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Journal articles on the topic 'R-peak detection'

Author: Grafiati

Published: 4 June 2021

Last updated: 1 February 2022

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1

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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2

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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3

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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4

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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5

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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6

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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7

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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8

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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9

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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10

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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11

Kaur, Amandeep, Alpana Agarwal, Ravinder Agarwal, and Sanjay Kumar. "A Novel Approach to ECG R-Peak Detection." Arabian Journal for Science and Engineering 44, no. 8 (2018): 6679–91. http://dx.doi.org/10.1007/s13369-018-3557-8.

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12

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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13

Qin, Qin, Jianqing Li, Yinggao Yue, and Chengyu Liu. "An Adaptive and Time-Efficient ECG R-Peak Detection Algorithm." Journal of Healthcare Engineering 2017 (2017): 1–14. http://dx.doi.org/10.1155/2017/5980541.

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R-peak detection is crucial in electrocardiogram (ECG) signal analysis. This study proposed an adaptive and time-efficient R-peak detection algorithm for ECG processing. First, wavelet multiresolution analysis was applied to enhance the ECG signal representation. Then, ECG was mirrored to convert large negative R-peaks to positive ones. After that, local maximums were calculated by the first-order forward differential approach and were truncated by the amplitude and time interval thresholds to locate the R-peaks. The algorithm performances, including detection accuracy and time consumption, we

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14

Lee, Miran, Dajeong Park, Suh-Yeon Dong, and Inchan Youn. "A Novel R Peak Detection Method for Mobile Environments." IEEE Access 6 (2018): 51227–37. http://dx.doi.org/10.1109/access.2018.2867329.

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15

Rooijakkers, Michael J., Chiara Rabotti, S. Guid Oei, and Massimo Mischi. "Low-complexity R-peak detection for ambulatory fetal monitoring." Physiological Measurement 33, no. 7 (2012): 1135–50. http://dx.doi.org/10.1088/0967-3334/33/7/1135.

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16

Gupta, Varun, Monika Mittal, and Vikas Mittal. "R-peak detection based chaos analysis of ECG signal." Analog Integrated Circuits and Signal Processing 102, no. 3 (2019): 479–90. http://dx.doi.org/10.1007/s10470-019-01556-1.

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17

Gupta, Varun, and Monika Mittal. "R-peak detection for improved analysis in health informatics." International Journal of Medical Engineering and Informatics 13, no. 3 (2021): 213. http://dx.doi.org/10.1504/ijmei.2021.10035358.

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18

Nguyen, Tam, Xiaoli Qin, Anh Dinh, and Francis Bui. "Low Resource Complexity R-peak Detection Based on Triangle Template Matching and Moving Average Filter." Sensors 19, no. 18 (2019): 3997. http://dx.doi.org/10.3390/s19183997.

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A novel R-peak detection algorithm suitable for wearable electrocardiogram (ECG) devices is proposed with four objectives: robustness to noise, low latency processing, low resource complexity, and automatic tuning of parameters. The approach is a two-pronged algorithm comprising (1) triangle template matching to accentuate the slope information of the R-peaks and (2) a single moving average filter to define a dynamic threshold for peak detection. The proposed algorithm was validated on eight ECG public databases. The obtained results not only presented good accuracy, but also low resource comp

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19

N, Santipriya, Venkateswara Rao M, Arun V, and R. Karthik. "Real Time Detection of R – Peak in QRS Complex of ECG using Microcontroller." Indonesian Journal of Electrical Engineering and Computer Science 11, no. 1 (2018): 372. http://dx.doi.org/10.11591/ijeecs.v11.i1.pp372-376.

