Journal articles: '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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Mertes, Gert, Yuan Long, Zhangdaihong Liu, Yuhui Li, Yang Yang, and David A. Clifton. "A Deep Learning Approach for the Assessment of Signal Quality of Non-Invasive Foetal Electrocardiography." Sensors 22, no. 9 (April 26, 2022): 3303. http://dx.doi.org/10.3390/s22093303.

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Non-invasive foetal electrocardiography (NI-FECG) has become an important prenatal monitoring method in the hospital. However, due to its susceptibility to non-stationary noise sources and lack of robust extraction methods, the capture of high-quality NI-FECG remains a challenge. Recording waveforms of sufficient quality for clinical use typically requires human visual inspection of each recording. A Signal Quality Index (SQI) can help to automate this task but, contrary to adult ECG, work on SQIs for NI-FECG is sparse. In this paper, a multi-channel signal quality classifier for NI-FECG waveforms is presented. The model can be used during the capture of NI-FECG to assist technicians to record high-quality waveforms, which is currently a labour-intensive task. A Convolutional Neural Network (CNN) is trained to distinguish between NI-FECG segments of high and low quality. NI-FECG recordings with one maternal channel and three abdominal channels were collected from 100 subjects during a routine hospital screening (102.6 min of data). The model achieves an average 10-fold cross-validated AUC of 0.95 ± 0.02. The results show that the model can reliably assess the FECG signal quality on our dataset. The proposed model can improve the automated capture and analysis of NI-FECG as well as reduce technician labour time.

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Sivaraman, Himani. "Application of SVM Algorithm for Fetal ECG Extraction from a Single Maternal Abdominal Record." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 10, no. 1 (April 10, 2019): 638–44. http://dx.doi.org/10.17762/turcomat.v10i1.13560.

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The electrical activity of the foetal cardiac muscles is known as foetal ECG (FECG), and it can provide crucial details on the health of the fetus's heart. A pregnant woman's belly can be used to non-invasively capture this signal during pregnancy. However, since additional sources of noise, including the maternal ECG generally overpower the FECG recording, it would be ineffective. However, a clean FECG may be retrieved from the abdominal recording if it is correctly processed, and FECG can be used to evaluate the functioning of the foetal heart. In order to extract foetal electrocardiogram (ECG) from a single abdomen record, a unique two-tier approach is presented in this work. The abdominal signal is processed through a smoothing filter in the first layer of the proposed approach in order to determine the maternal ECG's estimated value. Findings on synthetic and actual abdominal ECG data demonstrate that the intended technique can extract foetal ECG with signal quality equivalent or superior to that retrieved by multichannel based mechanisms. The anticipated maternal ECG is then nonlinearly matched with the abdominal signal through polynomial networks.

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Praneeth, CH N. V. S., Jaba Deva Krupa Abel, Dhanalakshmi Samiappan, R. Kumar, S. Pravin Kumar, and Patnala Venkat Nitin. "A COMPARISON ON VARIANTS OF LMS USED IN FIR ADAPTIVE NOISE CANCELLERS FOR FETAL ECG EXTRACTION." Biomedical Engineering: Applications, Basis and Communications 32, no. 04 (July 29, 2020): 2050026. http://dx.doi.org/10.4015/s101623722050026x.

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Fetal electrocardiogram (FECG) non-invasively obtained through abdominal recordings serves as a promising diagnostic tool for fetal health monitoring during pregnancy. However, in the abdominal ECG (AECG) signal, FECG overlaps with maternal ECG (MECG) in both temporal and spectral domains in addition to interference from various sources like electromyogram, electrogastrogram, motion artifacts and other noises. The objective of this paper is to eliminate MECG components from AECG signal to extract FECG signal through FIR adaptive noise canceller (ANC) with filter coefficients updated using adaptive algorithms. Adaptive filters are suitable for current problem of interest and Least Mean Square (LMS) and its variants are analyzed for the problem of FECG extraction. We have compared the four variants of LMS such as normalized LMS (NLMS), sign-error algorithm, least mean fourth (LMF) algorithms for FECG extraction. The algorithms are evaluated using real-time abdominal ECG recordings acquired from daisy database. The performance of each algorithm is evaluated using various parameters like sensitivity, accuracy, positive predictive values and [Formula: see text] score. Further, the convergence rate for different algorithms are plotted and analyzed. From the simulation results, it is observed that the LMF algorithm outperforms its counterparts by providing an accuracy and positive predictive value of 73.3%, sensitivity of 100% and [Formula: see text] measure of 84.5%. The convergence plots obtained justify that LMF algorithm has a faster convergence rate compared to the other variants of LMS.

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Jallouli, Malika, Sabrine Arfaoui, Anouar Ben Mabrouk, and Carlo Cattani. "Clifford Wavelet Entropy for Fetal ECG Extraction." Entropy 23, no. 7 (June 30, 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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Al-Sheikh, Bahaa, Mohammad Shukri Salman, Alaa Eleyan, and Shadi Alboon. "Non-invasive fetal ECG extraction using discrete wavelet transform recursive inverse adaptive algorithm." Technology and Health Care 28, no. 5 (September 18, 2020): 507–20. http://dx.doi.org/10.3233/thc-191948.

