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Thematische Bibliographien / ECG extraction / Zeitschriftenartikel

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Zeitschriftenartikel zum Thema „ECG extraction“

Autor: Grafiati

Veröffentlicht am 28. Juni 2021

Zuletzt aktualisiert am 14. Februar 2022

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1

Gohil, Heena Jaysukh. „Real Time ECG Extraction“. International Journal for Research in Applied Science and Engineering Technology 8, Nr. 2 (29.02.2020): 716–21. http://dx.doi.org/10.22214/ijraset.2020.2110.

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2

R, Rasu, P. Shanmugasundaram und N. Santhiyakumari. „Fetal ECG Extraction from Maternal ECG using MATLAB“. i-manager's Journal on Digital Signal Processing 3, Nr. 1 (15.03.2015): 7–11. http://dx.doi.org/10.26634/jdp.3.1.3284.

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3

Chandra, Shanti, Ambalika Sharma und Girish Kumar Singh. „Feature extraction of ECG signal“. Journal of Medical Engineering & Technology 42, Nr. 4 (19.05.2018): 306–16. http://dx.doi.org/10.1080/03091902.2018.1492039.

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4

Choi, Chul-Hyung, Young-Pil Kim, Si-Kyung Kim, Jeong-Bong You und Bong-Gyun Seo. „Mobile ECG Measurement System Design with Fetal ECG Extraction Capability“. Transactions of The Korean Institute of Electrical Engineers 66, Nr. 2 (01.02.2017): 431–38. http://dx.doi.org/10.5370/kiee.2017.66.2.431.

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5

HASAN, M. A., M. I. IBRAHIMY und M. B. I. REAZ. „Fetal ECG Extraction from Maternal Abdominal ECG Using Neural Network“. Journal of Software Engineering and Applications 02, Nr. 05 (2009): 330–34. http://dx.doi.org/10.4236/jsea.2009.25043.

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6

Selva Viji, C. Kezi, M. E. ,. P. Kanagasabap . und Stanley Johnson . „Fetal ECG Extraction using Softcomputing Technique“. Journal of Applied Sciences 6, Nr. 2 (01.01.2006): 251–56. http://dx.doi.org/10.3923/jas.2006.251.256.

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7

Bhyri, Channappa, S. T. Hamde und L. M. Waghmare. „ECG feature extraction and disease diagnosis“. Journal of Medical Engineering & Technology 35, Nr. 6-7 (20.07.2011): 354–61. http://dx.doi.org/10.3109/03091902.2011.595530.

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8

Raj, Chinmayee G., V. Sri Harsha, B. Sai Gowthami und Sunitha R. „Virtual Instrumentation Based Fetal ECG Extraction“. Procedia Computer Science 70 (2015): 289–95. http://dx.doi.org/10.1016/j.procs.2015.10.093.

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9

Jen, K. K., und Y. R. Hwang. „Long-term ECG signal feature extraction“. Journal of Medical Engineering & Technology 31, Nr. 3 (Januar 2007): 202–9. http://dx.doi.org/10.1080/03091900600718675.

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10

S V, Vinoth, und Kumarganesh S. „Fetal ECG Extraction using LMS Filter“. International Journal of Electronics and Communication Engineering 3, Nr. 11 (25.11.2016): 3–5. http://dx.doi.org/10.14445/23488549/ijece-v3i11p111.

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11

M, Anisha, Dr S. S. Kumar und Benisha M. „Methodological Survey on Fetal ECG Extraction“. IOSR Journal of Computer Engineering 16, Nr. 5 (2014): 105–15. http://dx.doi.org/10.9790/0661-1657105115.

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12

曹, 雪. „Non-Invasive Fetal ECG Signal Extraction“. Advances in Clinical Medicine 09, Nr. 04 (2019): 507–18. http://dx.doi.org/10.12677/acm.2019.94078.

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13

Patel, Ibrahim, A. Sandhya, V. Sripathi Raja und S. Saravanan. „Extraction of Features from ECG Signal“. International Journal of Current Research and Review 13, Nr. 08 (2021): 103–9. http://dx.doi.org/10.31782/ijcrr.2021.13806.

