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Yan Z, Jiao L, Sun H, Sun R, Zhang J. Integration method of compressed sensing with variational mode decomposition based on gray wolf optimization and its denoising effect in mud pulse signal. THE REVIEW OF SCIENTIFIC INSTRUMENTS 2024; 95:025109. [PMID: 38407493 DOI: 10.1063/5.0188710] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/23/2023] [Accepted: 01/29/2024] [Indexed: 02/27/2024]
Abstract
The continuous wave mud pulse transmission holds great promise for the future of downhole data communication. However, significant noise interference during the transmission process poses a formidable challenge for decoding. In particular, effectively eliminating random noise with a substantial amplitude that overlaps with the pulse signal spectrum has long been a complex issue. To address this, an enhanced integration algorithm that merges variational mode decomposition (VMD) and compressed sensing (CS) to suppress high-intensity random noise is proposed in this paper. In response to the inadequacy of manually preset parameters in VMD, which often leads to suboptimal decomposition outcomes, the gray wolf optimization algorithm is designed to obtain the optimal penalty factor and decomposition mode number in VMD. Subsequently, the optimized parameter combination decomposes the signal into a series of intrinsic modes. The mode exhibiting a stronger correlation with the original signal is retained to enhance signal sparsity, thereby fulfilling the prerequisite for compressed sensing. The signal is then observed and reconstructed using the compressed sensing method to yield the final signal. The proposed algorithm has been compared with VMD, CS, and CEEMD; the results demonstrate that the method can enhance the signal-noise ratio by up to ∼20.55 dB. Furthermore, it yields higher correlation coefficients and smaller mean square errors. Moreover, the experimental results using real field data show that the useful pulse waveforms can be recognized effectively, assisting surface workers in acquiring precise downhole information, enhancing drilling efficiency, and significantly reducing the risk of engineering accidents.
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Affiliation(s)
- Zhidan Yan
- College of Control Science and Engineering, China University of Petroleum (East China), Changjiangxi Road 66, Qingdao, Shandong Province 266580, China
| | - Le Jiao
- College of Control Science and Engineering, China University of Petroleum (East China), Changjiangxi Road 66, Qingdao, Shandong Province 266580, China
| | - Hehui Sun
- Bohai Drilling Engineering Company Limited, China National Petroleum Corporation, Tianjin 300457, China
| | - Ruirui Sun
- College of Control Science and Engineering, China University of Petroleum (East China), Changjiangxi Road 66, Qingdao, Shandong Province 266580, China
| | - Junzhuang Zhang
- College of Control Science and Engineering, China University of Petroleum (East China), Changjiangxi Road 66, Qingdao, Shandong Province 266580, China
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Chen S, Luo H, Lyu W, Yu J, Qin J, Yu C. Compressed sensing framework for BCG signals based on the optical fiber sensor. OPTICS EXPRESS 2023; 31:29606-29618. [PMID: 37710757 DOI: 10.1364/oe.499746] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/07/2023] [Accepted: 08/02/2023] [Indexed: 09/16/2023]
Abstract
A compressed sensing (CS) framework is built for ballistocardiography (BCG) signals, which contains two parts of an optical fiber sensor-based heart monitoring system with a CS module and an end-to-end deep learning-based reconstruction algorithm. The heart monitoring system collects BCG data, and then compresses and transmits the data through the CS module at the sensing end. The deep learning-based algorithm reconstructs compressed data at the received end. To evaluate results, three traditional CS reconstruction algorithms and a deep learning method are adopted as references to reconstruct the compressed BCG data with different compression ratios (CRs). Results show that our framework can reconstruct signals successfully when the CR grows from 50% to 95% and outperforms other methods at high CRs. The mean absolute error (MAE) of the estimated heartbeat rate (HR) is lower than 1 bpm when the CR is below 95%. The proposed CS framework for BCG signals can be integrated into the IoMT system, which has great potential in health care for both medical and home use.
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Tanasković I, Miljković N. A new algorithm for fetal heart rate detection: Fractional order calculus approach. Med Eng Phys 2023; 118:104007. [PMID: 37536830 DOI: 10.1016/j.medengphy.2023.104007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2023] [Revised: 05/23/2023] [Accepted: 06/15/2023] [Indexed: 08/05/2023]
Abstract
OBJECTIVES A new modified Pan-Tompkins' (mPT) method for fetal heart rate detection is presented. The mPT method is based on the hypothesis that optimal fractional order derivative and optimal window width of the moving average filter would enable efficient estimation of fetal heart rate from surface abdominal electrophysiological recordings with relatively low signal-to-noise ratios. METHODS The algorithm is tested on signals recorded from the abdomen of pregnant women available from the PhysioNet Computing in Cardiology Challenge database. Fetal heart rate detection is performed on 10-s long segments selected by the estimation of signal-to-noise ratios (the extravagance of the fetal QRS peak to its surroundings and to the whole signal; and the mean ratio of fetal and maternal QRS peaks) and on the manually selected segments. RESULTS The best results are obtained via criteria based on the extravagance of the fetal QRS peak to its surroundings that reached average sensitivity of 97%, positive predictive value of 97%, error rate of ∼3.5%, and F1 score of 97%. The obtained averaged optimal parameters for mPT are 0.51 for fractional order and 24.5 ms for the window width of the moving average filter. CONCLUSION Proposed mPT algorithm showed satisfactory performance for fetal heart rate detection. Further adaptations of the presented mPT method could be used for peak detection in noisy environments in biomedical signal analysis in general.
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Affiliation(s)
- Ilija Tanasković
- University of Belgrade - School of Electrical Engineering, Bulevar kralja Aleksandra 73, 11000 Belgrade, Serbia; Institute for Artificial Intelligence R&D, Fruskogorska 1, 21000 Novi Sad, Serbia
| | - Nadica Miljković
- University of Belgrade - School of Electrical Engineering, Bulevar kralja Aleksandra 73, 11000 Belgrade, Serbia; Faculty of Electrical Engineering, University of Ljubljana. Tržaška c. 25, 1000 Ljubljana, Slovenia.