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BACKGROUND: Fetal heart activity adds significant information about the status of the fetus health. Early diagnosis of issues in the heart before delivery allows early intervention and significantly improves the treatment. OBJECTIVE: This paper presents a new adaptive filtering algorithm for fetal electrocardiogram (FECG) extraction from the maternal abdominal signal, known in literature as abdominal electrocardiogram (AECG) signal. Fetal QRS complex waves will be identified and extracted accurately for fetal health care and monitoring purposes. METHODS: We use discrete wavelet transform recursive inverse (DWT-RI) adaptive filtering algorithm for this objective. Thoracic maternal electrocardiogram (MECG) is used as a reference in the proposed algorithm and FECG components are extracted from AECG signal after suppressing the MECG projections. The proposed algorithm is compared to other typical adaptive filtering algorithms, least mean squares (LMS), recursive least squares (RLS), and recursive inverse (RI). RESULTS: Fetal QRS waveforms successful identification and extraction from AECG signal is evaluated objectively and visually and compared to other algorithms. We validated the proposed algorithm using both synthetic data and real clinical data. CONCLUSIONS: The proposed algorithm is capable of extracting fetal QRS waveforms successfully from AECG and outperforms other adaptive filtering algorithms in terms of accuracy and positive predictivity.

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Jia, Yanfei, and Xiaodong Yang. "A Fetal Electrocardiogram Signal Extraction Algorithm Based on Fast One-Unit Independent Component Analysis with Reference." Computational and Mathematical Methods in Medicine 2016 (2016): 1–10. http://dx.doi.org/10.1155/2016/5127978.

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Fetal electrocardiogram (FECG) extraction is very important procedure for fetal health assessment. In this article, we propose a fast one-unit independent component analysis with reference (ICA-R) that is suitable to extract the FECG. Most previous ICA-R algorithms only focused on how to optimize the cost function of the ICA-R and payed little attention to the improvement of cost function. They did not fully take advantage of the prior information about the desired signal to improve the ICA-R. In this paper, we first use the kurtosis information of the desired FECG signal to simplify the non-Gaussian measurement function and then construct a new cost function by directly using a nonquadratic function of the extracted signal to measure its non-Gaussianity. The new cost function does not involve the computation of the difference between the function of the Gaussian random vector and that of the extracted signal, which is time consuming. Centering and whitening are also used to preprocess the observed signal to further reduce the computation complexity. While the proposed method has the same error performance as other improved one-unit ICA-R methods, it actually has lower computation complexity than those other methods. Simulations are performed separately on artificial and real-world electrocardiogram signals.

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Sarafan, Sadaf, Tai Le, Amir Mohammad Naderi, Quoc-Dinh Nguyen, Brandon Tiang-Yu Kuo, Tadesse Ghirmai, Huy-Dung Han, Michael P. H. Lau, and Hung Cao. "Investigation of Methods to Extract Fetal Electrocardiogram from the Mother’s Abdominal Signal in Practical Scenarios." Technologies 8, no. 2 (June 5, 2020): 33. http://dx.doi.org/10.3390/technologies8020033.

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Monitoring of fetal electrocardiogram (fECG) would provide useful information about fetal wellbeing as well as any abnormal development during pregnancy. Recent advances in flexible electronics and wearable technologies have enabled compact devices to acquire personal physiological signals in the home setting, including those of expectant mothers. However, the high noise level in the daily life renders long-entrenched challenges to extract fECG from the combined fetal/maternal ECG signal recorded in the abdominal area of the mother. Thus, an efficient fECG extraction scheme is a dire need. In this work, we intensively explored various extraction algorithms, including template subtraction (TS), independent component analysis (ICA), and extended Kalman filter (EKF) using the data from the PhysioNet 2013 Challenge. Furthermore, the modified data with Gaussian and motion noise added, mimicking a practical scenario, were utilized to examine the performance of algorithms. Finally, we combined different algorithms together, yielding promising results, with the best performance in the F1 score of 92.61% achieved by an algorithm combining ICA and TS. With the data modified by adding different types of noise, the combination of ICA–TS–ICA showed the highest F1 score of 85.4%. It should be noted that these combined approaches required higher computational complexity, including execution time and allocated memory compared with other methods. Owing to comprehensive examination through various evaluation metrics in different extraction algorithms, this study provides insights into the implementation and operation of state-of-the-art fetal and maternal monitoring systems in the era of mobile health.

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Awan, Muhammad Tayyib, Muhammad Amir, Sarmad Maqsood, Musyyab Yousufi, Suheel Abdullah, and MUHAMMAD IRFAN. "Modified Block Compressed Sensing for Extraction of Fetal Electrocardiogram from Mother Electrocardiogram Using Block Compressed Sensing Based Guided FOCUSS and FAST-Independent Component." Information Technology and Control 50, no. 1 (March 25, 2021): 123–37. http://dx.doi.org/10.5755/j01.itc.50.1.24145.

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Fetal ECG extraction from abdominal ECG is critical task for telemonitoring of fetus which require lot of understanding to the subject. Conventional source separation methods are not efficient enough to separate FECG from huge multichannel ECG. Thus use of compression technique is needed to compress and reconstruct ECG signal without any significant losses in quality of signal. Compressed sensing shows promising results for such tasks. However, current compressed sensing theory is not so far that successful due to the non-sparsity and strong noise contamination present in ECG signal. The proposed work explores the concept of block compressed sensing to reconstruct non-sparse FECG signal using GFOCUSS algorithm. The main objective of this paper is not only to successfully reconstruct the ECG signal but to efficiently separate FECG from abdominal ECG. The proposed algorithm is explained in very extensive manner for all experiments. The key feature of proposed method is, that it doesn’t affect the interdependence relation between multichannel ECG. The useof walsh sensing matrix made it possible to achieve high compression ratio. Experimental results shows that even at very high compression ratio, successful FECG reconstruction from raw ECG is possible. These results are validated using PSNR, SINR, and MSE. This shows the framework, compared to other algorithms such as current blocking CS algorithms, rackness CS algorithm and wavelet algorithms, can greatly reduce code execution time during data compression stage and achieve better reconstruction in terms of MSE, PSNR and SINR.