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<p>Real-time detection of R peaks in QRS complex of ECG signal is the first step in the processing of ECG waveform. Based on this, various other ECG parameters can be extracted. These parameters provide substantial information about various heart diseases. In this paper, we are proposing a method to detect R – peaks of ECG signal dynamically. The most prominent role in the R – peak detector is executed by the microcontroller. This method originates by acquiring signal from the subject and necessary pre-processing is carried out on the signal in order to achieve the denoised signal. Subse

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20

Sunkaria, R. K., S. C. Saxena, V. Kumar, and A. M. Singhal. "Wavelet based R-peak detection for heart rate variability studies." Journal of Medical Engineering & Technology 34, no. 2 (2010): 108–15. http://dx.doi.org/10.3109/03091900903281215.

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21

Lin, Chen, Hui-Ming Yeh, Men-Tzung Lo, et al. "Robust Fetal Heart Beat Detection via R-Peak Intervals Distribution." IEEE Transactions on Biomedical Engineering 66, no. 12 (2019): 3310–19. http://dx.doi.org/10.1109/tbme.2019.2904014.

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22

Celin, S., and K. Vasanth. "Detection and Classification of R-Peak Using Naïve Bayes Classifier." International Journal of Engineering & Technology 7, no. 3.27 (2018): 397. http://dx.doi.org/10.14419/ijet.v7i3.27.17982.

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Electrocardiogram (ECG) in classification of signals plays a major role in the diagnoses of heart diseases. The main challenging problem is the classification of accurate ECG. Here in this paper the ECG is classified into arrhythmia types. It is very important that detecting the heart disease and finding the treatment for the patient at the earliest must be done accurately. In the ECG classification different classifiers are available. The best accuracy value of 99.7% is produced by using the Bayes classifiers in this paper. ECG databases, classifiers, feature extraction techniques and perform

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23

Swatland, H. J. "Optical properties of turkey pectoralis muscle affecting detection of connective tissue fluorescence." Canadian Journal of Animal Science 81, no. 1 (2001): 25–31. http://dx.doi.org/10.4141/a00-053.

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A dual-channel probe was used to detect fluorescence (excitation 365 nm, emission 400 to 550 nm) and reflectance (550 nm) in turkey pectoralis muscles (n= 157). All fluorescence peaks had matching reflectance peaks, but not vice versa. Thus, some reflectance peaks originated from non-fluorescent structures in the muscle. Electrical impedance was used to assess fluid distribution. Electrical capacitance was correlated (P< 0.01) with areas under probe signals (r= 0.19 for both fluorescence and reflectance). In a subset of samples (n= 45), the reflectance of initially polarized light was used

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24

Thiamchoo, Nantarika, and Pornchai Phukpattaranont. "R Peak Detection Algorithm based on Continuous Wavelet Transform and Shannon Energy." ECTI Transactions on Computer and Information Technology (ECTI-CIT) 10, no. 2 (2017): 167–75. http://dx.doi.org/10.37936/ecti-cit.2016102.64837.

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The R peak detection algorithm is a necessary tool for monitoring and diagnosing the cardiovascular disease. This paper presents the R peak detection algorithm based on continuous wavelet transform (CWT) and Shannon energy. We evaluate the proposed algorithm with the 48 record of ECG data from MIT-BIH arrhythmia database. Results show that the proposed algorithm gives very good DER (0.48%-0.50%) compared to those from previous publications (0.168%-0.87%). We demonstrated that the use of the CWT with a single scaling parameter is capable of removing noises. In addition, we found that Shannon en

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25

BADIEZADEGAN, SHIRIN, and HAMID SOLTANIAN-ZADEH. "DESIGN AND EVALUATION OF MATCHED WAVELETS WITH MAXIMUM CODING GAIN AND MINIMUM APPROXIMATION ERROR CRITERIA FOR R PEAK DETECTION IN ECG." International Journal of Wavelets, Multiresolution and Information Processing 06, no. 06 (2008): 799–825. http://dx.doi.org/10.1142/s0219691308002690.

