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Academic literature on the topic 'FECG EXTRACTION'

Author: Grafiati
Published: 11 September 2023

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Journal articles on the topic "FECG EXTRACTION"

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Hao, Jingyu, Yuyao Yang, Zhuhuang Zhou, and Shuicai Wu. "Fetal Electrocardiogram Signal Extraction Based on Fast Independent Component Analysis and Singular Value Decomposition." Sensors 22, no. 10 (May 12, 2022): 3705. http://dx.doi.org/10.3390/s22103705.

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Fetal electrocardiograms (FECGs) provide important clinical information for early diagnosis and intervention. However, FECG signals are extremely weak and are greatly influenced by noises. FECG signal extraction and detection are still challenging. In this work, we combined the fast independent component analysis (FastICA) algorithm with singular value decomposition (SVD) to extract FECG signals. The improved wavelet mode maximum method was applied to detect QRS waves and ST segments of FECG signals. We used the abdominal and direct fetal ECG database (ADFECGDB) and the Cardiology Challenge Database (PhysioNet2013) to verify the proposed algorithm. The signal-to-noise ratio of the best channel signal reached 45.028 dB and the issue of missing waveforms was addressed. The sensitivity, positive predictive value and F1 score of fetal QRS wave detection were 96.90%, 98.23%, and 95.24%, respectively. The proposed algorithm may be used as a new method for FECG signal extraction and detection.
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K., Ricky, Arjuna M, and Sadegh Aminifar. "Fetal Heart Rate Extraction using NLMS Algorithm." International Journal of Biology and Biomedical Engineering 15 (April 7, 2021): 61–67. http://dx.doi.org/10.46300/91011.2021.15.8.

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This project develops a fetal heart rate (FHR) extraction application to analyze the fetus activity in the mother uterus. Several methods are available that can be used to detect FHR such as using the fetal electrocardiogram (FECG) that generated by fetus’ heart. Extracting FECG signals is considered a major challenge while the fetus is inside the mother uterus. Normalized Least Mean Square (NLMS) algorithm is one of adaptive filters that is chosen as adaptive filter to get FECG. Pan Tompkins algorithm is used for tracking R-peaks of heartbeat pulses of FECG signal. After detecting the RR interval a formula is used to calculate the bpm (heartbeat per minute) of FECG. Abdominal and direct FECG (ADFECG) database will be used to evaluate the implemented techniques as it has reference signal. At the end of research, calculated FHR is varied from 125.4 bpm to 130.3 bpm. When comparison is done between abdominal ECG (AECG) and direct FECG (DFECG), the error of FHR is 0.1%. The accuracy of R-peaks extraction is 100% where all R-peaks are detected by implemented techniques. MATLAB is used for signal simulations. This system will have ability to interpret the non-invasive FECG (NIFECG) database and compute its FHR.
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Taha, Luay, and Esam Abdel-Raheem. "A Null Space-Based Blind Source Separation for Fetal Electrocardiogram Signals." Sensors 20, no. 12 (June 22, 2020): 3536. http://dx.doi.org/10.3390/s20123536.

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This paper presents a new non-invasive deterministic algorithm of extracting the fetal Electrocardiogram (FECG) signal based on a new null space idempotent transformation matrix (NSITM). The mixture matrix is used to compute the ITM. Then, the fetal ECG (FECG) and maternal ECG (MECG) signals are extracted from the null space of the ITM. Next, MECG and FECG peaks detection, control logic, and adaptive comb filter are used to remove the unwanted MECG component from the raw FECG signal, thus extracting a clean FECG signal. The visual results from Daisy and Physionet real databases indicate that the proposed algorithm is effective in extracting the FECG signal, which can be compared with principal component analysis (PCA), fast independent component analysis (FastICA), and parallel linear predictor (PLP) filter algorithms. Results from Physionet synthesized ECG data show considerable improvement in extraction performances over other algorithms used in this work, considering different additive signal-to-noise ratio (SNR) increasing from 0 dB to 12 dB, and considering different fetal-to-maternal SNR increasing from −30 dB to 0 dB. The FECG detection of the NSITM is evaluated using statistical measures and results show considerable improvement in the sensitivity (SE), the accuracy (ACC), and the positive predictive value (PPV), as compared with other algorithms. The study demonstrated that the NSITM is a feasible algorithm for FECG extraction.
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Liao, Qiong, Jie Luo, and Yang Liu. "Fetal Electrocardiogram Extraction Based on SWT-MM Method." Applied Mechanics and Materials 644-650 (September 2014): 4415–21. http://dx.doi.org/10.4028/www.scientific.net/amm.644-650.4415.