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Kahankova R, Barnova K, Jaros R, Pavlicek J, Snasel V, Martinek R. Pregnancy in the time of COVID-19: towards Fetal monitoring 4.0. BMC Pregnancy Childbirth 2023; 23:33. [PMID: 36647041 PMCID: PMC9841500 DOI: 10.1186/s12884-023-05349-3] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2022] [Accepted: 01/05/2023] [Indexed: 01/18/2023] Open
Abstract
On the outbreak of the global COVID-19 pandemic, high-risk and vulnerable groups in the population were at particular risk of severe disease progression. Pregnant women were one of these groups. The infectious disease endangered not only the physical health of pregnant women, but also their mental well-being. Improving the mental health of pregnant women and reducing their risk of an infectious disease could be achieved by using remote home monitoring solutions. These would allow the health of the mother and fetus to be monitored from the comfort of their home, a reduction in the number of physical visits to the doctor and thereby eliminate the need for the mother to venture into high-risk public places. The most commonly used technique in clinical practice, cardiotocography, suffers from low specificity and requires skilled personnel for the examination. For that and due to the intermittent and active nature of its measurements, it is inappropriate for continuous home monitoring. The pandemic has demonstrated that the future lies in accurate remote monitoring and it is therefore vital to search for an option for fetal monitoring based on state-of-the-art technology that would provide a safe, accurate, and reliable information regarding fetal and maternal health state. In this paper, we thus provide a technical and critical review of the latest literature and on this topic to provide the readers the insights to the applications and future directions in fetal monitoring. We extensively discuss the remaining challenges and obstacles in future research and in developing the fetal monitoring in the new era of Fetal monitoring 4.0, based on the pillars of Healthcare 4.0.
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Affiliation(s)
- Radana Kahankova
- Department of Cybernetics and Biomedical Engineering, Faculty of Electrical Engineering and Computer Science, VSB–Technical University of Ostrava, Ostrava, Czechia
| | - Katerina Barnova
- Department of Cybernetics and Biomedical Engineering, Faculty of Electrical Engineering and Computer Science, VSB–Technical University of Ostrava, Ostrava, Czechia
| | - Rene Jaros
- Department of Cybernetics and Biomedical Engineering, Faculty of Electrical Engineering and Computer Science, VSB–Technical University of Ostrava, Ostrava, Czechia
| | - Jan Pavlicek
- Department of Pediatrics, Faculty Hospital, Faculty of Medicine, Ostrava University, Ostrava, Czechia
| | - Vaclav Snasel
- Department of Computer Science, Faculty of Electrical Engineering and Computer Science, VSB–Technical University of Ostrava, Ostrava, Czechia
| | - Radek Martinek
- Department of Cybernetics and Biomedical Engineering, Faculty of Electrical Engineering and Computer Science, VSB–Technical University of Ostrava, Ostrava, Czechia
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An Innovative Machine Learning Approach for Classifying ECG Signals in Healthcare Devices. JOURNAL OF HEALTHCARE ENGINEERING 2022; 2022:7194419. [PMID: 35463679 PMCID: PMC9020932 DOI: 10.1155/2022/7194419] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/22/2022] [Revised: 02/20/2022] [Accepted: 02/23/2022] [Indexed: 12/24/2022]
Abstract
An ECG is a diagnostic technique that examines and records the heart's electrical impulses. It is easy to categorise and prevent computational abstractions in the ECG signal using the conventional method for obtaining ECG features. It is a significant issue, but it is also a difficult and time-consuming chore for cardiologists and medical professionals. The proposed classifier eliminates all of the following limitations. Machine learning in healthcare equipment reduces moral transgressions. This study's primary purpose is to calculate the R-R interval and analyze the blockage utilising simple algorithms and approaches that give high accuracy. The MIT-BIH dataset may be used to rebuild the data. The acquired data may include both normal and abnormal ECGs. A Gabor filter is employed to generate a noiseless signal, and DCT-DOST is used to calculate the signal's amplitude. The amplitude is computed to detect any cardiac anomalies. A genetic algorithm derives the main highlights from the R peak and cycle segment length underlying the ECG signal. So, combining data with specific qualities maximises identification. The genetic algorithm aids in hereditary computations, which aids in multitarget improvement. Finally, Radial Basis Function Neural Network (RBFNN) is presented as an example. An efficient feedforward neural network lowers the number of local minima in the signal. It shows progress in identifying both normal and abnormal ECG signals.
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Optimization of adaptive filter control parameters for non-invasive fetal electrocardiogram extraction. PLoS One 2022; 17:e0266807. [PMID: 35404946 PMCID: PMC9000127 DOI: 10.1371/journal.pone.0266807] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2021] [Accepted: 03/28/2022] [Indexed: 11/23/2022] Open
Abstract
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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Barnova K, Martinek R, Jaros R, Kahankova R, Behbehani K, Snasel V. System for adaptive extraction of non-invasive fetal electrocardiogram. Appl Soft Comput 2021. [DOI: 10.1016/j.asoc.2021.107940] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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8
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Niida N, Wang L, Ohtsuki T, Owada K, Honma N, Hayashi H. Fetal Heart Rate Detection Using First Derivative of ECG Waveform and Multiple Weighting Functions. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2021; 2021:434-438. [PMID: 34891326 DOI: 10.1109/embc46164.2021.9630268] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
Fetal heart rate monitoring using the abdominal electrocardiograph (ECG) is an important topic for the diagnosis of heart defects. Many studies on fetal heart rate detection have been presented, however, their accuracy is still unsatisfactory. That is because the fetal ECG waveform is contaminated by maternal ECG interference, muscle contractions, and motion artifacts. One of the conventional methods is to detect the R-peaks from the integrated power of the frequency corresponding to the fetal heartbeats. However, the detection accuracy of the R-peaks is not enough. In this paper, we propose a method to generate the candidates of R-peaks using the first derivative of the signal and to pick up the estimated heartbeats by a multiple weighting function. The proposed multiple weighting function is designed by the Gaussian distribution, of which parameters are set from a grid search with the goal of minimizing the standard deviation of RR intervals (neighboring R-peaks intervals). The validation for the proposed framework has been evaluated on real-world data, which got the better accuracy than the conventional method that detects R-peaks from the integrated power and uses the weighting function produced by a fixed parameter of Gaussian distribution [12]. The averaged absolute error (AAE) which compares the estimated fetal heart rate and the reference fetal heart rate has been decreased by 17.528 bpm.