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Recently, several wavelet-based algorithms have been proposed for feature extraction in non-stationary signals such as ECG. These methods, however, have mainly used general purpose (unmatched) wavelet bases such as Daubechies and Quadratic Spline. In this paper, five new matched wavelet bases, with minimum approximation error and maximum coding gain criteria, are designed and applied to ECG signal analysis. To study the effect of using different wavelet bases for this application, two different wavelet-based R peak detection algorithms are implemented: (1) a conventional wavelet-based method;

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26

Ahn, Se-Jong, Chang-Joo Lim, Yong-Gwon Kim, and Sung-Taek Chung. "Study on R-peak Detection Algorithm of Arrhythmia Patients in ECG." Journal of the Korea Academia-Industrial cooperation Society 12, no. 10 (2011): 4443–49. http://dx.doi.org/10.5762/kais.2011.12.10.4443.

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27

Kaur, Amandeep, Sanjay Kumar, Alpana Agarwal, and Ravinder Agarwal. "An Efficient R-Peak Detection Using Riesz Fractional-Order Digital Differentiator." Circuits, Systems, and Signal Processing 39, no. 4 (2019): 1965–87. http://dx.doi.org/10.1007/s00034-019-01238-3.

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28

Sippel, Katrin, Julia Moser, Franziska Schleger, Hubert Preissl, Wolfgang Rosenstiel, and Martin Spüler. "Fully Automated R-peak Detection Algorithm (FLORA) for fetal magnetoencephalographic data." Computer Methods and Programs in Biomedicine 173 (May 2019): 35–41. http://dx.doi.org/10.1016/j.cmpb.2019.02.016.

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29

VOROPAI, Andrii, and Volodymyr SARANA. "REAL-TIME R-PEAK DETECTION ALGORITHM FOR LOW SNR ECG SIGNAL." Information Technology: Computer Science, Software Engineering and Cyber Security, no. 2 (2021): 3–9. http://dx.doi.org/10.32782/it/2021-2-1.

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30

Huque, A. S. A., K. I. Ahmed, M. A. Mukit, and R. Mostafa. "HMM-based Supervised Machine Learning Framework for the Detection of fECG R-R Peak Locations." IRBM 40, no. 3 (2019): 157–66. http://dx.doi.org/10.1016/j.irbm.2019.04.004.

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31

Khalaf, Akram, and Samir Mohammed. "A QRS-Detection Algorithm for Real-Time Applications." International Journal of Intelligent Engineering and Systems 14, no. 1 (2021): 356–67. http://dx.doi.org/10.22266/ijies2021.0228.33.

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The QRS detection algorithm is substantial for healthcare monitoring and diagnostic applications. A low error detection without adding more computation is a big challenge for researchers. The proposed QRS detection algorithm is a simple, real-time, and high-performance hybrid technique based on decision tree and artificial neural networks (ANN). In this study, the five stages algorithm is designed, implemented, and evaluated for wearable healthcare applications. The first stage is filtering the original ECG signal to reduce the noise and baseline wandering. After that, a maximum or minimum mov

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32

Moulton, Richard J., Stefan J. Konasiewicz, and Paul O'Connor. "A New Quantitative Measure for Monitoring Somatosensory Evoked Potentials." Canadian Journal of Neurological Sciences / Journal Canadien des Sciences Neurologiques 21, S1 (1994): S17—S22. http://dx.doi.org/10.1017/s0084255900007567.

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AbstractThis paper describes the development and testing of a computer algorithm to automate the process of peak identification and somatosensory evoked potential (SSEP) grading. We tested the accuracy of computerized peak detection and evaluated grading schemes using a test set of 60 SSEPs ranked from worst to best by the programmer (RJM) and a blinded grader (PO). The computer algorithm recognized 95% of peaks identified by visual inspection. Twelve percent of peaks identified by the computer were noise. Summed peak to peak amplitude gave the most accurate ranking of SSEPs. Rank correlation

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33

Sabar, Setiawidayat, Aviv Yuniar Rahman, and Ratna Hidayati. "Detection of Amplitude Peak and Duration of QRS Electrocardiogram Waves Using Discrete Data." Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) 4, no. 3 (2020): 438–46. http://dx.doi.org/10.29207/resti.v4i3.1658.

