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Marvin Tan XH, Wang Y, Zhu X, Mendes FN, Chung PS, Chow YT, Man T, Lan H, Lin YJ, Zhang X, Zhang X, Nguyen T, Ardehali R, Teitell MA, Deb A, Chiou PY. Massive field-of-view sub-cellular traction force videography enabled by Single-Pixel Optical Tracers (SPOT). Biosens Bioelectron 2024; 258:116318. [PMID: 38701538 DOI: 10.1016/j.bios.2024.116318] [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: 02/07/2024] [Revised: 04/15/2024] [Accepted: 04/17/2024] [Indexed: 05/05/2024]
Abstract
We report a massive field-of-view and high-speed videography platform for measuring the sub-cellular traction forces of more than 10,000 biological cells over 13 mm2 at 83 frames per second. Our Single-Pixel Optical Tracers (SPOT) tool uses 2-dimensional diffraction gratings embedded into a soft substrate to convert cells' mechanical traction force into optical colors detectable by a video camera. The platform measures the sub-cellular traction forces of diverse cell types, including tightly connected tissue sheets and near isolated cells. We used this platform to explore the mechanical wave propagation in a tightly connected sheet of Neonatal Rat Ventricular Myocytes (NRVMs) and discovered that the activation time of some tissue regions are heterogeneous from the overall spiral wave behavior of the cardiac wave.
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Affiliation(s)
- Xing Haw Marvin Tan
- Department of Mechanical and Aerospace Engineering, University of California Los Angeles, Westwood Plaza, Los Angeles, CA, 90095, United States; Department of Bioengineering, University of California Los Angeles, Westwood Plaza, Los Angeles, CA, 90095, United States; Department of Electronics and Photonics, Institute of High Performance Computing (IHPC), Agency for Science, Technology and Research (A*STAR), 1 Fusionopolis Way, 138632, Singapore
| | - Yijie Wang
- Division of Cardiology, Department of Medicine, David Geffen School of Medicine, University of California Los Angeles, 675 Charles E Young Dr S, Los Angeles, CA, 90095, United States
| | - Xiongfeng Zhu
- Department of Mechanical and Aerospace Engineering, University of California Los Angeles, Westwood Plaza, Los Angeles, CA, 90095, United States
| | - Felipe Nanni Mendes
- Department of Mechanical and Aerospace Engineering, University of California Los Angeles, Westwood Plaza, Los Angeles, CA, 90095, United States
| | - Pei-Shan Chung
- Department of Mechanical and Aerospace Engineering, University of California Los Angeles, Westwood Plaza, Los Angeles, CA, 90095, United States; Department of Bioengineering, University of California Los Angeles, Westwood Plaza, Los Angeles, CA, 90095, United States
| | - Yu Ting Chow
- Department of Mechanical and Aerospace Engineering, University of California Los Angeles, Westwood Plaza, Los Angeles, CA, 90095, United States
| | - Tianxing Man
- Department of Mechanical and Aerospace Engineering, University of California Los Angeles, Westwood Plaza, Los Angeles, CA, 90095, United States
| | - Hsin Lan
- Department of Mechanical and Aerospace Engineering, University of California Los Angeles, Westwood Plaza, Los Angeles, CA, 90095, United States
| | - Yen-Ju Lin
- Department of Electrical and Computer Engineering, University of California at Los Angeles, Westwood Plaza, Los Angeles, CA, 90095, United States
| | - Xiang Zhang
- Department of Mechanical and Aerospace Engineering, University of California Los Angeles, Westwood Plaza, Los Angeles, CA, 90095, United States
| | - Xiaohe Zhang
- Department of Mathematics, University of California Los Angeles, 520 Portola Plaza, Los Angeles, CA, 90095, United States
| | - Thang Nguyen
- Department of Bioengineering, University of California Los Angeles, Westwood Plaza, Los Angeles, CA, 90095, United States
| | - Reza Ardehali
- Division of Cardiology, Department of Medicine, David Geffen School of Medicine, University of California Los Angeles, 675 Charles E Young Dr S, Los Angeles, CA, 90095, United States
| | - Michael A Teitell
- Department of Bioengineering, University of California Los Angeles, Westwood Plaza, Los Angeles, CA, 90095, United States; Department of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California Los Angeles, 675 Charles E Young Dr S, Los Angeles, CA, 90095, United States
| | - Arjun Deb
- Division of Cardiology, Department of Medicine, David Geffen School of Medicine, University of California Los Angeles, 675 Charles E Young Dr S, Los Angeles, CA, 90095, United States
| | - Pei-Yu Chiou
- Department of Mechanical and Aerospace Engineering, University of California Los Angeles, Westwood Plaza, Los Angeles, CA, 90095, United States; Department of Bioengineering, University of California Los Angeles, Westwood Plaza, Los Angeles, CA, 90095, United States.