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Ţarălungă, Dragoş-Daniel, Georgeta-Mihaela Ungureanu, Ilinca Gussi, Rodica Strungaru, and Werner Wolf. "Fetal ECG Extraction from Abdominal Signals: A Review on Suppression of Fundamental Power Line Interference Component and Its Harmonics." Computational and Mathematical Methods in Medicine 2014 (2014): 1–15. http://dx.doi.org/10.1155/2014/239060.

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Interference of power line (PLI) (fundamental frequency and its harmonics) is usually present in biopotential measurements. Despite all countermeasures, the PLI still corrupts physiological signals, for example, electromyograms (EMG), electroencephalograms (EEG), and electrocardiograms (ECG). When analyzing the fetal ECG (fECG) recorded on the maternal abdomen, the PLI represents a particular strong noise component, being sometimes 10 times greater than the fECG signal, and thus impairing the extraction of any useful information regarding the fetal health state. Many signal processing methods for cancelling the PLI from biopotentials are available in the literature. In this review study, six different principles are analyzed and discussed, and their performance is evaluated on simulated data (three different scenarios), based on five quantitative performance indices.

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Haq, Tashreque Mohammed, Safkat Arefin, Shamiur Rahman, and Tanzilur Rahman. "Extraction of Fetal Heart Rate from Maternal ECG—Non Invasive Approach for Continuous Monitoring during Labor." Proceedings 2, no. 13 (December 19, 2018): 1009. http://dx.doi.org/10.3390/proceedings2131009.

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Here, we propose a signal processing based approach for the extraction of the fetal heart rate (FHR) from Maternal Abdominal ECG (MAECG) in a non-invasive way. Datasets from a Physionet database has been used in this study for evaluating the performance of the proposed model that performs three major tasks; preprocessing of the MAECG signal, separation of Fetal QRS complexes from that of maternal and estimation of Fetal R peak positions. The MAECG signal is first preprocessed with improved multistep filtering techniques to detect the Maternal QRS (MQRS) complexes, which are dominant in the MAECG. A reference template is then reconstructed based on MQRS locations and removed from the preprocessed signal resulting in the raw FECG. This extracted FECG is further corrected and enhanced before obtaining the Fetal R peaks. The detection of FQRS and calculation of FHR has been compared against the reference Fetal Scalp ECG. Results indicate that the approach achieved good accuracy.

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Cao, Shi, Hui Xiao, Gao Gong, Weiyang Fang, and Chaomin Chen. "Morphology extraction of fetal ECG using temporal CNN-based nonlinear adaptive noise cancelling." PLOS ONE 17, no. 12 (December 15, 2022): e0278917. http://dx.doi.org/10.1371/journal.pone.0278917.

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Objectives Noninvasive fetal electrocardiography (FECG) offers many advantages over alternative fetal monitoring techniques in evaluating fetal health conditions. However, it is difficult to extract a clean FECG signal with morphological features from an abdominal ECG recorded at the maternal abdomen; the signal is usually contaminated by the maternal ECG and various noises. The aim of the work is to extract an FECG signal that preserves the morphological features from the mother’s abdominal ECG recording, which allows for accurately estimating the fetal heart rate (FHR) and analyzing the waveforms of the fetal ECG. Methods We propose a novel nonlinear adaptive noise cancelling framework (ANC) based on a temporal convolutional neural network (CNN) to effectively extract fetal ECG signals from mothers’ abdominal ECG recordings. The proposed framework consists of a two-stage network, using the ANC architecture; one network is for the maternal ECG component elimination and the other is for the residual noise component removal of the extracted fetal ECG signal. Then, JADE (one of the blind source separation algorithms) is applied as a postprocessing step to produce a clean fetal ECG signal. Results Synthetic ECG data (FECGSYNDB) and clinical ECG data (NIFECGDB, PCDB) are used to evaluate the extraction performance of the proposed framework. The statistical and visual results demonstrate that our method outperforms the other state-of-the-art algorithms in the literature. Specifically, on the FECGSYNDB, the mean squared error (MSE), signal-to-noise ratio (SNR), correlation coefficient (R) and F1-score of our method are 0.16, 7.94, 0.95 and 98.89%, respectively. The F1-score on the NIFECGDB reaches 98.62%. The value of the F1-score on the PCDB is 98.62%. Conclusion As opposed to the existing algorithms being restricted to fetal QRS complex detection, the proposed framework can preserve the morphological features of the extracted fetal ECG signal well, which could support medical diagnoses based on the morphology of the fetal ECG signal.

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Ren, Dongxiao, Mao Ye, Ying Yin, and Yuanxiang Zhu. "FECG extraction from nonlinear mixture based on minimization of mutual information." JOURNAL OF ELECTRONIC MEASUREMENT AND INSTRUMENT 24, no. 7 (August 3, 2010): 680–85. http://dx.doi.org/10.3724/sp.j.1187.2010.00680.

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Vo, Khuong, Tai Le, Amir M. Rahmani, Nikil Dutt, and Hung Cao. "An Efficient and Robust Deep Learning Method with 1-D Octave Convolution to Extract Fetal Electrocardiogram." Sensors 20, no. 13 (July 4, 2020): 3757. http://dx.doi.org/10.3390/s20133757.