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A novel algorithm based on ensemble empirical mode decomposition for non-invasive fetal ECG extraction. PLoS One 2021; 16:e0256154. [PMID: 34388227 PMCID: PMC8363249 DOI: 10.1371/journal.pone.0256154] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2021] [Accepted: 08/01/2021] [Indexed: 11/19/2022] Open
Abstract
Non-invasive fetal electrocardiography appears to be one of the most promising fetal monitoring techniques during pregnancy and delivery nowadays. This method is based on recording electrical potentials produced by the fetal heart from the surface of the maternal abdomen. Unfortunately, in addition to the useful fetal electrocardiographic signal, there are other interference signals in the abdominal recording that need to be filtered. The biggest challenge in designing filtration methods is the suppression of the maternal electrocardiographic signal. This study focuses on the extraction of fetal electrocardiographic signal from abdominal recordings using a combination of independent component analysis, recursive least squares, and ensemble empirical mode decomposition. The method was tested on two databases, the Fetal Electrocardiograms, Direct and Abdominal with Reference Heartbeats Annotations and the PhysioNet Challenge 2013 database. The evaluation was performed by the assessment of the accuracy of fetal QRS complexes detection and the quality of fetal heart rate determination. The effectiveness of the method was measured by means of the statistical parameters as accuracy, sensitivity, positive predictive value, and F1-score. Using the proposed method, when testing on the Fetal Electrocardiograms, Direct and Abdominal with Reference Heartbeats Annotations database, accuracy higher than 80% was achieved for 11 out of 12 recordings with an average value of accuracy 92.75% [95% confidence interval: 91.19-93.88%], sensitivity 95.09% [95% confidence interval: 93.68-96.03%], positive predictive value 96.36% [95% confidence interval: 95.05-97.17%] and F1-score 95.69% [95% confidence interval: 94.83-96.35%]. When testing on the Physionet Challenge 2013 database, accuracy higher than 80% was achieved for 17 out of 25 recordings with an average value of accuracy 78.24% [95% confidence interval: 73.44-81.85%], sensitivity 81.79% [95% confidence interval: 76.59-85.43%], positive predictive value 87.16% [95% confidence interval: 81.95-90.35%] and F1-score 84.08% [95% confidence interval: 80.75-86.64%]. Moreover, the non-invasive ST segment analysis was carried out on the records from the Fetal Electrocardiograms, Direct and Abdominal with Reference Heartbeats Annotations database and achieved high accuracy in 7 from in total of 12 records (mean values μ < 0.1 and values of ±1.96σ < 0.1).
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10
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Non-Invasive Fetal Electrocardiogram Monitoring Techniques: Potential and Future Research Opportunities in Smart Textiles. SIGNALS 2021. [DOI: 10.3390/signals2030025] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
During the pregnancy, fetal electrocardiogram (FECG) is deployed to analyze fetal heart rate (FHR) of the fetus to indicate the growth and health of the fetus to determine any abnormalities and prevent diseases. The fetal electrocardiogram monitoring can be carried out either invasively by placing the electrodes on the scalp of the fetus, involving the skin penetration and the risk of infection, or non-invasively by recording the fetal heart rate signal from the mother’s abdomen through a placement of electrodes deploying portable, wearable devices. Non-invasive fetal electrocardiogram (NIFECG) is an evolving technology in fetal surveillance because of the comfort to the pregnant women and being achieved remotely, specifically in the unprecedented circumstances such as pandemic or COVID-19. Textiles have been at the heart of human technological progress for thousands of years, with textile developments closely tied to key inventions that have shaped societies. The relatively recent invention of smart textiles is set to push boundaries again and has already opened the potential for garments relevant to medicine, and health monitoring. This paper aims to discuss the different technologies and methods used in non-invasive fetal electrocardiogram (NIFECG) monitoring as well as the potential and future research directions of NIFECG in the smart textiles area.
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Shinde AN, Nalbalwar SL, Nandgaonkar AB. Impact of optimal scaling coefficients in bi-orthogonal wavelet filters on compressed sensing. INTERNATIONAL JOURNAL OF PERVASIVE COMPUTING AND COMMUNICATIONS 2021. [DOI: 10.1108/ijpcc-08-2019-0065] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Purpose
In today’s digital world, real-time health monitoring is becoming a most important challenge in the field of medical research. Body signals such as electrocardiogram (ECG), electromyogram and electroencephalogram (EEG) are produced in human body. This continuous monitoring generates huge count of data and thus an efficient method is required to shrink the size of the obtained large data. Compressed sensing (CS) is one of the techniques used to compress the data size. This technique is most used in certain applications, where the size of data is huge or the data acquisition process is too expensive to gather data from vast count of samples at Nyquist rate. This paper aims to propose Lion Mutated Crow search Algorithm (LM-CSA), to improve the performance of the LMCSA model.
Design/methodology/approach
A new CS algorithm is exploited in this paper, where the compression process undergoes three stages: designing of stable measurement matrix, signal compression and signal reconstruction. Here, the compression process falls under certain working principle, and is as follows: signal transformation, computation of Θ and normalization. As the main contribution, the theta value evaluation is proceeded by a new “Enhanced bi-orthogonal wavelet filter.” The enhancement is given under the scaling coefficients, where they are optimally tuned for processing the compression. However, the way of tuning seems to be the great crisis, and hence this work seeks the strategy of meta-heuristic algorithms. Moreover, a new hybrid algorithm is introduced that solves the above mentioned optimization inconsistency. The proposed algorithm is named as “Lion Mutated Crow search Algorithm (LM-CSA),” which is the hybridization of crow search algorithm (CSA) and lion algorithm (LA) to enhance the performance of the LM-CSA model.