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Fetal electrocardiogram (FECG) is of great importance due to the potentially precise information that FECG carries could assist clinicians in making more appropriate and timely decisions during pregnancy and labor. In this paper, a method based on combined Stationary Wavelet Transform and Modulus-Maxima (SWT-MM) method is proposed for extracting the complete morphology of the FECG from maternal abdominal ECG (AECG). It particularly provides a different way of constructing the maternal ECG (MECG) template. The Efficacy of the method was validated using real data in Non-Invasive Fetal Electrocardiogram Database. The morphology of the extracted FECG was clearly seen that the fetal R-peak detection by simple differential-threshold method acquired the average accuracy of 96.8%. The method provides additional important benefits of fast speed and automated control for applying into the fetal monitors. Therefore, the method is potentially a strong tool for FECG extraction, especially in real-time use.
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Graupe, D., M. H. Graupe, Y. Zhong, and R. K. Jackson. "Blind adaptive filtering for non-invasive extraction of the fetal electrocardiogram and its non-stationarities." Proceedings of the Institution of Mechanical Engineers, Part H: Journal of Engineering in Medicine 222, no. 8 (November 1, 2008): 1221–34. http://dx.doi.org/10.1243/09544119jeim417.

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The objective is to extract automatically a beat-to-beat fetal electrocardiogram (fECG) from a maternal electrocardiogram (mECG) using surface electrodes placed on the maternal abdomen and to derive fetal PR, QT, QTc, and QS durations to allow early diagnosis and monitoring treatment of certain fetal cardiac disorders. mECG and abdominal noise in abdominal maternal recordings can be orders of magnitude stronger than the fECG signal and the P and T waves that are embedded in them. A two-stage blind adaptive filtering algorithm was used for fECG extraction, the first stage using frequency-domain electrocardiogram features and the second considering time-domain features. Three channels of abdominal recordings were obtained from 12 patients at 20–40 weeks of gestation. In each case beat-to-beat unaveraged fECGs were isolated. The combined filter allowed identification of diagnostically important PR, QT, and RR durations. Comparison with synthetic data is also included.
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Li, Rui, and Bao Feng Chen. "FECG Extraction Algorithm Based on BSS Using Temporal Structure and DWT." Applied Mechanics and Materials 571-572 (June 2014): 209–12. http://dx.doi.org/10.4028/www.scientific.net/amm.571-572.209.

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Fetal electrocardiogram (FECG) blind source extraction (BSE) algorithm based on temporal structure and discrete wavelet transformation (DWT) in noise is proposed in this paper. After building the basic blind source separation (BSS) and BSE models for FECG, some preprocessing procedures based on the temporal structure of the FECG are constructed. Using DWT we can move the conventional time-domain signals to the wavelet-domain, and then the source number is detected and the robust noise reduction technique in FECG can be deduced too. According this preprocessing and second-order statistics (SOS), the proposed robust FECG extraction algorithm is derived.
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Li, Yibing, Wei Nie, Fang Ye, and Ao Li. "A Fetal Electrocardiogram Signal Extraction Algorithm Based on the Temporal Structure and the Non-Gaussianity." Computational and Mathematical Methods in Medicine 2016 (2016): 1–10. http://dx.doi.org/10.1155/2016/9658410.

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Fetal electrocardiogram (FECG) extraction is an important issue in biomedical signal processing. In this paper, we develop an objective function for extraction of FECG. The objective function is based on the non-Gaussianity and the temporal structure of source signals. Maximizing the objective function, we can extract the desired FECG. Combining with the solution vector obtained by maximizing the objective function, we further improve the accuracy of the extracted FECG. In addition, the feasibility of the innovative methods is analyzed by mathematical derivation theoretically and the efficiency of the proposed approaches is illustrated with the computer simulations experimentally.
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Zhang, Miao, and Guo Wei. "An Instantaneous Correlation Coefficient and Simplified Coherent Averaging Method for Single-Channel Foetal ECG Extraction." Applied Sciences 10, no. 16 (August 14, 2020): 5634. http://dx.doi.org/10.3390/app10165634.