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The invasive method of fetal electrocardiogram (fECG) monitoring is widely used with electrodes directly attached to the fetal scalp. There are potential risks such as infection and, thus, it is usually carried out during labor in rare cases. Recent advances in electronics and technologies have enabled fECG monitoring from the early stages of pregnancy through fECG extraction from the combined fetal/maternal ECG (f/mECG) signal recorded non-invasively in the abdominal area of the mother. However, cumbersome algorithms that require the reference maternal ECG as well as heavy feature crafting makes out-of-clinics fECG monitoring in daily life not yet feasible. To address these challenges, we proposed a pure end-to-end deep learning model to detect fetal QRS complexes (i.e., the main spikes observed on a fetal ECG waveform). Additionally, the model has the residual network (ResNet) architecture that adopts the novel 1-D octave convolution (OctConv) for learning multiple temporal frequency features, which in turn reduce memory and computational cost. Importantly, the model is capable of highlighting the contribution of regions that are more prominent for the detection. To evaluate our approach, data from the PhysioNet 2013 Challenge with labeled QRS complex annotations were used in the original form, and the data were then modified with Gaussian and motion noise, mimicking real-world scenarios. The model can achieve a F1 score of 91.1% while being able to save more than 50% computing cost with less than 2% performance degradation, demonstrating the effectiveness of our method.

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Yao, Wen Po, Jun Chang Zhao, Zheng Zhong Zheng, Tie Bing Liu, Hong Xing Liu, and Jun Wang. "Fetal Electrocardiogram Extraction Based on Modified Robust Independent Component Analysis." Advanced Materials Research 749 (August 2013): 250–53. http://dx.doi.org/10.4028/www.scientific.net/amr.749.250.

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Fetal electrocardiogram (FECG) separation gets widely attention due to its clinical significance. In the paper, we proposed an improved robust independent component analysis for fetal ECG separation. Firstly, wavelet decomposition was applied to fetal ECG to get the relevant parameters. Then, the RobustICA was used to separate the mixed signals. Compared to robust independent component analysis, computing speed of the improved algorithm increased by an average of 15 percent while minimum mean square error fluctuations 0.0008, which indicated that this algorithm could be effectively used in clinical fetal ECG monitoring.

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BAŞPINAR, Ulvi, and Yasemin KÖYLÜ. "DETECTION OF FETAL ELECTROCARDIOGRAM SIGNALS FROM MATERNAL ABDOMINAL ECG RECORDINGS." Journal of Scientific Reports-A, no. 052 (March 29, 2023): 266–78. http://dx.doi.org/10.59313/jsr-a.1173530.

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Fetal electrocardiogram (fECG) is a signal that contains vital information about the health of the fetus throughout pregnancy. During pregnancy, it is important to monitor and analyse this signal because it represents the electrical activity of the developing fetal heart. Early detection of fetal ECG problems during the fetus' development is crucial because it allows early treatment and provides knowledge about diseases that may emerge at a later time. Extraction of fetal ECG from the abdomen ECG signal is valuable in these aspects. In order to extract the fetal ECG from the recorded abdomen ECG signals correctly, it must be handled appropriately. It could be challenging to separate the fetal ECG signal from other physiological artifacts and noises in the mother abdominal signal. In this study, signal processing techniques were used to separate the fetus ECG signal from real abdominal ECG recordings. These methods include Ensemble Empirical Based Denoising, Finite Impulse Response Filter, Independent Component Analysis, and Pan & Tompkins approach. The results show that utilizing only the ICA technique to extract fECG signals is insufficient and that additional algorithms, such as those indicated above, should be used together. The mECG and fECG signals can be successfully extracted using the suggested approach.

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Meddour, Cherif, and Malika Kedir-Talha. "NEW METHOD EXPLOITING A HYBRID TECHNIQUES FOR FETAL CARDIAC SIGNAL EXTRACTION." Biomedical Engineering: Applications, Basis and Communications 31, no. 04 (June 27, 2019): 1950027. http://dx.doi.org/10.4015/s1016237219500273.

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According to WHO, 2.6 million babies die during pregnancy. Good monitoring during the prenatal period could provide a significant reduction of this mortality rate. This is possible by detection and extraction of the fetal electrocardiogram (FECG). Extraction of that information is complex due to other noise coming from the mother and within the fetus that drowns out the fetal heart signal. However, new technology and improved filtering technique have provided ways to more accurately and efficiently gather various electrical components regarding fetal heart condition. In this paper, we propose a new source separation filtering method exploiting linear and nonlinear filtering techniques. Our method is a non-invasive extraction technique, where the source signal is the cardiac electrical signal acquired by non-invasive electrodes to facilitate the collection of signals and reduce the cost of the acquisition system; it differs from other existing methods in minimizing the number of input signals and the simplicity of its implementation. The fetal heart signal is drowned out by the maternal electrocardiogram (MECG). The problem that arises is the exact knowledge of the MECG signal affecting the chosen measuring electrode, since the MECG is dependent on the position of the electrode and the type of tissue that goes through. Therefore, its knowledge can be made only by a mathematical estimation. A DWT decomposition with adaptive thresholding based on an LMS filter is applied to extract the fetal signal. So first we extract the QRS complex of the FECG and detect the fetal heart rate (FHR).

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Swarnalath, R., and D. V. Prasad. "A Novel Technique for Extraction of FECG using Multi Stage Adaptive Filtering." Journal of Applied Sciences 10, no. 4 (February 1, 2010): 319–24. http://dx.doi.org/10.3923/jas.2010.319.324.