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Jaeger KM, Nissen M, Rahm S, Titzmann A, Fasching PA, Beilner J, Eskofier BM, Leutheuser H. Power-MF: robust fetal QRS detection from non-invasive fetal electrocardiogram recordings. Physiol Meas 2024; 45:055009. [PMID: 38722552 DOI: 10.1088/1361-6579/ad4952] [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: 07/20/2023] [Accepted: 05/09/2024] [Indexed: 05/22/2024]
Abstract
Objective.Perinatal asphyxia poses a significant risk to neonatal health, necessitating accurate fetal heart rate monitoring for effective detection and management. The current gold standard, cardiotocography, has inherent limitations, highlighting the need for alternative approaches. The emerging technology of non-invasive fetal electrocardiography shows promise as a new sensing technology for fetal cardiac activity, offering potential advancements in the detection and management of perinatal asphyxia. Although algorithms for fetal QRS detection have been developed in the past, only a few of them demonstrate accurate performance in the presence of noise and artifacts.Approach.In this work, we proposePower-MF, a new algorithm for fetal QRS detection combining power spectral density and matched filter techniques. We benchmarkPower-MFagainst three open-source algorithms on two recently published datasets (Abdominal and Direct Fetal ECG Database: ADFECG, subsets B1 Pregnancy and B2 Labour; Non-invasive Multimodal Foetal ECG-Doppler Dataset for Antenatal Cardiology Research: NInFEA).Main results.Our results show thatPower-MFoutperforms state-of-the-art algorithms on ADFECG (B1 Pregnancy: 99.5% ± 0.5% F1-score, B2 Labour: 98.0% ± 3.0% F1-score) and on NInFEA in three of six electrode configurations by being more robust against noise.Significance.Through this work, we contribute to improving the accuracy and reliability of fetal cardiac monitoring, an essential step toward early detection of perinatal asphyxia with the long-term goal of reducing costs and making prenatal care more accessible.
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Affiliation(s)
- Katharina M Jaeger
- Friedrich-Alexander-Universitat Erlangen-Nürnberg, Machine Learning and Data Analytics Lab, Carl-Thiersch-Straße 2b, 91052 Erlangen, Germany
| | - Michael Nissen
- Friedrich-Alexander-Universitat Erlangen-Nürnberg, Machine Learning and Data Analytics Lab, Carl-Thiersch-Straße 2b, 91052 Erlangen, Germany
| | - Simone Rahm
- Friedrich-Alexander-Universitat Erlangen-Nürnberg, Machine Learning and Data Analytics Lab, Carl-Thiersch-Straße 2b, 91052 Erlangen, Germany
| | - Adriana Titzmann
- Department of Gynecology and Obstetrics, Erlangen University Hospital, Universitätsstraße 21-23, 91054 Erlangen, Germany
| | - Peter A Fasching
- Department of Gynecology and Obstetrics, Erlangen University Hospital, Universitätsstraße 21-23, 91054 Erlangen, Germany
| | - Janina Beilner
- Friedrich-Alexander-Universitat Erlangen-Nürnberg, Machine Learning and Data Analytics Lab, Carl-Thiersch-Straße 2b, 91052 Erlangen, Germany
| | - Bjoern M Eskofier
- Friedrich-Alexander-Universitat Erlangen-Nürnberg, Machine Learning and Data Analytics Lab, Carl-Thiersch-Straße 2b, 91052 Erlangen, Germany
- Translational Digital Health Group, Institute of AI for Health, Helmholtz Zentrum München-German Research Center for Environmental Health, Neuherberg, Germany
| | - Heike Leutheuser
- Friedrich-Alexander-Universitat Erlangen-Nürnberg, Machine Learning and Data Analytics Lab, Carl-Thiersch-Straße 2b, 91052 Erlangen, Germany
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Mekhfioui M, Benahmed A, Chebak A, Elgouri R, Hlou L. The Development and Implementation of Innovative Blind Source Separation Techniques for Real-Time Extraction and Analysis of Fetal and Maternal Electrocardiogram Signals. Bioengineering (Basel) 2024; 11:512. [PMID: 38790378 PMCID: PMC11117810 DOI: 10.3390/bioengineering11050512] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2024] [Revised: 04/18/2024] [Accepted: 04/19/2024] [Indexed: 05/26/2024] Open
Abstract
