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Journal articles on the topic 'ECG extraction'

Author: Grafiati

Published: 28 June 2021

Last updated: 29 July 2025

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1

Gohil, Heena Jaysukh. "Real Time ECG Extraction." International Journal for Research in Applied Science and Engineering Technology 8, no. 2 (2020): 716–21. http://dx.doi.org/10.22214/ijraset.2020.2110.

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2

R, Rasu, P. Shanmugasundaram, and N. Santhiyakumari. "Fetal ECG Extraction from Maternal ECG using MATLAB." i-manager's Journal on Digital Signal Processing 3, no. 1 (2015): 7–11. http://dx.doi.org/10.26634/jdp.3.1.3284.

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3

Chandra, Shanti, Ambalika Sharma, and Girish Kumar Singh. "Feature extraction of ECG signal." Journal of Medical Engineering & Technology 42, no. 4 (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, and Bong-Gyun Seo. "Mobile ECG Measurement System Design with Fetal ECG Extraction Capability." Transactions of The Korean Institute of Electrical Engineers 66, no. 2 (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, and M. B. I. REAZ. "Fetal ECG Extraction from Maternal Abdominal ECG Using Neural Network." Journal of Software Engineering and Applications 02, no. 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 ., and Stanley Johnson . "Fetal ECG Extraction using Softcomputing Technique." Journal of Applied Sciences 6, no. 2 (2006): 251–56. http://dx.doi.org/10.3923/jas.2006.251.256.

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7

Bhyri, Channappa, S. T. Hamde, and L. M. Waghmare. "ECG feature extraction and disease diagnosis." Journal of Medical Engineering & Technology 35, no. 6-7 (2011): 354–61. http://dx.doi.org/10.3109/03091902.2011.595530.

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8

Patel, Ibrahim, A. Sandhya, V. Sripathi Raja, and S. Saravanan. "Extraction of Features from ECG Signal." International Journal of Current Research and Review 13, no. 08 (2021): 103–9. http://dx.doi.org/10.31782/ijcrr.2021.13806.

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9

M, Anisha, Dr S. S. Kumar, and Benisha M. "Methodological Survey on Fetal ECG Extraction." IOSR Journal of Computer Engineering 16, no. 5 (2014): 105–15. http://dx.doi.org/10.9790/0661-1657105115.

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10

曹, 雪. "Non-Invasive Fetal ECG Signal Extraction." Advances in Clinical Medicine 09, no. 04 (2019): 507–18. http://dx.doi.org/10.12677/acm.2019.94078.

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11

Jen, K. K., and Y. R. Hwang. "Long-term ECG signal feature extraction." Journal of Medical Engineering & Technology 31, no. 3 (2007): 202–9. http://dx.doi.org/10.1080/03091900600718675.

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12

Raj, Chinmayee G., V. Sri Harsha, B. Sai Gowthami, and 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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13

S V, Vinoth, and Kumarganesh S. "Fetal ECG Extraction using LMS Filter." International Journal of Electronics and Communication Engineering 3, no. 11 (2016): 3–5. http://dx.doi.org/10.14445/23488549/ijece-v3i11p111.

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14

Kanjilal, P. P., S. Palit, and P. K. Dey. "Fetal ECG Extraction from Maternal ECG Using the Singular Value Decomposition." IFAC Proceedings Volumes 26, no. 2 (1993): 183–86. http://dx.doi.org/10.1016/s1474-6670(17)48710-6.

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15

Rahman, Arafat, Sakib Mahmud, Muhammad E. H. Chowdhury, et al. "Fetal ECG extraction from maternal ECG using deeply supervised LinkNet++ model." Engineering Applications of Artificial Intelligence 123 (August 2023): 106414. http://dx.doi.org/10.1016/j.engappai.2023.106414.

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16

Hua, Xiyao, and Boni Su. "A Fetal ECG Extraction System Based on Blind Extraction Method." Journal of Software Engineering 9, no. 4 (2015): 848–57. http://dx.doi.org/10.3923/jse.2015.848.857.