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14

Kanjilal, P. P., S. Palit und P. K. Dey. „Fetal ECG Extraction from Maternal ECG Using the Singular Value Decomposition“. IFAC Proceedings Volumes 26, Nr. 2 (Juli 1993): 183–86. http://dx.doi.org/10.1016/s1474-6670(17)48710-6.

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15

Hua, Xiyao, und Boni Su. „A Fetal ECG Extraction System Based on Blind Extraction Method“. Journal of Software Engineering 9, Nr. 4 (15.09.2015): 848–57. http://dx.doi.org/10.3923/jse.2015.848.857.

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16

Kanjilal, P. P., S. Palit und G. Saha. „Fetal ECG extraction from single-channel maternal ECG using singular value decomposition“. IEEE Transactions on Biomedical Engineering 44, Nr. 1 (1997): 51–59. http://dx.doi.org/10.1109/10.553712.

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17

John, Rolant Gini, und K. I. Ramachandran. „Extraction of foetal ECG from abdominal ECG by nonlinear transformation and estimations“. Computer Methods and Programs in Biomedicine 175 (Juli 2019): 193–204. http://dx.doi.org/10.1016/j.cmpb.2019.04.022.

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18

Saxena, Nishant, und Kshitij Shinghal. „Extraction of Various Features of ECG Signal“. International Journal of Engineering Sciences & Emerging Technologies 7, Nr. 4 (01.01.2015): 707–14. http://dx.doi.org/10.7323/ijeset/v7_i4/02.

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19

Kisan, Phadte Sneha, und Amita Dessai. „Classification and Morphological Extraction of ECG Parameters“. IJIREEICE 4, Nr. 2 (11.04.2016): 217–20. http://dx.doi.org/10.17148/ijireeice/ncaee.2016.43.

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20

Mohammed, Abdullah, und Rajendra D. „An Efficient Approach for Fetal ECG Extraction“. International Journal of Computer Applications 182, Nr. 33 (17.12.2018): 1–5. http://dx.doi.org/10.5120/ijca2018918258.

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21

Martín-Clemente, Ruben, Jose Luis Camargo-Olivares, Susana Hornillo-Mellado, Mar Elena und Isabel Román. „Fast Technique for Noninvasive Fetal ECG Extraction“. IEEE Transactions on Biomedical Engineering 58, Nr. 2 (Februar 2011): 227–30. http://dx.doi.org/10.1109/tbme.2010.2059703.

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22

Parameshwari, R., C. Emlyn Gloria Ponrani und S. Shenbaga Devi. „Foetal ECG extraction using BPN and UWT“. International Journal of Biomedical Engineering and Technology 22, Nr. 1 (2016): 1. http://dx.doi.org/10.1504/ijbet.2016.078980.

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23

Wei, Zheng, Li Xiaolong, Wei Xueyun und Liu Hongxing. „Foetal ECG extraction by support vector regression“. Electronics Letters 52, Nr. 7 (April 2016): 506–7. http://dx.doi.org/10.1049/el.2016.0171.

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24

SHUBHAM, MISHRA, PANDEY SHREYASH, DESHMUKH KHEMRAJ und KUMAR JITENDRA. „FEATURE EXTRACTION OF ECG SIGNAL USING LABVIEW“. i-manager's Journal on Digital Signal Processing 4, Nr. 1 (2016): 9. http://dx.doi.org/10.26634/jdp.4.1.4856.

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25

Cherian, Winnie Rachel, D. J. Jagannath und A. Immanuel Selvakumar. „Comparison of Algorithms for Fetal ECG Extraction“. International Journal of Engineering Trends and Technology 9, Nr. 11 (25.03.2014): 540–43. http://dx.doi.org/10.14445/22315381/ijett-v9p304.

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26

Eilebrecht, Benjamin, Jorge Henriques, Teresa Rocha, Marian Walter, Simão Paredes, Paulo de Carvalho, Michael Czaplik und Steffen Leonhardt. „Automatic Parameter Extraction from Capacitive ECG Measurements“. Cardiovascular Engineering and Technology 3, Nr. 3 (26.06.2012): 319–32. http://dx.doi.org/10.1007/s13239-012-0101-y.

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27

Lee, Ho Soo, Quin-lan Cheng und Nitish V. Thakor. „ECG waveform analysis by significant point extraction“. Computers and Biomedical Research 20, Nr. 5 (Oktober 1987): 410–27. http://dx.doi.org/10.1016/0010-4809(87)90030-9.