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Vasudeva, Bhavya, Puneesh Deora, Pradhan Mohan Pradhan, and Sudeb Dasgupta. "Efficient implementation of LMS adaptive filter-based FECG extraction on an FPGA." Healthcare Technology Letters 7, no. 5 (October 1, 2020): 125–31. http://dx.doi.org/10.1049/htl.2020.0016.

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Yu, Wei, Qiang Han, Jing Jing Ma, and Pei Xie. "A New Method for Biomedical Signal Processing with EMD and ICA Approach." Advanced Materials Research 546-547 (July 2012): 548–52. http://dx.doi.org/10.4028/www.scientific.net/amr.546-547.548.

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Faint signal extraction is always a difficult issue in biomedical signal processing field, because the desired signal is often submerged in several relatively large signals or noises. A novel faint signal processing method based on Empirical Mode Decomposition (EMD) and Independent Component Analysis (ICA) is developed to enhance the sensitivity and reliability of faint signal detection. This novel method includes two major steps, which is, firstly the decomposition of the biomedical composite signal using EMD, then the classification or extraction of the desired faint signal component through ICA. This paper explored the working principles and the performance of this novel signal processing method under the specific biomedical environment of fetal electrocardiogram extraction (FECG). The experimental results show that the proposed method has better extraction effect and quality compared with traditional ICA methods.

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Zhang, Yuwei, Aihua Gu, Zhijun Xiao, Yantao Xing, Chenxi Yang, Jianqing Li, and Chengyu Liu. "Wearable Fetal ECG Monitoring System from Abdominal Electrocardiography Recording." Biosensors 12, no. 7 (June 30, 2022): 475. http://dx.doi.org/10.3390/bios12070475.

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Fetal electrocardiography (ECG) monitoring during pregnancy can provide crucial information for assessing the fetus’s health status and making timely decisions. This paper proposes a portable ECG monitoring system to record the abdominal ECG (AECG) of the pregnant woman, comprising both maternal ECG (MECG) and fetal ECG (FECG), which could be applied to fetal heart rate (FHR) monitoring at the home setting. The ECG monitoring system is based on data acquisition circuits, data transmission module, and signal analysis platform, which consists of low input-referred noise, high input impedance, and high resolution. The combination of the adaptive dual threshold (ADT) and the independent component analysis (ICA) algorithm is employed to extract the FECG from the AECG signals. To validate the performance of the proposed system, AECG is recorded and analyzed of pregnant women in three different postures (supine, seated, and standing). The result shows that the proposed system can record the AECG in different postures with good signal quality and high accuracy in fetal ECG and heart rate information. Sensitivity (Se), positive predictive accuracy (PPV), accuracy (ACC), and their harmonic mean (F1) are utilized as the metrics to evaluate the performance of the fetal QRS (fQRS) complexes extraction. The average Se, PPV, ACC, and F1 score are 99.62%, 97.90%, 97.40%, and 98.66% for the fQRS complexes extraction,, respectively. This paper shows the proposed system has a promising application in fetal health monitoring.

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Karvounis, E. C., M. G. Tsipouras, C. Papaloukas, D. G. Tsalikakis, K. K. Naka, and D. I. Fotiadis. "A Non-invasive Methodology for Fetal Monitoring during Pregnancy." Methods of Information in Medicine 49, no. 03 (2010): 238–53. http://dx.doi.org/10.3414/me09-01-0041.

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Summary Objectives: This paper describes a methodology for the monitoring of the fetal cardiac health status during pregnancy, through the effective and non-invasive monitoring of the abdominal ECG signals (abdECG) of the mother. Methods: For this purpose, a three-stage methodology has been developed. In the first stage, the fetal heart rate (fHR) is extracted from the abdECG signals, using nonlinear analysis. Also, the eliminated ECG (eECG) is calculated, which is the abdECG after the maternal QRSs elimination. In the second stage, a blind source separation technique is applied to the eECG signals and the fetal ECG (fECG) is obtained. Finally, monitoring of the fetus is implemented using features extracted from the fHR and f ECG, such as the T/QRS ratio and the characterization of the fetal ST waveforms. Results: The methodology is evaluated using a dataset of simulated multichannel abdECG signals: 94.79% accuracy for fHR extraction, 92.49% accuracy in T/QRS ratio calculation and 79.87% in ST waveform classification. Conclusions: The novel non-invasive proposed methodology is advantageous since it offers automated identification of fHR and fECG and automated ST waveform analysis, exhibiting a high diagnostic accuracy.

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Kahankova, Radana, Martina Mikolasova, and Radek Martinek. "Optimization of adaptive filter control parameters for non-invasive fetal electrocardiogram extraction." PLOS ONE 17, no. 4 (April 11, 2022): e0266807. http://dx.doi.org/10.1371/journal.pone.0266807.

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This paper is focused on the design, implementation and verification of a novel method for the optimization of the control parameters of different hybrid systems used for non-invasive fetal electrocardiogram (fECG) extraction. The tested hybrid systems consist of two different blocks, first for maternal component estimation and second, so-called adaptive block, for maternal component suppression by means of an adaptive algorithm (AA). Herein, we tested and optimized four different AAs: Adaptive Linear Neuron (ADALINE), Standard Least Mean Squares (LMS), Sign-Error LMS, Standard Recursive Least Squares (RLS), and Fast Transversal Filter (FTF). The main criterion for optimal parameter selection was the F1 parameter. We conducted experiments using real signals from publicly available databases and those acquired by our own measurements. Our optimization method enabled us to find the corresponding optimal settings for individual adaptive block of all tested hybrid systems which improves achieved results. These improvements in turn could lead to a more accurate fetal heart rate monitoring and detection of fetal hypoxia. Consequently, our approach could offer the potential to be used in clinical practice to find optimal adaptive filter settings for extracting high quality fetal ECG signals for further processing and analysis, opening new diagnostic possibilities of non-invasive fetal electrocardiography.