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In this paper, an instantaneous correlation coefficient and simplified coherent averaging method for single-channel foetal ECG (FECG) extraction is proposed. The instantaneous correlation coefficient is used to determine the position of the R peak of the measured ECG signal, and the simplified coherent averaging method is used to extract the main information of the ECG signal. The loss of the nonlinear and nonstationary characteristics by coherent averaging is recovered by threshold processing of the residual signal. The FECG signal extraction is performed in three steps. In the first step, the main information of the maternal electrocardiogram (MECG) is extracted from the abdomen electrocardiogram (AECG) signal by means of the instantaneous correlation coefficient and simplified coherent averaging method, and then the noisy FECG is obtained by subtracting the MECG obtained by simplified coherent averaging from the AECG. The second step is to extract the main information of the FECG by applying the instantaneous correlation coefficient and simplified coherent averaging method to the noisy FECG. The remaining signal is obtained by subtracting the simplified coherent averaging FECG from the noisy FECG. Thirdly, the threshold method is utilised to remove MECG residual noise and random gross value noise from the remaining signal to extract the nonlinear and nonstationary information, and the final FECG extraction is obtained by adding the nonlinear and nonstationary information to the simplified coherent averaging FECG. The validity of the proposed method is verified by experiments using synthetic data and real database data. FECG extracted by the method has the advantages of clear QRS complex wave, reasonable enhancement of P wave and T wave morphology, and no loss of nonlinear and nonstationary characteristics.
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Mohsen Alkanfery, Hadi, and Ibrahim Mustafa Mehedi. "Fractional Order Butterworth Filter for Fetal Electrocardiographic Signal Feature Extraction." Signal & Image Processing : An International Journal 12, no. 05 (October 31, 2021): 45–56. http://dx.doi.org/10.5121/sipij.2021.12503.

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The non-invasive Fetal Electrocardiogram (FECG) signal has become a significant method for monitoring the fetus's physiological conditions, extracted from the Abdominal Electrocardiogram (AECG) during pregnancy. The current techniques are limited during delivery for detecting and analyzing fECG. The non - intrusive fECG recorded from the mother's abdomen is contaminated by a variety of noise sources, can be a more challenging task for removing the maternal ECG. These contaminated noises have become a major challenge during the extraction of fetal ECG is managed by uni-modal technique. In this research, a new method based on the combination of Wavelet Transform (WT) and Fast Independent Component Analysis (FICA) algorithm approach to extract fECG from AECG recordings of the pregnant woman is proposed. Initially, preprocessing of a signal is done by applying a Fractional Order Butterworth Filter (FBWF). To select the Direct ECG signal which is characterized as a reference signal and the abdominal signal which is characterized as an input signal to the WT, the cross-correlation technique is used to find the signal with greater similarity among the available four abdominal signals. The model performance of the proposed method shows the most frequent similarity of fetal heartbeat rate present in the database can be evaluated through MAE and MAPE is 0.6 and 0.041209 respectively. Thus the proposed methodology of de-noising and separation of fECG signals will act as the predominant one and assist in understanding the nature of the delivery on further analysis.
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Sarafan, Sadaf, Tai Le, Michael P. H. Lau, Afshan Hameed, Tadesse Ghirmai, and Hung Cao. "Fetal Electrocardiogram Extraction from the Mother’s Abdominal Signal Using the Ensemble Kalman Filter." Sensors 22, no. 7 (April 5, 2022): 2788. http://dx.doi.org/10.3390/s22072788.