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28

Cheng, Quin-Lan, Ho Soo Lee und Nitish V. Thakor. „ECG waveform analysis by significant point extraction“. Computers and Biomedical Research 20, Nr. 5 (Oktober 1987): 428–42. http://dx.doi.org/10.1016/0010-4809(87)90031-0.

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29

Jallouli, Malika, Sabrine Arfaoui, Anouar Ben Mabrouk und Carlo Cattani. „Clifford Wavelet Entropy for Fetal ECG Extraction“. Entropy 23, Nr. 7 (30.06.2021): 844. http://dx.doi.org/10.3390/e23070844.

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Analysis of the fetal heart rate during pregnancy is essential for monitoring the proper development of the fetus. Current fetal heart monitoring techniques lack the accuracy in fetal heart rate monitoring and features acquisition, resulting in diagnostic medical issues. The challenge lies in the extraction of the fetal ECG from the mother ECG during pregnancy. This approach has the advantage of being a reliable and non-invasive technique. In the present paper, a wavelet/multiwavelet method is proposed to perfectly extract the fetal ECG parameters from the abdominal mother ECG. In a first step, due to the wavelet/mutiwavelet processing, a denoising procedure is applied to separate the noised parts from the denoised ones. The denoised signal is assumed to be a mixture of both the MECG and the FECG. One of the well-known measures of accuracy in information processing is the concept of entropy. In the present work, a wavelet/multiwavelet Shannon-type entropy is constructed and applied to evaluate the order/disorder of the extracted FECG signal. The experimental results apply to a recent class of Clifford wavelets constructed in Arfaoui, et al. J. Math. Imaging Vis. 2020, 62, 73–97, and Arfaoui, et al. Acta Appl. Math. 2020, 170, 1–35. Additionally, classical Haar–Faber–Schauder wavelets are applied for the purpose of comparison. Two main well-known databases have been applied, the DAISY database and the CinC Challenge 2013 database. The achieved accuracy over the test databases resulted in Se = 100%, PPV = 100% for FECG extraction and peak detection.

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30

Ionescu, Viorel. „Fetal ECG Extraction from Multichannel Abdominal ECG Recordings for Health Monitoring During Labor“. Procedia Technology 22 (2016): 682–89. http://dx.doi.org/10.1016/j.protcy.2016.01.143.

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31

Yu, Qiong, Huawen Yan, Lin Song, Wenya Guo, Hongxing Liu, Junfeng Si und Ying Zhao. „Automatic identifying of maternal ECG source when applying ICA in fetal ECG extraction“. Biocybernetics and Biomedical Engineering 38, Nr. 3 (2018): 448–55. http://dx.doi.org/10.1016/j.bbe.2018.03.003.

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32

Das, Manab Kumar, und Samit Ari. „ECG Beats Classification Using Mixture of Features“. International Scholarly Research Notices 2014 (17.09.2014): 1–12. http://dx.doi.org/10.1155/2014/178436.

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Classification of electrocardiogram (ECG) signals plays an important role in clinical diagnosis of heart disease. This paper proposes the design of an efficient system for classification of the normal beat (N), ventricular ectopic beat (V), supraventricular ectopic beat (S), fusion beat (F), and unknown beat (Q) using a mixture of features. In this paper, two different feature extraction methods are proposed for classification of ECG beats: (i) S-transform based features along with temporal features and (ii) mixture of ST and WT based features along with temporal features. The extracted feature set is independently classified using multilayer perceptron neural network (MLPNN). The performances are evaluated on several normal and abnormal ECG signals from 44 recordings of the MIT-BIH arrhythmia database. In this work, the performances of three feature extraction techniques with MLP-NN classifier are compared using five classes of ECG beat recommended by AAMI (Association for the Advancement of Medical Instrumentation) standards. The average sensitivity performances of the proposed feature extraction technique for N, S, F, V, and Q are 95.70%, 78.05%, 49.60%, 89.68%, and 33.89%, respectively. The experimental results demonstrate that the proposed feature extraction techniques show better performances compared to other existing features extraction techniques.