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Putra, Darma Setiawan, and Yuril Umbu WW. "Feature Extraction of Facial Electromyograph (EMG) Signal for Aceh Languages Speech using Discrete Wavelet Transform (DWT)." Jurnal Inotera 4, no. 1 (July 10, 2019): 31. http://dx.doi.org/10.31572/inotera.vol4.iss1.2019.id73.

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The facial electromyograph (FEMG) signal is a signal that occurs in the muscles of the contracted human face. This FEMG signal is one of the techniques used to study human speech recognition. It can be acquired by placing an electrode surface on the skin around the facial articulation muscle. Three types of muscles in this study are the masseter, risorius and depressor muscle. This study aims to extract and analyze the features in the FEMG signal. The extraction method is the discrete wavelet transform (DWT). The type of wavelet transform is Daubechies2 with level 5. After extraction and analysis of FEMG signals, the FEMG signal pattern for each spoken word indicated by differences in the approximation and detail coefficient of the FEMG signal. In addition, the level of difference in the FEMG signal pattern is also indicated by the histogram of the approximation coefficient of the FEMG signal. Thus, the discrete wavelet transform method can be used as one of the methods for extracting the FEMG signal feature in a human facial electromyograph (FEMG) signal.

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Zhou, Heng Yan, Yu Cong Xu, Yu Xi Luo, and Yu Bao Gao. "Optimizing the Algorithm of FECG Separation from MECG Based on ICA Rationale." Advanced Materials Research 846-847 (November 2013): 1257–61. http://dx.doi.org/10.4028/www.scientific.net/amr.846-847.1257.

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The study presents a method to separate the fetal electrocardiograph (FECG) from concomitant maternal electrocardiograph (MECG) by using Fast Independent component analysis (ICA) algorithm of Blind Signal Separation. Current methods of extracting fetal ECG have defects and drawbacks. Traditional ICA method has a persistent problem that the signal of FECG extracted from MECG was always mixed with the signal of MECG in diverse levels, and the order of MECG and FECG is uncertain, resulting in the decrease of its rate of convergence. To improve the rate of convergence, this research adopts Fast ICA algorithm. Experimental results indicate that this method is useful for extracting the fetal signal of ECG. And a satisfactory signal to noise ratio (SNR) is obtained.

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Keenan, Emerson, Chandan Karmakar, Radhagayathri K. Udhayakumar, Fiona C. Brownfoot, Igor Lakhno, Vyacheslav Shulgin, Joachim A. Behar, and Marimuthu Palaniswami. "Detection of fetal arrhythmias in non-invasive fetal ECG recordings using data-driven entropy profiling." Physiological Measurement 43, no. 2 (February 28, 2022): 025008. http://dx.doi.org/10.1088/1361-6579/ac4e6d.

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Abstract Objective. Fetal arrhythmias are a life-threatening disorder occurring in up to 2% of pregnancies. If identified, many fetal arrhythmias can be effectively treated using anti-arrhythmic therapies. In this paper, we present a novel method of detecting fetal arrhythmias in short length non-invasive fetal electrocardiography (NI-FECG) recordings. Approach. Our method consists of extracting a fetal heart rate time series from each NI-FECG recording and computing an entropy profile using a data-driven range of the entropy tolerance parameter r. To validate our approach, we apply our entropy profiling method to a large clinical data set of 318 NI-FECG recordings. Main Results. We demonstrate that our method (TotalSampEn) provides strong performance for classifying arrhythmic fetuses (AUC of 0.83) and outperforms entropy measures such as SampEn (AUC of 0.68) and FuzzyEn (AUC of 0.72). We also find that NI-FECG recordings incorrectly classified using the investigated entropy measures have significantly lower signal quality, and that excluding recordings of low signal quality (13.5% of recordings) increases the classification performance of TotalSampEn (AUC of 0.90). Significance. The superior performance of our approach enables automated detection of fetal arrhythmias and warrants further investigation in a prospective clinical trial.

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Nezhadali, A., Z. Es’haghi, and A. Khatibi. "Selective extraction of progesterone hormones from environmental and biological samples using a polypyrrole molecularly imprinted polymer and determination by gas chromatography." Analytical Methods 8, no. 8 (2016): 1813–27. http://dx.doi.org/10.1039/c5ay02174j.

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Li, Xiaohua, Wouter Monnens, Zheng Li, Jan Fransaer, and Koen Binnemans. "Solvometallurgical process for extraction of copper from chalcopyrite and other sulfidic ore minerals." Green Chemistry 22, no. 2 (2020): 417–26. http://dx.doi.org/10.1039/c9gc02983d.

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Lee, Man-Seung, Gwang-Seop Lee, and Keun Yong Sohn. "Solvent Extraction Equilibria of FeCl₃ with TBP." MATERIALS TRANSACTIONS 45, no. 6 (2004): 1859–63. http://dx.doi.org/10.2320/matertrans.45.1859.