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17

Mhetre, Pallavi S., and Dr Sunita S. Lokhande. "A Survey Paper on Arrhythmia Classification Using ECG Signals." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 07, no. 11 (2023): 1–11. http://dx.doi.org/10.55041/ijsrem27346.

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Cardiovascular diseases (CVDs) rank among diseasesof highest mortality. Electrocardiography (ECG) is a non-invasive tool to assess the generalcardiac condition of a patient and is therefore as first-in-line examination for diagnosis of CVD.Arrhythmia Classification plays a major role while diagnosing heart diseases. Any change in the regular sequence of electric impulses is called as arrhythmia. Identifyingarrhythmia as early as possible helps the patient in choosing appropriate treatment. Classification of ECG arrhythmia with high accuracy is a challenging problem. Arrhythmiaclassification re

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18

Kanjilal, P. P., S. Palit, and G. Saha. "Fetal ECG extraction from single-channel maternal ECG using singular value decomposition." IEEE Transactions on Biomedical Engineering 44, no. 1 (1997): 51–59. http://dx.doi.org/10.1109/10.553712.

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19

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

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20

Prof.S.M.Walke and Prof.S.S.Gundecha. "ADVANCED WAVELET TRANSFORM TECHNIQUES FOR ECG FEATURE EXTRACTION." JournalNX - A Multidisciplinary Peer Reviewed Journal QIPCEI2K18 (April 30, 2018): 148–54. https://doi.org/10.5281/zenodo.1411819.

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 Existing life care systems simply monitor human health and rely on a centralized server to store and process sensed data, leading to a high cost of system maintenance, yet with limited services and low performance. One of the important parameter in health Monitoring is ECG and Wireless ECG acquisition has emerged as a comfortable low-cost technology for continuous Cardiac Monitoring. The analysis of ECG is widely used for diagnosing many cardiac diseases, which are the main cause mortality in developed countries. Wireless ECG sensors have been employed to monitor human health and provide

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21

Das, Manab Kumar, and Samit Ari. "ECG Beats Classification Using Mixture of Features." International Scholarly Research Notices 2014 (September 17, 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 featur

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22

Kisan, Phadte Sneha, and Amita Dessai. "Classification and Morphological Extraction of ECG Parameters." IJIREEICE 4, no. 2 (2016): 217–20. http://dx.doi.org/10.17148/ijireeice/ncaee.2016.43.

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23

Mohammed, Abdullah, and Rajendra D. "An Efficient Approach for Fetal ECG Extraction." International Journal of Computer Applications 182, no. 33 (2018): 1–5. http://dx.doi.org/10.5120/ijca2018918258.

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24

Jallouli, Malika, Sabrine Arfaoui, Anouar Ben Mabrouk, and Carlo Cattani. "Clifford Wavelet Entropy for Fetal ECG Extraction." Entropy 23, no. 7 (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

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25

Saxena, Nishant, and Kshitij Shinghal. "Extraction of Various Features of ECG Signal." International Journal of Engineering Sciences & Emerging Technologies 7, no. 4 (2015): 707–14. http://dx.doi.org/10.7323/ijeset/v7_i4/02.

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26

SHUBHAM, MISHRA, PANDEY SHREYASH, DESHMUKH KHEMRAJ, and KUMAR JITENDRA. "FEATURE EXTRACTION OF ECG SIGNAL USING LABVIEW." i-manager's Journal on Digital Signal Processing 4, no. 1 (2016): 9. http://dx.doi.org/10.26634/jdp.4.1.4856.

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27

Martín-Clemente, Ruben, Jose Luis Camargo-Olivares, Susana Hornillo-Mellado, Mar Elena, and Isabel Román. "Fast Technique for Noninvasive Fetal ECG Extraction." IEEE Transactions on Biomedical Engineering 58, no. 2 (2011): 227–30. http://dx.doi.org/10.1109/tbme.2010.2059703.