Findings
Finally, the proposed LM-CSA model is compared over the traditional models in terms of certain error measures such as mean error percentage (MEP), symmetric mean absolute percentage error (SMAPE), mean absolute scaled error, mean absolute error (MAE), root mean square error, L1-norm and L2-normand infinity-norm. For ECG analysis, under bior 3.1, LM-CSA is 56.6, 62.5 and 81.5% better than bi-orthogonal wavelet in terms of MEP, SMAPE and MAE, respectively. Under bior 3.7 for ECG analysis, LM-CSA is 0.15% better than genetic algorithm (GA), 0.10% superior to particle search optimization (PSO), 0.22% superior to firefly (FF), 0.22% superior to CSA and 0.14% superior to LA, respectively, in terms of L1-norm. Further, for EEG analysis, LM-CSA is 86.9 and 91.2% better than the traditional bi-orthogonal wavelet under bior 3.1. Under bior 3.3, LM-CSA is 91.7 and 73.12% better than the bi-orthogonal wavelet in terms of MAE and MEP, respectively. Under bior 3.5 for EEG, L1-norm of LM-CSA is 0.64% superior to GA, 0.43% superior to PSO, 0.62% superior to FF, 0.84% superior to CSA and 0.60% better than LA, respectively.
Originality/value
This paper presents a novel CS framework using LM-CSA algorithm for EEG and ECG signal compression. To the best of the authors’ knowledge, this is the first work to use LM-CSA with enhanced bi-orthogonal wavelet filter for enhancing the CS capability as well reducing the errors.
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Matonia A, Jezewski J, Kupka T, Jezewski M, Horoba K, Wrobel J, Czabanski R, Kahankowa R. Fetal electrocardiograms, direct and abdominal with reference heartbeat annotations. Sci Data 2020; 7:200. [PMID: 32587253 PMCID: PMC7316827 DOI: 10.1038/s41597-020-0538-z] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2019] [Accepted: 05/20/2020] [Indexed: 11/09/2022] Open
Abstract
Monitoring fetal heart rate (FHR) variability plays a fundamental role in fetal state assessment. Reliable FHR signal can be obtained from an invasive direct fetal electrocardiogram (FECG), but this is limited to labour. Alternative abdominal (indirect) FECG signals can be recorded during pregnancy and labour. Quality, however, is much lower and the maternal heart and uterine contractions provide sources of interference. Here, we present ten twenty-minute pregnancy signals and 12 five-minute labour signals. Abdominal FECG and reference direct FECG were recorded simultaneously during labour. Reference pregnancy signal data came from an automated detector and were corrected by clinical experts. The resulting dataset exhibits a large variety of interferences and clinically significant FHR patterns. We thus provide the scientific community with access to bioelectrical fetal heart activity signals that may enable the development of new methods for FECG signals analysis, and may ultimately advance the use and accuracy of abdominal electrocardiography methods.
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Affiliation(s)
- Adam Matonia
- Łukasiewicz Research Network - Institute of Medical Technology and Equipment, 118 Roosevelt Str., 41-800, Zabrze, Poland.
| | - Janusz Jezewski
- Łukasiewicz Research Network - Institute of Medical Technology and Equipment, 118 Roosevelt Str., 41-800, Zabrze, Poland
| | - Tomasz Kupka
- Łukasiewicz Research Network - Institute of Medical Technology and Equipment, 118 Roosevelt Str., 41-800, Zabrze, Poland
| | - Michał Jezewski
- Silesian University of Technology, Department of Cybernetics, Nanotechnology and Data Processing, 16 Akademicka Str., 44-100, Gliwice, Poland
| | - Krzysztof Horoba
- Łukasiewicz Research Network - Institute of Medical Technology and Equipment, 118 Roosevelt Str., 41-800, Zabrze, Poland
| | - Janusz Wrobel
- Łukasiewicz Research Network - Institute of Medical Technology and Equipment, 118 Roosevelt Str., 41-800, Zabrze, Poland
| | - Robert Czabanski
- Silesian University of Technology, Department of Cybernetics, Nanotechnology and Data Processing, 16 Akademicka Str., 44-100, Gliwice, Poland
| | - Radana Kahankowa
- VSB-Technical University of Ostrava, Department of Cybernetics and Biomedical Engineering, Faculty of Electrical Engineering and Computer Science, 17. Listopadu 2172/15 Str., 70800, Ostrava, Czech Republic
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Taha L, Abdel-Raheem E. A Null Space-Based Blind Source Separation for Fetal Electrocardiogram Signals. SENSORS 2020; 20:s20123536. [PMID: 32580397 PMCID: PMC7348901 DOI: 10.3390/s20123536] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/20/2020] [Revised: 06/19/2020] [Accepted: 06/19/2020] [Indexed: 11/16/2022]
Abstract
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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Alsayyari A. Fetal cardiotocography monitoring using Legendre neural networks. ACTA ACUST UNITED AC 2020; 64:669-675. [PMID: 31199757 DOI: 10.1515/bmt-2018-0074] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2018] [Accepted: 10/18/2018] [Indexed: 11/15/2022]
Abstract
A new technique for electronic fetal monitoring (EFM) using an efficient structure of neural networks based on the Legendre series is presented in this paper. Such a structure is achieved by training a Legendre series-based neural network (LNN) to classify the different fetal states based on recorded cardiotocographic (CTG) data sets given by others. These data sets consist of measurements of fetal heart rate (FHR) and uterine contraction (UC). The applied LNN utilizes a Legendre series expansion for the input vectors and, hence, has the capability to produce explicit equations describing multi-input multi-output systems. Simulations of the proposed technique in EFM demonstrate its high efficiency. Training the LNN requires a few number of iterations (5-10 epochs). The applied technique makes the classification of the fetal state available through equations combining the trained LNN weights and the current measured CTG record. A comparison of performance between the proposed LNN and other popular neural network techniques such as the Volterra neural network (VNN) in EFM is provided. The comparison shows that, the LNN outperforms the VNN in case of less computational requirements and fast convergence with a lower mean square error.