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Babaee, Shirin, and Ali Daneshfar. "Magnetic deep eutectic solvent-based ultrasound-assisted liquid–liquid microextraction for determination of hexanal and heptanal in edible oils followed by gas chromatography–flame ionization detection." Analytical Methods 10, no. 34 (2018): 4162–69. http://dx.doi.org/10.1039/c8ay01058g.

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Use of a novel magnetic deep eutectic solvent (MDES), consisting of the inexpensive and simple components [choline chloride/para-cresol] [FeCl₄] was examined using ultrasound-assisted liquid–liquid microextraction and back-extraction methods to determine hexanal and heptanal in edible oils.

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Hao, Yun, Ying-juan Hao, Jie Ren, Biao Wu, Xiao-jing Wang, Dishun Zhao, and Fa-tang Li. "Extractive/catalytic oxidative mechanisms over [Hnmp]Cl·xFeCl3 ionic liquids towards the desulfurization of model oils." New Journal of Chemistry 43, no. 20 (2019): 7725–32. http://dx.doi.org/10.1039/c9nj00691e.

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Wang, Yong, Andrey Karasev, and Pär G. Jönsson. "An Investigation of Non-Metallic Inclusions in Different Ferroalloys using Electrolytic Extraction." Metals 9, no. 6 (June 15, 2019): 687. http://dx.doi.org/10.3390/met9060687.

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Ferroalloys are integral constituents of the steelmaking process, since non-metallic inclusions (NMIs) from ferroalloys significantly influence the transformation of inclusions present in liquid steel or they are directly involved in casted steel. In this study, the characteristics of inclusions (such as the number, morphology, size, and composition) in different industrial ferroalloys (FeV, FeMo, FeB, and FeCr) were investigated using the electrolytic extraction (EE) technique. After extraction from the ferroalloy samples and filtration of the solution, the inclusions were investigated on a film filter. The three-dimensional (3D) investigations were conducted using a scanning electron microscopy in combination with energy dispersive spectroscopy (SEM-EDS). The characteristics of inclusions observed in the ferroalloys were compared with previous results and discussed with respect to their possible behaviors in the melt and their effects on the quality of the cast steels. The particle size distributions and floatation distances were plotted for the main inclusion types. The results showed that the most harmful inclusions in the ferroalloys investigated are the following: pure Al2O3 and high Al2O3-containing inclusions in FeV alloys; pure SiO2 and high SiO2-containing inclusions in FeMo alloys; Al2O3 and SiO2-containing inclusions in FeB alloys; and MnO-Cr2O3, Al2O3, and Cr2O3-based inclusions in FeCr alloys.

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Коплак, О. В., В. Л. Сидоров, Е. И. Куницына, Р. А. Валеев, Д. В. Королев, В. П. Пискорский, and Р. Б. Моргунов. "Бистабильное и многодоменное состояния ферромагнитных микропроводов alpha-Fe/(PrDy)(FeCo)B." Физика твердого тела 61, no. 11 (2019): 2090. http://dx.doi.org/10.21883/ftt.2019.11.48412.524.

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Microwire of α-Fe(50 at.%)(PrDy)(FeCo)B(48 at.%) was obtained by extracting a hanging melt drop of (PrDy)(FeCo)B in an electron beam. It was shown that a single microwire with a diameter of 50 μm and a length of 0.8 - 6 mm with an amorphous phase (PrDy)(FeCo)B content of ~ 48% and a polycrystalline α-Fe phase of ~ 52% has a rectangular narrow magnetic hysteresis loop and, accordingly, a bistable state with a switching field of ~ 100 Oe. The shortening of the wire to ~ 0.6 mm leads to a sharp deviation from the squareness of the loop, reducing the slope of the dependence of the magnetization on the field and the coercive force to 20 Oe. In the subsurface layers consisting of the amorphous phase (PrDy)(FeCo)B, oriented areas of reverse magnetization are observed. The role of the magnetic dipole interaction in the formation of a magnetic hysteresis loop of chaotic microwire assemblies of various compositions is discussed.

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Li, Wei Guang, Ke Li Chen, and Hu Hua Liu. "Research on Technology of Gas Drainage in Highly Gassy and Thin Coal Seams with Long Wall Coal Face on the Strike." Advanced Materials Research 807-809 (September 2013): 2450–54. http://dx.doi.org/10.4028/www.scientific.net/amr.807-809.2450.

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Based on the Feng Huang Coalmine 1402# coalface, applying the vertical three zones of overlying strata theory and O-ring theory, this paper discusses the migration paths of pressure-relieved gas in stope and the abundant gas region. By means of optimizing the roof crossing borehole method and high-position borehole in gob method, the gas drainage efficiency and extraction concentration have been improved, the gas emission arising from coalface has been reduced, and the gas accumulation phenomenon has been eliminated, which verifies the accuracy of this research and realizes the safe and efficient gas extraction goal as well.

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Mauro Mirto, Ignazio. "Automatic Extraction of Semantic Roles in Support Verb Constructions." International Journal on Natural Language Computing 10, no. 03 (June 30, 2021): 1–10. http://dx.doi.org/10.5121/ijnlc.2021.10301.

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This paper deals with paraphrastic relations in Italian. In the following sentences: (a) Max strappò delle lacrime a Sara 'Max moved Sara to tears' and (b) Max fece piangere Sara 'Max made Sara cry', the verbs differ syntactically and semantically. Strappare 'tear/rip/wring' is transitive, fare ‘have/make’ is a causative, and piangere 'cry' is intransitive. Despite this, a translation of (a) as (b) is legitimate and therefore (a) is a paraphrase of (b). In theoretical linguistics this raises an issue concerning the relationship between strappare and fare/piangere in Italian, and that in English between move and make. In computational linguistics, can such paraphrases be obtained automatically? Which apparatus should be deployed? The aim of this paper is to suggest a pathway with which to answer these questions.