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28

Wei, Zheng, Li Xiaolong, Wei Xueyun, and Liu Hongxing. "Foetal ECG extraction by support vector regression." Electronics Letters 52, no. 7 (2016): 506–7. http://dx.doi.org/10.1049/el.2016.0171.

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29

Cherian, Winnie Rachel, D. J. Jagannath, and A. Immanuel Selvakumar. "Comparison of Algorithms for Fetal ECG Extraction." International Journal of Engineering Trends and Technology 9, no. 11 (2014): 540–43. http://dx.doi.org/10.14445/22315381/ijett-v9p304.

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30

Parameshwari, R., C. Emlyn Gloria Ponrani, and S. Shenbaga Devi. "Foetal ECG extraction using BPN and UWT." International Journal of Biomedical Engineering and Technology 22, no. 1 (2016): 1. http://dx.doi.org/10.1504/ijbet.2016.078980.

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31

Lee, Ho Soo, Quin-lan Cheng, and Nitish V. Thakor. "ECG waveform analysis by significant point extraction." Computers and Biomedical Research 20, no. 5 (1987): 410–27. http://dx.doi.org/10.1016/0010-4809(87)90030-9.

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32

Cheng, Quin-Lan, Ho Soo Lee, and Nitish V. Thakor. "ECG waveform analysis by significant point extraction." Computers and Biomedical Research 20, no. 5 (1987): 428–42. http://dx.doi.org/10.1016/0010-4809(87)90031-0.

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33

Eilebrecht, Benjamin, Jorge Henriques, Teresa Rocha, et al. "Automatic Parameter Extraction from Capacitive ECG Measurements." Cardiovascular Engineering and Technology 3, no. 3 (2012): 319–32. http://dx.doi.org/10.1007/s13239-012-0101-y.

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34

Yuan, Li, Zhuhuang Zhou, Yanchao Yuan, and 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 improve

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35

Tuncer, Turker, Abdul Hafeez Baig, Emrah Aydemir, et al. "Cardioish: Lead-Based Feature Extraction for ECG Signals." Diagnostics 14, no. 23 (2024): 2712. https://doi.org/10.3390/diagnostics14232712.

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Background: Electrocardiography (ECG) signals are commonly used to detect cardiac disorders, with 12-lead ECGs being the standard method for acquiring these signals. The primary objective of this research is to propose a new feature engineering model that achieves both high classification accuracy and explainable results using ECG signals. To this end, a symbolic language, named Cardioish, has been introduced. Methods: In this research, two publicly available datasets were used: (i) a mental disorder classification dataset and (ii) a myocardial infarction (MI) dataset. These datasets contain E

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36

Rawi, Atiaf A., Murtada K. Elbashir, and Awadallah M. Ahmed. "ECG Heartbeat Classification Using CONVXGB Model." Electronics 11, no. 15 (2022): 2280. http://dx.doi.org/10.3390/electronics11152280.

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ELECTROCARDIOGRAM (ECG) signals are reliable in identifying and monitoring patients with various cardiac diseases and severe cardiovascular syndromes, including arrhythmia and myocardial infarction (MI). Thus, cardiologists use ECG signals in diagnosing cardiac diseases. Machine learning (ML) has also proven its usefulness in the medical field and in signal classification. However, current ML approaches rely on hand-crafted feature extraction methods or very complicated deep learning networks. This paper presents a novel method for feature extraction from ECG signals and ECG classification usi

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37

Fikri, Muhammad Rausan, Indah Soesanti, and Hanung Adi Nugroho. "ECG Signal Classification Review." IJITEE (International Journal of Information Technology and Electrical Engineering) 5, no. 1 (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. Ther

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38

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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39

Yu, Qiong, Huawen Yan, Lin Song, et al. "Automatic identifying of maternal ECG source when applying ICA in fetal ECG extraction." Biocybernetics and Biomedical Engineering 38, no. 3 (2018): 448–55. http://dx.doi.org/10.1016/j.bbe.2018.03.003.