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Affiliation(s)
- Abdulaziz Alsayyari
- Computer Engineering Department, Shaqra University, Dawadmi 11911, Ar Riyadh, Saudi Arabia
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15
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Singh R, Rajpal N, Mehta R. An empirical sequence to extract fetal electrocardiogram using the Kernel and wavelet optimization. JOURNAL OF INFORMATION & OPTIMIZATION SCIENCES 2020. [DOI: 10.1080/02522667.2020.1715562] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
Affiliation(s)
- Ritu Singh
- University School of Information and Communication Technology, Guru Gobind Singh Indraprastha University, Sector 16C, Dwarka, New Delhi 110078, India
| | - Navin Rajpal
- University School of Information and Communication Technology, Guru Gobind Singh Indraprastha University, Sector 16C, Dwarka, New Delhi 110078, India,
| | - Rajesh Mehta
- Thapar Institute of Engineering and Technology, Bhadson Road, Patiala 147001, Punjab, India,
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Kaleem AM, Kokate RD. An Efficient Adaptive Filter for Fetal ECG Extraction Using Neural Network. JOURNAL OF INTELLIGENT SYSTEMS 2019. [DOI: 10.1515/jisys-2017-0031] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022] Open
Abstract
Abstract
Fetal electrocardiogram checking is a strategy for acquiring critical data about the state of the fetus during pregnancy and labor. This is done by measuring electrical signals created by the fetal heart as measured from multichannel potential recordings on the mother’s body surface. In any case, extraction of fetal signal is difficult because the signal is marred by the mother’s heartbeat signal. Subsequently, in this paper, a powerful versatile filtering strategy is utilized to eliminate the mother’s heartbeat signal with the specific end goal of extricating the fetal signal. The proposed procedure was executed in the working stage of MATLAB and the execution results were investigated.
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Electrocardiographic Fragmented Activity (I): Physiological Meaning of Multivariate Signal Decompositions. APPLIED SCIENCES-BASEL 2019. [DOI: 10.3390/app9173566] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/03/2023]
Abstract
Recent research has proven the existence of statistical relation among fragmented QRS and several highly prevalence diseases, such as cardiac sarcoidosis, acute coronary syndrome, arrythmogenic cardiomyopathies, Brugada syndrome, and hypertrophic cardiomyopathy. One out of five hundred people suffer from hypertrophic cardiomyopathies. The relation among the fragmentation and arrhythmias drives the objective of this work, which is to propose a valid method for QRS fragmentation detection. With that aim, we followed a two-stage approach. First, we identified the features that better characterize the fragmentation by analyzing the physiological interpretation of multivariate approaches, such as principal component analysis (PCA) and independent component analysis (ICA). Second, we created an invariant transformation method for the multilead electrocardiogram (ECG), by scrutinizing the statistical distributions of the PCA eigenvectors and of the ICA transformation arrays, in order to anchor the desired elements in the suitable leads in the feature space. A complete database was compounded incorporating real fragmented ECGs, surrogate registers by synthetically adding fragmented activity to real non-fragmented ECG registers, and standard clean ECGs. Results showed that the creation of beat templates together with the application of PCA over eight independent leads achieves 0.995 fragmentation enhancement ratio and 0.07 dispersion coefficient. In the case of ICA over twelve leads, the results were 0.995 fragmentation enhancement ratio and 0.70 dispersion coefficient. We conclude that the algorithm presented in this work constructs a new paradigm, by creating a systematic and powerful tool for clinical anamnesis and evaluation based on multilead ECG. This approach consistently consolidates the inconspicuous elements present in multiple leads onto designated variables in the output space, hence offering additional and valid visual and non-visual information to standard clinical review, and opening the door to a more accurate automatic detection and statistically valid systematic approach for a wide number of applications. In this direction and within the companion paper, further developments are presented applying this technique to fragmentation detection.
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Gurve D, Krishnan S. Separation of Fetal-ECG From Single-Channel Abdominal ECG Using Activation Scaled Non-Negative Matrix Factorization. IEEE J Biomed Health Inform 2019; 24:669-680. [PMID: 31170084 DOI: 10.1109/jbhi.2019.2920356] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
Performing a fetal electrocardiogram (ECG) analysis, which contains important information about the status of a fetal, can help to detect fetus health even before birth. Since the fetal ECG extracted from the ECG signal recorded from the mother's abdomen, this extraction problem can be seen as a source separation problem, of recovering source signals from signal mixtures. In this paper, a method for separation of fetal ECG from abdominal ECG using activation scaled non-negative matrix factorization (NMF) is proposed. The performance of the proposed method is also compared with independent component analysis. The proposed method is tested under three different scenarios. First, the original abdominal ECG signal is used for fetal separation. Second, the recovered abdominal ECG after compression is used for separation. Third, the fetal ECG is extracted from the compressed domain of the abdominal ECG. We applied scaling on the activation matrix obtained using NMF for emphasizing the fetal ECG present in abdominal ECG. The improved-regularized least-squares [Formula: see text] algorithm is used for signal reconstruction, which provides better reconstruction quality and less processing time in comparison with other existing methods. The proposed algorithm is evaluated and tested on real abdominal recordings obtained from two different datasets from Physionet. The first dataset used for this paper is Silesia dataset for abdominal and direct f-ECG, and the second dataset we considered is Set-A of the Physionet challenge. The obtained outcomes reveal that it is possible to separate fetal ECG from single-channel abdominal ECG signal, which can help us to achieve energy-efficient transmission, and cost-effective fetal ECG remote monitoring for Internet-of-Things applications, where device battery and computational capacity are limited.
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Jaros R, Martinek R, Kahankova R. Non-Adaptive Methods for Fetal ECG Signal Processing: A Review and Appraisal. SENSORS 2018; 18:s18113648. [PMID: 30373259 PMCID: PMC6263968 DOI: 10.3390/s18113648] [Citation(s) in RCA: 26] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/28/2018] [Revised: 10/18/2018] [Accepted: 10/24/2018] [Indexed: 11/16/2022]
Abstract
Fetal electrocardiography is among the most promising methods of modern electronic fetal monitoring. However, before they can be fully deployed in the clinical practice as a gold standard, the challenges associated with the signal quality must be solved. During the last two decades, a great amount of articles dealing with improving the quality of the fetal electrocardiogram signal acquired from the abdominal recordings have been introduced. This article aims to present an extensive literature survey of different non-adaptive signal processing methods applied for fetal electrocardiogram extraction and enhancement. It is limiting that a different non-adaptive method works well for each type of signal, but independent component analysis, principal component analysis and wavelet transforms are the most commonly published methods of signal processing and have good accuracy and speed of algorithms.