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33

Yuan, Li, Zhuhuang Zhou, Yanchao Yuan und Shuicai Wu. „An Improved FastICA Method for Fetal ECG Extraction“. Computational and Mathematical Methods in Medicine 2018 (2018): 1–7. http://dx.doi.org/10.1155/2018/7061456.

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Objective. The fast fixed-point algorithm for independent component analysis (FastICA) has been widely used in fetal electrocardiogram (ECG) extraction. However, the FastICA algorithm is sensitive to the initial weight vector, which affects the convergence of the algorithm. In order to solve this problem, an improved FastICA method was proposed to extract fetal ECG. Methods. First, the maternal abdominal mixed signal was centralized and whitened, and the overrelaxation factor was incorporated into Newton’s iterative algorithm to process the initial weight vector randomly generated. The improved FastICA algorithm was used to separate the source components, selected the best maternal ECG from the separated source components, and detected the R-wave location of the maternal ECG. Finally, the maternal ECG component in each channel was removed by the singular value decomposition (SVD) method to obtain a clean fetal ECG signal. Results. An annotated clinical fetal ECG database was used to evaluate the improved algorithm and the conventional FastICA algorithm. The average number of iterations of the algorithm was reduced from 35 before the improvement to 13. Correspondingly, the average running time was reduced from 1.25 s to 1.04 s when using the improved algorithm. The signal-to-noise ratio (SNR) based on eigenvalues of the improved algorithm was 1.55, as compared to 0.99 of the conventional FastICA algorithm. The SNR based on cross-correlation coefficients of the conventional algorithm was also improved from 0.59 to 2.02. The sensitivity, positive predictive accuracy, and harmonic mean (F1) of the improved method were 99.37%, 99.00%, and 99.19%, respectively, while these metrics of the conventional FastICA method were 99.03%, 98.53%, and 98.78%, respectively. Conclusions. The proposed improved FastICA algorithm based on the overrelaxation factor, while maintaining the rate of convergence, relaxes the requirement of initial weight vector, avoids the unbalanced convergence, reduces the number of iterations, and improves the convergence performance.

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Fikri, Muhammad Rausan, Indah Soesanti und Hanung Adi Nugroho. „ECG Signal Classification Review“. IJITEE (International Journal of Information Technology and Electrical Engineering) 5, Nr. 1 (18.06.2021): 15. http://dx.doi.org/10.22146/ijitee.60295.

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The heart is an important part of the human body, functioning to pump blood through the circulatory system. Heartbeats generate a signal called an ECG signal. ECG signals or electrocardiogram signals are basic raw signals to identify and classify heart function based on heart rate. Its main task is to analyze each signal in the heart, whether normal or abnormal. This paper discusses some of the classification methods which most frequently used to classify ECG signals. These methods include pre-processing, feature extraction, and classification methods such as MLP, K-NN, SVM, CNN, and RNN. There were two stages of ECG classification, the feature extraction stage and the classification stage. Before ECG features were extracted, raw ECG signal data first processed in the pre-processing stage because ECG signals were not necessarily free of noise. Noise will cause a decrease in accuracy during the classification process. After features were extracted, ECG signals were then classified with the classification method. Neural Network methods such as CNN and RNN are best to use since they can give better accuracy. For further research, the machine learning method needs to be improved to get high accuracy and high precision in the ECG signals classification.

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Sahay, Shalini, A. K. Wadhwani A.K.Wadhwani und Sulochana Wadhwani. „A Survey Approach on ECG Feature Extraction Techniques“. International Journal of Computer Applications 120, Nr. 11 (18.06.2015): 1–4. http://dx.doi.org/10.5120/21268-4002.

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36

Gadallah, M., S. Alian und Kh Reda. „Features Extraction of ECG Signals Using Wavelet Transforms“. International Conference on Electrical Engineering 2, Nr. 2 (01.11.1999): 166–76. http://dx.doi.org/10.21608/iceeng.1999.62311.

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37

Patro, Kiran Kumar, und P. Rajesh Kumar. „Effective Feature Extraction of ECG for Biometric Application“. Procedia Computer Science 115 (2017): 296–306. http://dx.doi.org/10.1016/j.procs.2017.09.138.