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40

Ge, Zhaoyang, Huiqing Cheng, Zhuang Tong, et al. "ECG-MAKE: An ECG signal delineation approach based on medical attribute knowledge extraction." Information Sciences 637 (August 2023): 118978. http://dx.doi.org/10.1016/j.ins.2023.118978.

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41

Chen, Lin, Shuicai Wu, and Zhuhuang Zhou. "Fetal ECG Signal Extraction from Maternal Abdominal ECG Signals Using Attention R2W-Net." Sensors 25, no. 3 (2025): 601. https://doi.org/10.3390/s25030601.

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Fetal electrocardiogram (FECG) signals directly reflect the electrical activity of the fetal heart, enabling the assessment of fetal cardiac health. To effectively separate and extract FECG signals from maternal abdominal electrocardiogram (ECG) signals, this study proposed a W-shaped parallel network, termed Attention R2W-Net, which consisted of two Attention R2U-Nets. In the encoder and decoder, recurrent residual modules were used to replace feedforward convolutional layers, significantly enhancing feature representation and improving noise suppression. Additionally, attention gates were us

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42

Kim, Gwang-Nam, Han-ju Lee, Han-Jin Lee, and Seok-Hwan Choi. "ECG Data De-identification-based Defense Methods against Model Extraction Attack." Journal of Korean Institute of Intelligent Systems 34, no. 3 (2024): 202–9. http://dx.doi.org/10.5391/jkiis.2024.34.3.202.

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43

Jagannadham, D. B. V., D. V. Sai Narayana, P. Ganesh, and D. Koteswar. "Identification of myocardial infarction from analysis of ECG signal." International Journal of Knowledge-based and Intelligent Engineering Systems 24, no. 3 (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. Fe

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44

Sundari, Tribhuvanam, C. Nagaraj H, and P. S. Naidu V. "Analysis and classification of ECG beat based on wavelet decomposition and SVM." Indian Journal of Science and Technology 13, no. 24 (2020): 2404–17. https://doi.org/10.17485/IJST/v13i24.452.

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Abstract Objectives: To extract the features of single arrhythmia ECG beat. To develop efficient algorithms for automated detection of arrhythmia based on ECG. Methods/Statistical analysis: The methodology includes pre-processing and segmentation of ECG. Extraction of ECG features are to support the ECG beat classification and analysis of cardiac abnormalities using machine learning techniques. Wavelet decomposition is considered for feature extraction and classification with multiclass support vector machine. Findings: This work evaluates the suitability of the wavelet features of ECG for cla

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45

Jonkman, M., F. de Boer, and A. Matsuyama. "Improved ECG Signal Analysis Using Wavelet and Feature Extraction." Methods of Information in Medicine 46, no. 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 fe

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46

Gadallah, M., S. Alian, and Kh Reda. "Features Extraction of ECG Signals Using Wavelet Transforms." International Conference on Electrical Engineering 2, no. 2 (1999): 166–76. http://dx.doi.org/10.21608/iceeng.1999.62311.

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47

Sahay, Shalini, A. K. Wadhwani A.K.Wadhwani, and Sulochana Wadhwani. "A Survey Approach on ECG Feature Extraction Techniques." International Journal of Computer Applications 120, no. 11 (2015): 1–4. http://dx.doi.org/10.5120/21268-4002.

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48

SAXENA, S. C., A. SHARMA, and S. C. CHAUDHARY. "Data compression and feature extraction of ECG signals." International Journal of Systems Science 28, no. 5 (1997): 483–98. http://dx.doi.org/10.1080/00207729708929409.

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49

Richter, M., T. Schreiber, and D. T. Kaplan. "Fetal ECG extraction with nonlinear state-space projections." IEEE Transactions on Biomedical Engineering 45, no. 1 (1998): 133–37. http://dx.doi.org/10.1109/10.650369.

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50

Soorma, Neha, Jaikaran Singh, and Mukesh Tiwari. "Feature Extraction of ECG Signal Using HHT Algorithm." International Journal of Engineering Trends and Technology 8, no. 8 (2014): 454–60. http://dx.doi.org/10.14445/22315381/ijett-v8p278.

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