This article presents an innovative approach to analyzing and extracting electrocardiogram (ECG) signals from the abdomen and thorax of pregnant women, with the primary goal of isolating fetal ECG (fECG) and maternal ECG (mECG) signals. To resolve the difficulties related to the low amplitude of the fECG, various noise sources during signal acquisition, and the overlapping of R waves, we developed a new method for extracting ECG signals using blind source separation techniques. This method is based on independent component analysis algorithms to detect and accurately extract fECG and mECG signals from abdomen and thorax data. To validate our approach, we carried out experiments using a real and reliable database for the evaluation of fECG extraction algorithms. Moreover, to demonstrate real-time applicability, we implemented our method in an embedded card linked to electronic modules that measure blood oxygen saturation (SpO2) and body temperature, as well as the transmission of data to a web server. This enables us to present all information related to the fetus and its mother in a mobile application to assist doctors in diagnosing the fetus's condition. Our results demonstrate the effectiveness of our approach in isolating fECG and mECG signals under difficult conditions and also calculating different heart rates (fBPM and mBPM), which offers promising prospects for improving fetal monitoring and maternal healthcare during pregnancy.
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Affiliation(s)
- Mohcin Mekhfioui
- Green Tech Institute (GTI), Mohammed VI Polytechnic University, Benguerir 43150, Morocco
- Faculty of Science, University Ibn Tofail, Kenitra 14000, Morocco
| | - Aziz Benahmed
- ERSC Team, Mohammadia Engineering School, Mohammed V University, Rabat 10106, Morocco
| | - Ahmed Chebak
- Green Tech Institute (GTI), Mohammed VI Polytechnic University, Benguerir 43150, Morocco
| | - Rachid Elgouri
- Laboratory of Electrical Engineering and Telecommunications Systems, ENSA, Ibn Tofail University, Kenitra 14000, Morocco
| | - Laamari Hlou
- Faculty of Science, University Ibn Tofail, Kenitra 14000, Morocco
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Hussain NM, O'Halloran M, McDermott B, Elahi MA. Fetal monitoring technologies for the detection of intrapartum hypoxia - challenges and opportunities. Biomed Phys Eng Express 2024; 10:022002. [PMID: 38118183 DOI: 10.1088/2057-1976/ad17a6] [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: 05/13/2023] [Accepted: 12/20/2023] [Indexed: 12/22/2023]
Abstract
Intrapartum fetal hypoxia is related to long-term morbidity and mortality of the fetus and the mother. Fetal surveillance is extremely important to minimize the adverse outcomes arising from fetal hypoxia during labour. Several methods have been used in current clinical practice to monitor fetal well-being. For instance, biophysical technologies including cardiotocography, ST-analysis adjunct to cardiotocography, and Doppler ultrasound are used for intrapartum fetal monitoring. However, these technologies result in a high false-positive rate and increased obstetric interventions during labour. Alternatively, biochemical-based technologies including fetal scalp blood sampling and fetal pulse oximetry are used to identify metabolic acidosis and oxygen deprivation resulting from fetal hypoxia. These technologies neither improve clinical outcomes nor reduce unnecessary interventions during labour. Also, there is a need to link the physiological changes during fetal hypoxia to fetal monitoring technologies. The objective of this article is to assess the clinical background of fetal hypoxia and to review existing monitoring technologies for the detection and monitoring of fetal hypoxia. A comprehensive review has been made to predict fetal hypoxia using computational and machine-learning algorithms. The detection of more specific biomarkers or new sensing technologies is also reviewed which may help in the enhancement of the reliability of continuous fetal monitoring and may result in the accurate detection of intrapartum fetal hypoxia.