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Affiliation(s)
- Rene Jaros
- Department of Cybernetics and Biomedical Engineering, Faculty of Electrical Engineering and Computer Science, VSB-Technical University of Ostrava, 17. listopadu 15, 708 33 Ostrava, Czech Republic.
| | - Radek Martinek
- Department of Cybernetics and Biomedical Engineering, Faculty of Electrical Engineering and Computer Science, VSB-Technical University of Ostrava, 17. listopadu 15, 708 33 Ostrava, Czech Republic.
| | - Radana Kahankova
- Department of Cybernetics and Biomedical Engineering, Faculty of Electrical Engineering and Computer Science, VSB-Technical University of Ostrava, 17. listopadu 15, 708 33 Ostrava, Czech Republic.
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Gurve D, Pant JK, Krishnan S. Real-time fetal ECG extraction from multichannel abdominal ECG using compressive sensing and ICA. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2018; 2017:2794-2797. [PMID: 29060478 DOI: 10.1109/embc.2017.8037437] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Abstract
An improved method for separation of fetal electrocardiogram (fECG) from abdominal electrocardiogram (abdECG) is proposed in this paper. Proposed method combines two widely used techniques i.e. compressive sensing (CS) and independent component analysis (ICA). Separation of fECG is carried out by applying ICA directly on the compressed signal. The efficient improved ℓp-regularized least-sqaures (ℓp-RLS) algorithm is used for signal reconstruction, which provides better reconstruction quality and less processing time in comparison with other existing methods. The proposed algorithm is evaluated and tested on Physionet datasets which contain 75 records in set-A, 100 records in set-B and 6 records in Silesia dataset. The obtained outcomes reveal that proposed algorithm shows promising results (Sensitivity S=92%, Positive predictivity P+ = 93%, F1 measure = 92.5% with average percentage root-mean-square difference PRD =6.89% and Execution time= 2.91 sec.). The results also indicate that there is a substantial improvement in quality of reconstructed signal which is achieved by maintaining lowest PRD.
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An Improved FastICA Method for Fetal ECG Extraction. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2018; 2018:7061456. [PMID: 29887913 PMCID: PMC5985131 DOI: 10.1155/2018/7061456] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/08/2018] [Revised: 04/03/2018] [Accepted: 04/22/2018] [Indexed: 12/29/2022]
Abstract
Objective The fast fixed-point algorithm for independent component analysis (FastICA) has been widely used in fetal electrocardiogram (ECG) extraction. However, the FastICA algorithm is sensitive to the initial weight vector, which affects the convergence of the algorithm. In order to solve this problem, an improved FastICA method was proposed to extract fetal ECG. Methods First, the maternal abdominal mixed signal was centralized and whitened, and the overrelaxation factor was incorporated into Newton's iterative algorithm to process the initial weight vector randomly generated. The improved FastICA algorithm was used to separate the source components, selected the best maternal ECG from the separated source components, and detected the R-wave location of the maternal ECG. Finally, the maternal ECG component in each channel was removed by the singular value decomposition (SVD) method to obtain a clean fetal ECG signal. Results An annotated clinical fetal ECG database was used to evaluate the improved algorithm and the conventional FastICA algorithm. The average number of iterations of the algorithm was reduced from 35 before the improvement to 13. Correspondingly, the average running time was reduced from 1.25 s to 1.04 s when using the improved algorithm. The signal-to-noise ratio (SNR) based on eigenvalues of the improved algorithm was 1.55, as compared to 0.99 of the conventional FastICA algorithm. The SNR based on cross-correlation coefficients of the conventional algorithm was also improved from 0.59 to 2.02. The sensitivity, positive predictive accuracy, and harmonic mean (F1) of the improved method were 99.37%, 99.00%, and 99.19%, respectively, while these metrics of the conventional FastICA method were 99.03%, 98.53%, and 98.78%, respectively. Conclusions The proposed improved FastICA algorithm based on the overrelaxation factor, while maintaining the rate of convergence, relaxes the requirement of initial weight vector, avoids the unbalanced convergence, reduces the number of iterations, and improves the convergence performance.
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A Digital Compressed Sensing-Based Energy-Efficient Single-Spot Bluetooth ECG Node. JOURNAL OF HEALTHCARE ENGINEERING 2018; 2018:2687389. [PMID: 29599945 PMCID: PMC5823422 DOI: 10.1155/2018/2687389] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/31/2017] [Accepted: 12/05/2017] [Indexed: 11/17/2022]
Abstract
Energy efficiency is still the obstacle for long-term real-time wireless ECG monitoring. In this paper, a digital compressed sensing- (CS-) based single-spot Bluetooth ECG node is proposed to deal with the challenge in wireless ECG application. A periodic sleep/wake-up scheme and a CS-based compression algorithm are implemented in a node, which consists of ultra-low-power analog front-end, microcontroller, Bluetooth 4.0 communication module, and so forth. The efficiency improvement and the node's specifics are evidenced by the experiments using the ECG signals sampled by the proposed node under daily activities of lay, sit, stand, walk, and run. Under using sparse binary matrix (SBM), block sparse Bayesian learning (BSBL) method, and discrete cosine transform (DCT) basis, all ECG signals were essentially undistorted recovered with root-mean-square differences (PRDs) which are less than 6%. The proposed sleep/wake-up scheme and data compression can reduce the airtime over energy-hungry wireless links, the energy consumption of proposed node is 6.53 mJ, and the energy consumption of radio decreases 77.37%. Moreover, the energy consumption increase caused by CS code execution is negligible, which is 1.3% of the total energy consumption.