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38

SAXENA, S. C., A. SHARMA und S. C. CHAUDHARY. „Data compression and feature extraction of ECG signals“. International Journal of Systems Science 28, Nr. 5 (Mai 1997): 483–98. http://dx.doi.org/10.1080/00207729708929409.

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39

Richter, M., T. Schreiber und D. T. Kaplan. „Fetal ECG extraction with nonlinear state-space projections“. IEEE Transactions on Biomedical Engineering 45, Nr. 1 (1998): 133–37. http://dx.doi.org/10.1109/10.650369.

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40

Kunzmann, U., G. Wagner, J. Schöchlin und A. Bolz. „PARAMETER EXTRACTION OF ECG SIGNALS IN REAL-TIME“. Biomedizinische Technik/Biomedical Engineering 47, s1b (2002): 875–78. http://dx.doi.org/10.1515/bmte.2002.47.s1b.875.

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41

Soorma, Neha, Jaikaran Singh und Mukesh Tiwari. „Feature Extraction of ECG Signal Using HHT Algorithm“. International Journal of Engineering Trends and Technology 8, Nr. 8 (25.02.2014): 454–60. http://dx.doi.org/10.14445/22315381/ijett-v8p278.

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42

Montemurro, Alessandro, Jonas L. Isaksen, Torben Hansen, Allan Linneberg und Jørgen K. Kanters. „Temporal Convolutional Networks for Automatic ECG Features Extraction“. Journal of Electrocardiology 57 (November 2019): S106—S107. http://dx.doi.org/10.1016/j.jelectrocard.2019.08.038.

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43

Akhbari, Mahsa, Nasim Montazeri Ghahjaverestan, Mohammad B. Shamsollahi und Christian Jutten. „ECG fiducial point extraction using switching Kalman filter“. Computer Methods and Programs in Biomedicine 157 (April 2018): 129–36. http://dx.doi.org/10.1016/j.cmpb.2018.01.018.

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44

Camargo-Olivares, J. L., R. Martín-Clemente, S. Hornillo-Mellado, M. M. Elena und I. Román. „The Maternal Abdominal ECG as Input to MICA in the Fetal ECG Extraction Problem“. IEEE Signal Processing Letters 18, Nr. 3 (März 2011): 161–64. http://dx.doi.org/10.1109/lsp.2011.2104415.

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45

Wang, Shuang, Shugang Zhang, Zhen Li, Lei Huang und Zhiqiang Wei. „Automatic digital ECG signal extraction and normal QRS recognition from real scene ECG images“. Computer Methods and Programs in Biomedicine 187 (April 2020): 105254. http://dx.doi.org/10.1016/j.cmpb.2019.105254.

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46

Xie, Zhisheng, Qundi Liu, Zhikun Liang, Mingqian Zhao, Xiaoxue Yu, Depo Yang und Xinjun Xu. „The GC/MS Analysis of Volatile Components Extracted by Different Methods fromExocarpium Citri Grandis“. Journal of Analytical Methods in Chemistry 2013 (2013): 1–8. http://dx.doi.org/10.1155/2013/918406.

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Volatile components fromExocarpium Citri Grandis(ECG) were, respectively, extracted by three methods, that is, steam distillation (SD), headspace solid-phase microextraction (HS-SPME), and solvent extraction (SE). A total of 81 compounds were identified by gas chromatography-mass spectrometry including 77 (SD), 56 (HS-SPME), and 48 (SE) compounds, respectively. Despite of the extraction method, terpenes (39.98~57.81%) were the main volatile components of ECG, mainly germacrene-D, limonene, 2,6,8,10,14-hexadecapentaene, 2,6,11,15-tetramethyl-, (E,E,E)-, andtrans-caryophyllene. Comparison was made among the three methods in terms of extraction profile and property. SD relatively gave an entire profile of volatile in ECG by long-time extraction; SE enabled the analysis of low volatility and high molecular weight compounds but lost some volatiles components; HS-SPME generated satisfactory extraction efficiency and gave similar results to those of SD at analytical level when consuming less sample amount, shorter extraction time, and simpler procedure. Although SD and SE were treated as traditionally preparative extractive techniques for volatiles in both small batches and large scale, HS-SPME coupled with GC/MS could be useful and appropriative for the rapid extraction and qualitative analysis of volatile components from medicinal plants at analytical level.