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Affiliation(s)
- Nadia Muhammad Hussain
- Discipline of Electrical & Electronic Engineering, University of Galway, Ireland
- Translational Medical Device Lab, Lambe Institute for Translational Research, University Hospital Galway, Ireland
| | - Martin O'Halloran
- Discipline of Electrical & Electronic Engineering, University of Galway, Ireland
- Translational Medical Device Lab, Lambe Institute for Translational Research, University Hospital Galway, Ireland
| | - Barry McDermott
- Translational Medical Device Lab, Lambe Institute for Translational Research, University Hospital Galway, Ireland
- College of Medicine, Nursing & Health Sciences, University of Galway, Ireland
| | - Muhammad Adnan Elahi
- Discipline of Electrical & Electronic Engineering, University of Galway, Ireland
- Translational Medical Device Lab, Lambe Institute for Translational Research, University Hospital Galway, Ireland
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Jaros R, Tomicova E, Martinek R. Template subtraction based methods for non-invasive fetal electrocardiography extraction. Sci Rep 2024; 14:630. [PMID: 38182757 PMCID: PMC10770155 DOI: 10.1038/s41598-024-51213-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2023] [Accepted: 01/02/2024] [Indexed: 01/07/2024] Open
Abstract
Assessment of fetal heart rate (fHR) through non-invasive fetal electrocardiogram (fECG) is challenging task. This study compares the performance of five template subtraction (TS) methods on Labor (12 5-min recordings) and Pregnancy datasets (10 20-min recordings). The methods include TS without adaptation, TS using singular value decomposition (TS[Formula: see text]), TS using linear prediction (TS[Formula: see text]), TS using scaling factor (TS[Formula: see text]), and sequential analysis (SA). The influence of the chosen maternal wavelet for the continuous wavelet transform (CWT) detector is also compared. The F1 score was used to measure performance. Each recording in both datasets consisted of four signals, resulting in a total comparison of 88 signals for the TS-based methods. The study reported the following results: F1 = 95.71% with TS, F1 = 95.93% with TS[Formula: see text], F1 = 95.30% with TS[Formula: see text], F1 = 95.82% with TS[Formula: see text], and F1 = 95.99% with SA. The study identified gaus3 as the suitable maternal wavelet for fetal R-peak detection using the CWT detector. Furthermore, the study classified signals from the tested datasets into categories of high, medium, and low quality, providing valuable insights for subsequent fECG signal extraction. This research contributes to advancing the understanding of non-invasive fECG signal processing and lays the groundwork for improving fetal monitoring in clinical settings.
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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 2172/15, 708 00, Ostrava, Czechia.