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Da Poian G, Rozell CJ, Bernardini R, Rinaldo R, Clifford GD. Matched Filtering for Heart Rate Estimation on Compressive Sensing ECG Measurements. IEEE Trans Biomed Eng 2017; 65:1349-1358. [PMID: 28920895 DOI: 10.1109/tbme.2017.2752422] [Citation(s) in RCA: 32] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
OBJECTIVE Compressive sensing (CS) has recently been applied as a low-complexity compression framework for long-term monitoring of electrocardiogram (ECG) signals using wireless body sensor networks. Long-term recording of ECG signals can be useful for diagnostic purposes and to monitor the evolution of several widespread diseases. In particular, beat-to-beat intervals provide important clinical information, and these can be derived from the ECG signal by computing the distance between QRS complexes (R-peaks). Numerous methods for R-peak detection are available for uncompressed ECG. However, in the case of compressed sensed data, signal reconstruction can be performed with relatively complex optimization algorithms, which may require significant energy consumption. This paper addresses the problem of heart rate estimation from CS ECG recordings, avoiding the reconstruction of the entire signal. METHODS We consider a framework, where the ECG signals are represented under the form of CS linear measurements. The QRS locations are estimated in the compressed domain by computing the correlation of the compressed ECG and a known QRS template. RESULTS Experiments on actual ECG signals show that our novel solution is competitive with methods applied to the reconstructed signals. CONCLUSION Avoiding the reconstruction procedure, the proposed method proves to be very convenient for real-time low-power applications.
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An innovative multi-level singular value decomposition and compressed sensing based framework for noise removal from heart sounds. Biomed Signal Process Control 2017. [DOI: 10.1016/j.bspc.2017.04.005] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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Jezewski J, Wrobel J, Matonia A, Horoba K, Martinek R, Kupka T, Jezewski M. Is Abdominal Fetal Electrocardiography an Alternative to Doppler Ultrasound for FHR Variability Evaluation? Front Physiol 2017; 8:305. [PMID: 28559852 PMCID: PMC5432618 DOI: 10.3389/fphys.2017.00305] [Citation(s) in RCA: 34] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2017] [Accepted: 04/27/2017] [Indexed: 12/02/2022] Open
Abstract
Great expectations are connected with application of indirect fetal electrocardiography (FECG), especially for home telemonitoring of pregnancy. Evaluation of fetal heart rate (FHR) variability, when determined from FECG, uses the same criteria as for FHR signal acquired classically—through ultrasound Doppler method (US). Therefore, the equivalence of those two methods has to be confirmed, both in terms of recognizing classical FHR patterns: baseline, accelerations/decelerations (A/D), long-term variability (LTV), as well as evaluating the FHR variability with beat-to-beat accuracy—short-term variability (STV). The research material consisted of recordings collected from 60 patients in physiological and complicated pregnancy. The FHR signals of at least 30 min duration were acquired dually, using two systems for fetal and maternal monitoring, based on US and FECG methods. Recordings were retrospectively divided into normal (41) and abnormal (19) fetal outcome. The complex process of data synchronization and validation was performed. Obtained low level of the signal loss (4.5% for US and 1.8% for FECG method) enabled to perform both direct comparison of FHR signals, as well as indirect one—by using clinically relevant parameters. Direct comparison showed that there is no measurement bias between the acquisition methods, whereas the mean absolute difference, important for both visual and computer-aided signal analysis, was equal to 1.2 bpm. Such low differences do not affect the visual assessment of the FHR signal. However, in the indirect comparison the inconsistencies of several percent were noted. This mainly affects the acceleration (7.8%) and particularly deceleration (54%) patterns. In the signals acquired using the electrocardiography the obtained STV and LTV indices have shown significant overestimation by 10 and 50% respectively. It also turned out, that ability of clinical parameters to distinguish between normal and abnormal groups do not depend on the acquisition method. The obtained results prove that the abdominal FECG, considered as an alternative to the ultrasound approach, does not change the interpretation of the FHR signal, which was confirmed during both visual assessment and automated analysis.
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Affiliation(s)
- Janusz Jezewski
- Institute of Medical Technology and Equipment ITAMZabrze, Poland
| | - Janusz Wrobel
- Institute of Medical Technology and Equipment ITAMZabrze, Poland
| | - Adam Matonia
- Institute of Medical Technology and Equipment ITAMZabrze, Poland
| | - Krzysztof Horoba
- Institute of Medical Technology and Equipment ITAMZabrze, Poland
| | - Radek Martinek
- Department of Cybernetics and Biomedical Engineering, VSB-Technical University of OstravaOstrava, Czechia
| | - Tomasz Kupka
- Institute of Medical Technology and Equipment ITAMZabrze, Poland
| | - Michal Jezewski
- Institute of Electronics, Silesian University of TechnologyGliwice, Poland
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A Combined Independent Source Separation and Quality Index Optimization Method for Fetal ECG Extraction from Abdominal Maternal Leads. SENSORS 2017; 17:s17051135. [PMID: 28509860 PMCID: PMC5470811 DOI: 10.3390/s17051135] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/21/2017] [Revised: 05/06/2017] [Accepted: 05/11/2017] [Indexed: 11/21/2022]
Abstract
The non-invasive fetal electrocardiogram (fECG) technique has recently received considerable interest in monitoring fetal health. The aim of our paper is to propose a novel fECG algorithm based on the combination of the criteria of independent source separation and of a quality index optimization (ICAQIO-based). The algorithm was compared with two methods applying the two different criteria independently—the ICA-based and the QIO-based methods—which were previously developed by our group. All three methods were tested on the recently implemented Fetal ECG Synthetic Database (FECGSYNDB). Moreover, the performance of the algorithm was tested on real data from the PhysioNet fetal ECG Challenge 2013 Database. The proposed combined method outperformed the other two algorithms on the FECGSYNDB (ICAQIO-based: 98.78%, QIO-based: 97.77%, ICA-based: 97.61%). Significant differences were obtained in particular in the conditions when uterine contractions and maternal and fetal ectopic beats occurred. On the real data, all three methods obtained very high performances, with the QIO-based method proving slightly better than the other two (ICAQIO-based: 99.38%, QIO-based: 99.76%, ICA-based: 99.37%). The findings from this study suggest that the proposed method could potentially be applied as a novel algorithm for accurate extraction of fECG, especially in critical recording conditions.