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Jaros, Rene, Katerina Barnova, Radana Vilimkova Kahankova, Jan Pelisek, Martina Litschmannova, and Radek Martinek. "Independent component analysis algorithms for non-invasive fetal electrocardiography." PLOS ONE 18, no. 6 (June 6, 2023): e0286858. http://dx.doi.org/10.1371/journal.pone.0286858.

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The independent component analysis (ICA) based methods are among the most prevalent techniques used for non-invasive fetal electrocardiogram (NI-fECG) processing. Often, these methods are combined with other methods, such adaptive algorithms. However, there are many variants of the ICA methods and it is not clear which one is the most suitable for this task. The goal of this study is to test and objectively evaluate 11 variants of ICA methods combined with an adaptive fast transversal filter (FTF) for the purpose of extracting the NI-fECG. The methods were tested on two datasets, Labour dataset and Pregnancy dataset, which contained real records obtained during clinical practice. The efficiency of the methods was evaluated from the perspective of determining the accuracy of detection of QRS complexes through the parameters of accuracy (ACC), sensitivity (SE), positive predictive value (PPV), and harmonic mean between SE and PPV (F1). The best results were achieved with a combination of FastICA and FTF, which yielded mean values of ACC = 83.72%, SE = 92.13%, PPV = 90.16%, and F1 = 91.14%. Time of calculation was also taken into consideration in the methods. Although FastICA was ranked to be the sixth fastest with its mean computation time of 0.452 s, it had the best ratio of performance and speed. The combination of FastICA and adaptive FTF filter turned out to be very promising. In addition, such device would require signals acquired from the abdominal area only; no need to acquire reference signal from the mother’s chest.

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Takahashi, Gen. "Tannin-ferrocyanide OsO4 method for scanning electron microscopy with use of microwave irradiation." Proceedings, annual meeting, Electron Microscopy Society of America 48, no. 3 (August 12, 1990): 26–27. http://dx.doi.org/10.1017/s0424820100157668.

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The disadvantages of metal-coating techniques in high resolution SEM are limitation of resolution due to the thickness of coating, metal decoration of the surface ultrastructures, the lack of uniformity and continuity of coating or specimen damage during coating due to heat generation. In order to overcome these disadvantages and to obtain the better ultrastructural preservation after drying of specimens, the conductive staining methods have been devised.˜4The microwave irradiation(MWI) has recently been used for fixation of biological specimens for light and electron microscopy, immunohistochemistry and for acceleration of the time required for tissue processing and staining. In the present study, MWI can be successfully applied to each step in the tannin- ferrocyanide-OsO4(TA- FeCN-Os) method for high resolution SEM.The TA- FeCNsOs method for high resolution SEM [A] Primary Osmication(Fixation): double fixation with glutaraldehyde-paraformaldehyde and OsO4, TA-FeCN-Os method for TEM,Osmium-DMSO-Osmium, prolonged osmication or osmication after extraction with saponin, glycerol or detergents.

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Joy, David C. "Microanalysis with a 200keV FEG TEM." Proceedings, annual meeting, Electron Microscopy Society of America 49 (August 1991): 700–701. http://dx.doi.org/10.1017/s0424820100087811.

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Introduction The Hitachi HF-2000 is a 200keV cold field emitter TEM, designed to offer both high resolution imaging performance and a nanometer probe-forming mode for microanalytical operation. The purpose of this paper is to report some initial microanalytical results obtained from the HF-2000 installed at the University of Tennessee.General Electron-Optical Parameters The cold field emission gun, which runs at about 5x10-9pA pressure, uses an electrostatic lens configuration and a six stage accelerator. The entire electron gun system is under the control of a dedicated microprocessor which permits all of the operational parameters of the gun to be set, monitored, and adjusted through a keyboard and display. The tip emission current can be adjusted up to 40μA, and the gun voltage ratio (that is the ratio between the voltage on the second anode and the tip extraction voltage) can be varied from 4.5 to 7.5, permitting a significant degree of flexibility in optimizing the optics of the emitter.

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Lee, Man-Seung, Kyoung-Ju Lee, and Young-Joo Oh. "Solvent Extraction Equilibria of FeCl₃ from Hydrochloric Acid Solution with Alamine336." MATERIALS TRANSACTIONS 45, no. 7 (2004): 2364–68. http://dx.doi.org/10.2320/matertrans.45.2364.

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Zheng, Hai-yan, Yu Sun, Jin-wen Lu, Jian-hong Dong, Wei-ling Zhang, and Feng-man Shen. "Vanadium extraction from vanadium-bearing titanomagnetite by selective chlorination using chloride wastes (FeCl x )." Journal of Central South University 24, no. 2 (February 2017): 311–17. http://dx.doi.org/10.1007/s11771-017-3432-x.

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Malallah, Yaaqoub, Antarpreet Singh, Sampada Deshmukh, Chins Chinnasamy, Melania Marinescu, and Afshin S. Daryoush. "Complex permeability extraction of FeCo nanoparticles using annular ring resonator and its RF applications." Journal of the Franklin Institute 354, no. 18 (December 2017): 8758–71. http://dx.doi.org/10.1016/j.jfranklin.2017.04.014.

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Journal articles on the topic 'FECG EXTRACTION'

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