| | - Eva Tomicova
- Department of Cybernetics and Biomedical Engineering, Faculty of Electrical Engineering and Computer Science, VSB-Technical University of Ostrava, 17. listopadu 2172/15, 708 00, Ostrava, Czechia
| | - Radek Martinek
- Department of Cybernetics and Biomedical Engineering, Faculty of Electrical Engineering and Computer Science, VSB-Technical University of Ostrava, 17. listopadu 2172/15, 708 00, Ostrava, Czechia
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Zhong W, Luo J, Du W. Deep learning with fetal ECG recognition. Physiol Meas 2023; 44:115006. [PMID: 37939396 DOI: 10.1088/1361-6579/ad0ab7] [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: 06/25/2023] [Accepted: 11/07/2023] [Indexed: 11/10/2023]
Abstract
Objective.Independent component analysis (ICA) is widely used in the extraction of fetal ECG (FECG). However, the amplitude, order, and positive or negative values of the ICA results are uncertain. The main objective is to present a novel approach to FECG recognition by using a deep learning strategy.Approach.A cross-domain consistent convolutional neural network (CDC-Net) is developed for the task of FECG recognition. The output of the ICA algorithm is used as input to the CDC-Net and the CDC-Net identifies which channel's signal is the target FECG.Main results.Signals from two databases are used to test the efficiency of the proposed method. The proposed deep learning method exhibits good performance on FECG recognition. Specifically, the Precision, Recall and F1-score of the proposed method on the ADFECGDB database are 91.69%, 91.37% and 91.52%, respectively. The Precision, Recall and F1-score of the proposed method on the Daisy database are 97.85%, 97.42% and 97.63%, respectively.Significance. This study is a proof of concept that the proposed method can automatically recognize the FECG signals in multi-channel ECG data. The development of FECG recognition technology contributes to automated FECG monitoring.
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Affiliation(s)
- Wei Zhong
- Guangdong Police College, Guangzhou, 510000, People's Republic of China
| | - Jiahui Luo
- Guangdong Police College, Guangzhou, 510000, People's Republic of China
| | - Wei Du
- Guangdong Police College, Guangzhou, 510000, People's Republic of China
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Marvin Tan XH, Wang Y, Zhu X, Mendes FN, Chung PS, Chow YT, Man T, Lan H, Lin YJ, Zhang X, Zhang X, Nguyen T, Ardehali R, Teitell MA, Deb A, Chiou PY. Massively Concurrent Sub-Cellular Traction Force Videography enabled by Single-Pixel Optical Tracers (SPOTs). BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.07.25.550454. [PMID: 37546726 PMCID: PMC10402113 DOI: 10.1101/2023.07.25.550454] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/08/2023]
Abstract
We report a large field-of-view and high-speed videography platform for measuring the sub-cellular traction forces of more than 10,000 biological cells over 13mm 2 at 83 frames per second. Our Single-Pixel Optical Tracers (SPOT) tool uses 2-dimensional diffraction gratings embedded into a soft substrate to convert cells' mechanical traction stress into optical colors detectable by a video camera. The platform measures the sub-cellular traction forces of diverse cell types, including tightly connected tissue sheets and near isolated cells. We used this platform to explore the mechanical wave propagation in a tightly connected sheet of Neonatal Rat Ventricular Myocytes (NRVMs) and discovered that the activation time of some tissue regions are heterogeneous from the overall spiral wave behavior of the cardiac wave. One-Sentence Summary An optical platform for fast, concurrent measurements of cell mechanics at 83 frames per second, over a large area of 13mm 2 .
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Kasap B, Vali K, Qian W, Saffarpour M, Fowler R, Ghiasi S. Robust Fetal Heart Rate Tracking through Fetal Electrocardiography (ECG) and Photoplethysmography (PPG) Fusion . ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2023; 2023:1-4. [PMID: 38083436 DOI: 10.1109/embc40787.2023.10341068] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/18/2023]
Abstract
Fetal electrocardiogram (fECG) or photoplethysmogram (fPPG) devices are being developed for fetal heart rate (FHR) monitoring. However, deep tissue sensing is challenged by low fetal signal-to-noise ratio (SNR). Data quality is easily degraded by motion, or interference from maternal tissues and data losses can happen due to communication faults. In this paper, we propose to combine fECG and fPPG measurements in order to increase robustness against such dynamic challenges and increase FHR estimation accuracy. To the author's knowledge the fusion of two sensory data types (fECG, fPPG) has not been investigated for FHR tracking purposes in the literature. The proposed methods are evaluated on real-world data captured from gold-standard large pregnant animal experiments. A particle filtering algorithm with sensor fusion in the measurement likelihood, called KUBAI, is used to estimate FHR. Fusion of PPG&ECG data resulted in 36.6% improvement in root-mean-square-error (RMSE) and 20.3% improvement in R2 correlation between estimated and reference FHR values compared to single sensor-type (PPG-only or ECG-only) data. We demonstrate that using different types of sensory data improves the robustness and accuracy of FHR tracking.