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Energy and Quality Evaluation for Compressive Sensing of Fetal Electrocardiogram Signals. SENSORS 2016; 17:s17010009. [PMID: 28025510 PMCID: PMC5298582 DOI: 10.3390/s17010009] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/19/2016] [Revised: 12/12/2016] [Accepted: 12/14/2016] [Indexed: 11/22/2022]
Abstract
This manuscript addresses the problem of non-invasive fetal Electrocardiogram (ECG) signal acquisition with low power/low complexity sensors. A sensor architecture using the Compressive Sensing (CS) paradigm is compared to a standard compression scheme using wavelets in terms of energy consumption vs. reconstruction quality, and, more importantly, vs. performance of fetal heart beat detection in the reconstructed signals. We show in this paper that a CS scheme based on reconstruction with an over-complete dictionary has similar reconstruction quality to one based on wavelet compression. We also consider, as a more important figure of merit, the accuracy of fetal beat detection after reconstruction as a function of the sensor power consumption. Experimental results with an actual implementation in a commercial device show that CS allows significant reduction of energy consumption in the sensor node, and that the detection performance is comparable to that obtained from original signals for compression ratios up to about 75%.
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Piekarski E, Chitiboi T, Ramb R, Feng L, Axel L. Use of self-gated radial cardiovascular magnetic resonance to detect and classify arrhythmias (atrial fibrillation and premature ventricular contraction). J Cardiovasc Magn Reson 2016; 18:83. [PMID: 27884152 PMCID: PMC5123392 DOI: 10.1186/s12968-016-0306-6] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2016] [Accepted: 11/03/2016] [Indexed: 11/15/2023] Open
Abstract
BACKGROUND Arrhythmia can significantly alter the image quality of cardiovascular magnetic resonance (CMR); automatic detection and sorting of the most frequent types of arrhythmias during the CMR acquisition could potentially improve image quality. New CMR techniques, such as non-Cartesian CMR, can allow self-gating: from cardiac motion-related signal changes, we can detect cardiac cycles without an electrocardiogram. We can further use this data to obtain a surrogate for RR intervals (valley intervals: VV). Our purpose was to evaluate the feasibility of an automated method for classification of non-arrhythmic (NA) (regular cycles) and arrhythmic patients (A) (irregular cycles), and for sorting of common arrhythmia patterns between atrial fibrillation (AF) and premature ventricular contraction (PVC), using the cardiac motion-related signal obtained during self-gated free-breathing radial cardiac cine CMR with compressed sensing reconstruction (XD-GRASP). METHODS One hundred eleven patients underwent cardiac XD-GRASP CMR between October 2015 and February 2016; 33 were included for retrospective analysis with the proposed method (6 AF, 8 PVC, 19 NA; by recent ECG). We analyzed the VV, using pooled statistics (histograms) and sequential analysis (Poincaré plots), including the median (medVV), the weighted mean (meanVV), the total number of VV values (VVval), and the total range (VVTR) and half range (VVHR) of the cumulative frequency distribution of VV, including the median to half range (medVV/VVHR) and the half range to total range (VVHR/VVTR) ratios. We designed a simple algorithm for using the VV results to differentiate A from NA, and AF from PVC. RESULTS Between NA and A, meanVV, VVval, VVTR, VVHR, medVV/VVHR and VVHR/VVTR ratios were significantly different (p values = 0.00014, 0.0027, 0.000028, 5×10-9, 0.002, respectively). Between AF and PVC, meanVV, VVval and medVV/VVHR ratio were significantly different (p values = 0.018, 0.007, 0.044, respectively). Using our algorithm, sensitivity, specificity, and accuracy were 93 %, 95 % and 94 % to discriminate between NA and A, and 83 %, 71 %, and 77 % to discriminate between AF and PVC, respectively; areas under the ROC curve were 0.93 and 0.89. CONCLUSIONS Our study shows we can reliably detect arrhythmias and differentiate AF from PVC, using self-gated cardiac cine XD-GRASP CMR.
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Affiliation(s)
- Eve Piekarski
- Department of Radiology, The Center for Advanced Imaging Innovation and Research (CAI2R), New York University School of Medicine, 660 First Ave, New York, NY USA
- Department of Radiology, Bernard and Irene Schwartz Center for Biomedical Imaging, New York University School of Medicine, New York, NY USA
| | - Teodora Chitiboi
- Department of Radiology, The Center for Advanced Imaging Innovation and Research (CAI2R), New York University School of Medicine, 660 First Ave, New York, NY USA
- Department of Radiology, Bernard and Irene Schwartz Center for Biomedical Imaging, New York University School of Medicine, New York, NY USA
| | - Rebecca Ramb
- Department of Radiology, The Center for Advanced Imaging Innovation and Research (CAI2R), New York University School of Medicine, 660 First Ave, New York, NY USA
- Department of Radiology, Bernard and Irene Schwartz Center for Biomedical Imaging, New York University School of Medicine, New York, NY USA
| | - Li Feng
- Department of Radiology, The Center for Advanced Imaging Innovation and Research (CAI2R), New York University School of Medicine, 660 First Ave, New York, NY USA
- Department of Radiology, Bernard and Irene Schwartz Center for Biomedical Imaging, New York University School of Medicine, New York, NY USA
- Sackler Institute of Graduate Biomedical Sciences, New York University School of Medicine, New York, NY USA
| | - Leon Axel
- Department of Radiology, The Center for Advanced Imaging Innovation and Research (CAI2R), New York University School of Medicine, 660 First Ave, New York, NY USA
- Department of Radiology, Bernard and Irene Schwartz Center for Biomedical Imaging, New York University School of Medicine, New York, NY USA
- Sackler Institute of Graduate Biomedical Sciences, New York University School of Medicine, New York, NY USA
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