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In each cycle of the Heart on the Electrocardiogram there are generally P waves as a presentation of Atrial Muscle Depolarization, QRS waves as a presentation of Ventricular Muscle Depolarization and T waves as a presentation of Ventricular Muscle Repolarization. Some types of electrocardiographs only represent wave morphology and some other types of electrocardiographs are equipped with duration and amplitude information but are limited. This limitation of information is calculated manually using small boxes on ecg paper measuring 40 ms for duration and 1 mV for amplitude. The consequences of

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34

Lee, Dae-Seok, Gi-Hyun Hwang, and Kyoung-Hwan Cha. "R-peak Detection Algorithm in Wireless Sensor Node for Ubiquitous Healthcare Application." Journal of the Korean Institute of Information and Communication Engineering 15, no. 1 (2011): 227–32. http://dx.doi.org/10.6109/jkiice.2011.15.1.227.

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35

Kumar, S. Sachin, Neethu Mohan, P. Prabaharan, and K. P. Soman. "Total Variation Denoising Based Approach for R-peak Detection in ECG Signals." Procedia Computer Science 93 (2016): 697–705. http://dx.doi.org/10.1016/j.procs.2016.07.268.

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36

Dave, Tejal, and Utpal Pandya. "R peak detection for wireless ECG using DWT and entropy of coefficients." International Journal of Biomedical Engineering and Technology 34, no. 3 (2020): 268. http://dx.doi.org/10.1504/ijbet.2020.111472.

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37

Jafari Moghadam Fard, P., M. H. Moradi, and M. R. Tajvidi. "A novel approach in R peak detection using Hybrid Complex Wavelet (HCW)." International Journal of Cardiology 124, no. 2 (2008): 250–53. http://dx.doi.org/10.1016/j.ijcard.2006.11.236.

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38

Rakshit, M., D. Panigrahy, and P. K. Sahu. "An improved method for R-peak detection by using Shannon energy envelope." Sādhanā 41, no. 5 (2016): 469–77. http://dx.doi.org/10.1007/s12046-016-0485-8.

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39

Pandya, Utpal, and Tejal Dave. "R peak Detection for Wireless ECG using DWT and Entropy of coefficients." International Journal of Biomedical Engineering and Technology 1, no. 1 (2017): 1. http://dx.doi.org/10.1504/ijbet.2017.10013949.

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40

Lu, Qi. "RMKnet: A novel and parameter-efficient architecture for robust r-peak detection." Journal of Physics: Conference Series 2010, no. 1 (2021): 012057. http://dx.doi.org/10.1088/1742-6596/2010/1/012057.

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41

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 (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 computat

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42

Kelly, Ryan F., Philip E. Higuera, Carolyn M. Barrett, and Feng Sheng Hu. "Short Paper: A signal-to-noise index to quantify the potential for peak detection in sediment–charcoal records." Quaternary Research 75, no. 1 (2011): 11–17. http://dx.doi.org/10.1016/j.yqres.2010.07.011.

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AbstractCharcoal peaks in lake-sediment records are commonly used to reconstruct fire histories spanning thousands of years, but quantitative methods for evaluating the suitability of records for peak detection are largely lacking. We present a signal-to-noise index (SNI) that quantifies the separation of charcoal peaks (signal) from other variability in a record (noise). We validate the SNI with simulated and empirical charcoal records and show that an SNI > 3 consistently identifies records appropriate for peak detection. The SNI thus offers a means to evaluate the suitability of sediment

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43

Bae, Tae Wuk, Sang Hag Lee, and Kee Koo Kwon. "An Adaptive Median Filter Based on Sampling Rate for R-Peak Detection and Major-Arrhythmia Analysis." Sensors 20, no. 21 (2020): 6144. http://dx.doi.org/10.3390/s20216144.