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Jaros R, Barnova K, Vilimkova Kahankova R, Pelisek J, Litschmannova M, Martinek R. Independent component analysis algorithms for non-invasive fetal electrocardiography. PLoS One 2023; 18:e0286858. [PMID: 37279195 PMCID: PMC10243647 DOI: 10.1371/journal.pone.0286858] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2023] [Accepted: 05/24/2023] [Indexed: 06/08/2023] Open
Abstract
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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Affiliation(s)
- Rene Jaros
- 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
| | - Radana Vilimkova Kahankova
- Department of Cybernetics and Biomedical Engineering, Faculty of Electrical Engineering and Computer Science, VSB–Technical University of Ostrava, Ostrava, Czechia
| | - Jan Pelisek
- Department of Cybernetics and Biomedical Engineering, Faculty of Electrical Engineering and Computer Science, VSB–Technical University of Ostrava, Ostrava, Czechia
| | - Martina Litschmannova
- Department of Applied Mathematics, 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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Alim A, Imtiaz MH. Wearable Sensors for the Monitoring of Maternal Health-A Systematic Review. SENSORS (BASEL, SWITZERLAND) 2023; 23:2411. [PMID: 36904615 PMCID: PMC10007071 DOI: 10.3390/s23052411] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/10/2023] [Revised: 02/17/2023] [Accepted: 02/19/2023] [Indexed: 06/18/2023]
Abstract
Maternal health includes health during pregnancy and childbirth. Each stage during pregnancy should be a positive experience, ensuring that women and their babies reach their full potential in health and well-being. However, this cannot always be achieved. According to UNFPA (United Nations Population Fund), approximately 800 women die every day from avoidable causes related to pregnancy and childbirth, so it is important to monitor mother and fetal health throughout the pregnancy. Many wearable sensors and devices have been developed to monitor both fetal and the mother's health and physical activities and reduce risk during pregnancy. Some wearables monitor fetal ECG or heart rate and movement, while others focus on the mother's health and physical activities. This study presents a systematic review of these analyses. Twelve scientific articles were reviewed to address three research questions oriented to (1) sensors and method of data acquisition; (2) processing methods of the acquired data; and (3) detection of the activities or movements of the fetus or the mother. Based on these findings, we discuss how sensors can help effectively monitor maternal and fetal health during pregnancy. We have observed that most of the wearable sensors were used in a controlled environment. These sensors need more testing in free-living conditions and to be employed for continuous monitoring before being recommended for mass implementation.
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Souriau R, Fontecave-Jallon J, Rivet B. Fetal heart rate monitoring by fusion of estimations from two modalities: A modified Viterbi’s algorithm. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2022.104405] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
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12
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Raj A, Brablik J, Kahankova R, Jaros R, Barnova K, Snasel V, Mirjalili S, Martinek R. Nature inspired method for noninvasive fetal ECG extraction. Sci Rep 2022; 12:20159. [PMID: 36418487 PMCID: PMC9684417 DOI: 10.1038/s41598-022-24733-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2022] [Accepted: 11/18/2022] [Indexed: 11/27/2022] Open
Abstract
This paper introduces a novel algorithm for effective and accurate extraction of non-invasive fetal electrocardiogram (NI-fECG). In NI-fECG based monitoring, the useful signal is measured along with other signals generated by the pregnant women's body, especially maternal electrocardiogram (mECG). These signals are more distinct in magnitude and overlap in time and frequency domains, making the fECG extraction extremely challenging. The proposed extraction method combines the Grey wolf algorithm (GWO) with sequential analysis (SA). This innovative combination, forming the GWO-SA method, optimises the parameters required to create a template that matches the mECG, which leads to an accurate elimination of the said signal from the input composite signal. The extraction system was tested on two databases consisting of real signals, namely, Labour and Pregnancy. The databases used to test the algorithms are available on a server at the generalist repositories (figshare) integrated with Matonia et al. (Sci Data 7(1):1-14, 2020). The results show that the proposed method extracts the fetal ECG signal with an outstanding efficacy. The efficacy of the results was evaluated based on accurate detection of the fQRS complexes. The parameters used to evaluate are as follows: accuracy (ACC), sensitivity (SE), positive predictive value (PPV), and F1 score. Due to the stochastic nature of the GWO algorithm, ten individual runs were performed for each record in the two databases to assure stability as well as repeatability. Using these parameters, for the Labour dataset, we achieved an average ACC of 94.60%, F1 of 96.82%, SE of 97.49%, and PPV of 98.96%. For the Pregnancy database, we achieved an average ACC of 95.66%, F1 of 97.44%, SE of 98.07%, and PPV of 97.44%. The obtained results show that the fHR related parameters were determined accurately for most of the records, outperforming the other state-of-the-art approaches. The poorer quality of certain signals have caused deviation from the estimated fHR for certain records in the databases. The proposed algorithm is compared with certain well established algorithms, and has proven to be accurate in its fECG extractions.