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Fetal electrocardiogram (fECG) assessment is essential throughout pregnancy to monitor the wellbeing and development of the fetus, and to possibly diagnose potential congenital heart defects. Due to the high noise incorporated in the abdominal ECG (aECG) signals, the extraction of fECG has been challenging. And it is even a lot more difficult for fECG extraction if only one channel of aECG is provided, i.e., in a compact patch device. In this paper, we propose a novel algorithm based on the Ensemble Kalman filter (EnKF) for non-invasive fECG extraction from a single-channel aECG signal. To assess the performance of the proposed algorithm, we used our own clinical data, obtained from a pilot study with 10 subjects each of 20 min recording, and data from the PhysioNet 2013 Challenge bank with labeled QRS complex annotations. The proposed methodology shows the average positive predictive value (PPV) of 97.59%, sensitivity (SE) of 96.91%, and F1-score of 97.25% from the PhysioNet 2013 Challenge bank. Our results also indicate that the proposed algorithm is reliable and effective, and it outperforms the recently proposed extended Kalman filter (EKF) based algorithm.
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Dissertations / Theses on the topic "FECG EXTRACTION"

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VIMAL, MANOJ KUMAR. "FECG EXTRACTION USING VARIOUS LMS ALGORITHMS." Thesis, 2018. http://dspace.dtu.ac.in:8080/jspui/handle/repository/16315.

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During pregnancy, it is very important to know the foetal development condition so that if there is any problem in development of foetal, it can be treated before creation of any critical condition. Foetal development & health status can be acknowledged through various methods such as ultrasound etc., but foetal ECG plays an important role in providing important information about the health status of the baby during labor condition. Doctors always perform foetal ECG extraction during labor to know if any disease is developing in the foetal & if there is any problem, then they try to diagnose the problem accordingly. Foetal ECG extraction is the process of separating the baby’s heartbeat signal from mother’s heartbeat signal. Various methods have been developed for the extraction of the foetal ECG signal such as Principal Component Analysis (PCA), Blind Source Separation (Independent Component Analysis (ICA)), Wavelet method etc. Adaptive filtering is one the most popular method used for the separation of foetal ECG signal. Adaptive filtering is the method which generates an error signal corresponding to the desired output signal. Adaptive filters are based upon adaptive algorithm. Adaptive algorithms are designed in such a way that it always tries to minimize the amplitude of error signal by changing the filter coefficient values in an iterative manner. Least Mean Square is the standard adaptive algorithm which tries to minimize its cost function value. The cost function for LMS algorithm is the square of the difference v | P a g e between the desired signal & the obtained output signal. In this project, we are taking pure foetal ECG signal which is our desired signal & the obtained foetal ECG signal. In this thesis, some improvements have been implemented in standard LMS algorithm. L1 norm penalty has been applied on the LMS cost function to generate a new algorithm named as Zero Attracting Least Mean Square (ZALMS). As the name suggests “Zero Attracting”, this algorithm tries to make the weights of the filter equal to zero as much as possible. Higher the number of the coefficients equal to zero, higher is the sparsity of the system & this higher sparsity helps in decreasing the error signal results in increasing the performance rate. Also an adaptive algorithm named as Normalized Least Mean Square (NLMS) is implemented for the extraction of the foetal ECG signal. Both ZALMS & NLMS provide better results in terms of signal to noise ratio and convergence speed in comparison to standard LMS algorithm.
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Book chapters on the topic "FECG EXTRACTION"

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Sharma, Yojana, Shashwati Ray, and Om Prakash Yadav. "FECG Extraction Using 1D Convolution Neural Network." In Algorithms for Intelligent Systems, 331–38. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-1295-4_34.

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John, Rolant Gini, Ponmozhy Deepan Chakravarthy, K. I. Ramachandran, and Pooja Anand. "Modeling of a System for fECG Extraction from abdECG." In Advances in Intelligent Systems and Computing, 568–79. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-76348-4_55.

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Dr Prasad, D. V., and R. Swarnalatha. "A New Method of Extraction of FECG from Abdominal Signal." In IFMBE Proceedings, 98–100. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-540-92841-6_24.

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Joseph, Jeffy, J. Rolant Gini, and K. I. Ramachandran. "Removal of BW and Respiration Noise in abdECG for fECG Extraction." In Advances in Intelligent Systems and Computing, 3–14. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-67934-1_1.