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With the advancement of the Internet of Medical Things technology, many vital sign-sensing devices are being developed. Among the diverse healthcare devices, portable electrocardiogram (ECG) measuring devices are being developed most actively with the recent development of sensor technology. These ECG measuring devices use different sampling rates according to the hardware conditions, which is the first variable to consider in the development of ECG analysis technology. Herein, we propose an R-point detection method using an adaptive median filter based on the sampling rate and analyze major a

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44

Nayan, Nazrul Anuar, and Hafifah Ab Hamid. "Evaluation of patient electrocardiogram datasets using signal quality indexing." Bulletin of Electrical Engineering and Informatics 8, no. 2 (2019): 519–26. http://dx.doi.org/10.11591/eei.v8i2.1289.

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Electrocardiogram (ECG) is widely used in the hospital emergency rooms for detecting vital signs, such as heart rate variability and respiratory rate. However, the quality of the ECGs is inconsistent. ECG signals lose information because of noise resulting from motion artifacts. To obtain an accurate information from ECG, signal quality indexing (SQI) is used where acceptable thresholds are set in order to select or eliminate the signals for the subsequent information extraction process. A good evaluation of SQI depends on the R-peak detection quality. Nevertheless, most R-peak detectors in th

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45

Yasmeen, Fatima, Mohammad Arifuddin Mallick, and Yusuf Uzzaman Khan. "Detection of Real Time QRS Complex Using Wavelet Transform." International Journal of Electrical and Computer Engineering (IJECE) 8, no. 5 (2018): 2857. http://dx.doi.org/10.11591/ijece.v8i5.pp2857-2863.

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<p><span lang="EN-IN">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 predictivit

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46

JIA, Menghan, Feiteng LI, Zhijian CHEN, Xiaoyan XIANG, and Xiaolang YAN. "High Noise Tolerant R-Peak Detection Method Based on Deep Convolution Neural Network." IEICE Transactions on Information and Systems E102.D, no. 11 (2019): 2272–75. http://dx.doi.org/10.1587/transinf.2019edl8097.

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47

Shinde, Miss Aishwarya. "ECG Signal Processing for Feature Extraction with R Peak Detection and DWT Coefficients." International Journal for Research in Applied Science and Engineering Technology 8, no. 11 (2020): 887–90. http://dx.doi.org/10.22214/ijraset.2020.32320.

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48

Gopalakrishnan Nair, T. R., A. P. Geetha, and M. Asharani. "Continuous digital ECG analysis over accurate R-peak detection using adaptive wavelet technique." Journal of Medical Engineering & Technology 37, no. 7 (2013): 429–35. http://dx.doi.org/10.3109/03091902.2013.828105.

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49

Sadhukhan, Deboleena, and Madhuchhanda Mitra. "R-Peak Detection Algorithm for Ecg using Double Difference And RR Interval Processing." Procedia Technology 4 (2012): 873–77. http://dx.doi.org/10.1016/j.protcy.2012.05.143.

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50

LIU, SI, ENQI ZHAN, JIANBIN ZHENG, LIE YU, and TONG XUE. "REAL-TIME METHOD FOR ECG R-PEAK DETECTION COMBINING AUTOMATIC THRESHOLD AND DIFFERENTIATION." Journal of Mechanics in Medicine and Biology 20, no. 08 (2020): 1950023. http://dx.doi.org/10.1142/s0219519419500234.

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This paper proposed a novel real-time algorithm for electrocardiogram (ECG) signal analysis using the first derivative and automatic threshold to locate the R-peaks. First, the ECG signals are filtered by Butterworth low pass filter to reduce the high frequency noise. Then, the first 10[Formula: see text]s datasets of the first derivative of ECG signal are analyzed to search the maximum value. Three process thresholds are computed using this maximum value, which is used to avoid the missed and false peak detections. Thus, a threshold is automatically calculated using these searched maximum val

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