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Jonkman, M., F. de Boer und A. Matsuyama. „Improved ECG Signal Analysis Using Wavelet and Feature Extraction“. Methods of Information in Medicine 46, Nr. 02 (2007): 227–30. http://dx.doi.org/10.1055/s-0038-1625412.

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Summary Objectives : Automatic detection of arrhythmias is important for diagnosis of heart problems. However, in ECG signals, there is significant variation of waveforms in both normal and abnormal beats. It is this phenomenon, which makes it difficult to analyse ECG signals. The aim of developing methodology is to distinguish between normal beats and abnormal beats in an ECG signal. Methods : ECG signals were first decomposed using wavelet transform. The feature vectors were then extracted from these decomposed signals as normalised energy and entropy. To improve the classification of the feature vectors of normal and abnormal beats, the normal beats which occur before and after the abnormal beats were eliminated from the group of normal beats. Results : With our proposed methods, the normal beats and abnormal beats formed different clusters of vector points. By eliminating normal beats which occur before and after the abnormal beats, the clusters of different types of beats showed more apparent separation. Conclusions : The combination of wavelet decomposition and the classification using feature vectors of the beats in ECG signals separate abnormal beats from normal beats. The elimination of the normal beats which occur before and after the abnormal beats succeeded in minimising the size of normal beats cluster.

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Lastre-Domínguez, Carlos, Yuriy S. Shmaliy, Oscar Ibarra-Manzano, Jorge Munoz-Minjares und Luis J. Morales-Mendoza. „ECG Signal Denoising and Features Extraction Using Unbiased FIR Smoothing“. BioMed Research International 2019 (20.02.2019): 1–16. http://dx.doi.org/10.1155/2019/2608547.

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Methods of the electrocardiography (ECG) signal features extraction are required to detect heart abnormalities and different kinds of diseases. However, different artefacts and measurement noise often hinder providing accurate features extraction. One of the standard techniques developed for ECG signals employs linear prediction. Referring to the fact that prediction is not required for ECG signal processing, smoothing can be more efficient. In this paper, we employ the p-shift unbiased finite impulse response (UFIR) filter, which becomes smooth by p<0. We develop this filter to have an adaptive averaging horizon: optimal for slow ECG behaviours and minimal for fast excursions. It is shown that the adaptive UFIR algorithm developed in such a way provides better denoising and suboptimal features extraction in terms of the output signal-noise ratio (SNR). The algorithm is developed to detect durations and amplitudes of the P-wave, QRS-complex, and T-wave in the standard ECG signal map. Better performance of the algorithm designed is demonstrated in a comparison with the standard linear predictor, UFIR filter, and UFIR predictive filter based on real ECG data associated with normal heartbeats.

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Jagannadham, D. B. V., D. V. Sai Narayana, P. Ganesh und D. Koteswar. „Identification of myocardial infarction from analysis of ECG signal“. International Journal of Knowledge-based and Intelligent Engineering Systems 24, Nr. 3 (28.09.2020): 217–26. http://dx.doi.org/10.3233/kes-200043.

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Many heart diseases can be identified and cured at an early stage by studying the changes in the features of electrocardiogram (ECG) signal. Myocardial Infarction (MI) is the serious cause of death worldwide. If MI can be detected early, the death rate will reduce. In this paper, an algorithm to detect MI in an ECG signal using Daubechies wavelet transform technique is developed. The ECG signal-denoising is performed by removing the corresponding wavelet coefficients at higher scale. After denoising, an important step towards identifying an arrhythmia is the feature extraction from the ECG. Feature extraction is carried out to detect the R peaks of the ECG signal. Since as R peak is having the highest amplitude, and therefore it is detected in the first round, subsequently location of other peaks are determined. Having completed the preprocessing and the feature extraction the MI is detected from the ECG based on inverted T wave logic and ST segment elevation. The algorithm was evaluated using MIT-BIH database and European database satisfactorily.

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Talib, Mushtaq, Ali A. Abdullah, Ahmed K. Abdullah und Bahaa Bahaa. „TWIN FETUS ECG SIGNAL EXTRACTION BASED ON TEMPORAL PREDICTABILITY“. Kufa Journal of Engineering 11, Nr. 1 (25.01.2020): 35–51. http://dx.doi.org/10.30572/2018/kje/110103.

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