing (ICCSP). IEEE, 2016. http://dx.doi.org/10.1109/iccsp.2016.7754140.

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Madan, Yamini, Shamika Ganesan, B. Ganga Gowri, V. Sowmya, and KP Soman. "R Peak Detection in ECG signals using Chebfun." In 2020 IEEE International Conference on Cloud Computing in Emerging Markets (CCEM). IEEE, 2020. http://dx.doi.org/10.1109/ccem50674.2020.00023.

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