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Dembrani, Mahesh B., K. B. Khanchandani, and Anita Zurani. "Extraction of FECG Signal Based on Blind Source Separation Using Principal Component Analysis." In Advances in Intelligent Systems and Computing, 173–80. Singapore: Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-3373-5_17.

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"Extracting Revolutionary Spirit." In From Ah Q to Lei Feng, 155–96. Stanford University Press, 2008. http://dx.doi.org/10.11126/stanford/9780804700757.003.0006.

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"Extracting Revolutionary Spirit:." In From Ah Q to Lei Feng, 155–96. Stanford University Press, 2008. http://dx.doi.org/10.2307/j.ctvqr1f3h.10.

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"Five. Extracting Revolutionary Spirit." In From Ah Q to Lei Feng, 155–96. Stanford University Press, 2020. http://dx.doi.org/10.1515/9780804769822-008.

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Conference papers on the topic "FECG EXTRACTION"

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Murawwat, Sadia, Anisa Batool, Ayesha Ahmed, Anum Ansar, and Anam Iqbal. "FECG Extraction Using Adaptive LMS." In 2018 International Conference on Computing, Electronic and Electrical Engineering (ICE Cube). IEEE, 2018. http://dx.doi.org/10.1109/icecube.2018.8610961.

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Li, Yunxia, and Hui Zhao. "A new method for FECG extraction." In 2013 International Conference on Communications, Circuits and Systems (ICCCAS). IEEE, 2013. http://dx.doi.org/10.1109/icccas.2013.6765300.

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Shao, Wenting, Bin Fang, Pu Wang, and Mingrong Ren. "FECG Extraction Based On BSS Of Sparse Signal." In 2008 2nd International Conference on Bioinformatics and Biomedical Engineering (ICBBE '08). IEEE, 2008. http://dx.doi.org/10.1109/icbbe.2008.693.

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Ma, Ming, Yu-Lin Yang, and San-Ya Lei. "Blind Extraction of FECG Combining Periodicity and Kurtosis." In 2009 3rd International Conference on Bioinformatics and Biomedical Engineering (iCBBE 2009). IEEE, 2009. http://dx.doi.org/10.1109/icbbe.2009.5162460.

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Sugumar, D., Supriya Prashant Diwan, Hemant P. Chavan, Mahesh H. Khedkar, Sandipkumar S. Bhandare, and Pravin Patil. "Extraction of FECG signal to detect preterm delivery." In PROCEEDING OF INTERNATIONAL CONFERENCE ON ENERGY, MANUFACTURE, ADVANCED MATERIAL AND MECHATRONICS 2021. AIP Publishing, 2023. http://dx.doi.org/10.1063/5.0126166.

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Lihua Liu and Xiaozhi Liu. "The methods of BSS used for extraction of FECG." In 2008 7th World Congress on Intelligent Control and Automation. IEEE, 2008. http://dx.doi.org/10.1109/wcica.2008.4593743.

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Nie, Wei, Wei Lv, and Yibing Li. "A new FECG extraction method based on improved independent component analysis." In 2016 IEEE 13th International Conference on Signal Processing (ICSP). IEEE, 2016. http://dx.doi.org/10.1109/icsp.2016.7878058.

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Li, Chaolan, Bin Fang, Huijie Li, and Pu Wang. "A novel method of FECG extraction combined self-correlation analysis with ICA." In 2016 8th IEEE International Conference on Communication Software and Networks (ICCSN). IEEE, 2016. http://dx.doi.org/10.1109/iccsn.2016.7586629.

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Taralunga, Dragos, Mihaela Ungureanu, Rodica Strungaru, and Werner Wolf. "Performance comparison of four ICA algorithms applied for fECG extraction from transabdominal recordings." In 2011 10th International Symposium on Signals, Circuits and Systems (ISSCS). IEEE, 2011. http://dx.doi.org/10.1109/isscs.2011.5978768.

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Hasan, Muhammad A., Muhammad I. Ibrahimy, Mamun B. I. Reaz, Md J. Uddin, and Mohammed S. Hussain. "VHDL modeling of FECG extraction from the composite abdominal ECG using Atificial Intelligence." In 2009 IEEE International Conference on Industrial Technology - (ICIT). IEEE, 2009. http://dx.doi.org/10.1109/icit.2009.4939637.

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