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Affiliation(s)
- Akshaya Raj
- grid.440850.d0000 0000 9643 2828Department of Cybernetics and Biomedical Engineering, Faculty of Electrical Engineering and Computer Science, VSB-Technical University of Ostrava, 17. listopadu, Ostrava, 708 00 Czechia
| | - Jindrich Brablik
- grid.440850.d0000 0000 9643 2828Department of Cybernetics and Biomedical Engineering, Faculty of Electrical Engineering and Computer Science, VSB-Technical University of Ostrava, 17. listopadu, Ostrava, 708 00 Czechia
| | - Radana Kahankova
- grid.440850.d0000 0000 9643 2828Department of Cybernetics and Biomedical Engineering, Faculty of Electrical Engineering and Computer Science, VSB-Technical University of Ostrava, 17. listopadu, Ostrava, 708 00 Czechia
| | - Rene Jaros
- grid.440850.d0000 0000 9643 2828Department of Cybernetics and Biomedical Engineering, Faculty of Electrical Engineering and Computer Science, VSB-Technical University of Ostrava, 17. listopadu, Ostrava, 708 00 Czechia
| | - Katerina Barnova
- grid.440850.d0000 0000 9643 2828Department of Cybernetics and Biomedical Engineering, Faculty of Electrical Engineering and Computer Science, VSB-Technical University of Ostrava, 17. listopadu, Ostrava, 708 00 Czechia
| | - Vaclav Snasel
- grid.440850.d0000 0000 9643 2828Department of Computer Science, Faculty of Electrical Engineering and Computer Science, VSB-Technical University of Ostrava, 17. listopadu, Ostrava, 708 00 Czechia
| | - Seyedali Mirjalili
- grid.449625.80000 0004 4654 2104Centre for Artificial Intelligence Research and Optimisation, Torrens University Australia, 90 Bowen Terrace, Brisbane, QLD 4006 Australia
| | - Radek Martinek
- grid.440850.d0000 0000 9643 2828Department of Cybernetics and Biomedical Engineering, Faculty of Electrical Engineering and Computer Science, VSB-Technical University of Ostrava, 17. listopadu, Ostrava, 708 00 Czechia
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Lafaye de Micheaux H, Resendiz M, Rivet B, Fontecave-Jallon J. Residual convolutional autoencoder combined with a non-negative matrix factorization to estimate fetal heart rate. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2022; 2022:1292-1295. [PMID: 36085674 DOI: 10.1109/embc48229.2022.9871887] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
The fetal heart rate (fHR) plays an important role in the determination of the good health of the fetus. Beside the traditional Doppler ultrasound technique, non-invasive fetal electrocardiography (fECG) has become an interesting alternative. However, extracting clean fECG from abdominal ECG (aECG) recordings is a challenging task due to the presence of the maternal ECG component and various noise sources. In this context, we propose a deep residual convolutional autoencoder network trained on synthetic aECG simulations followed by a transfer learning phase on real aECG recordings to extract the cleanest fECG. Afterwards, we propose to use a non-negative matrix factorization based approach on the obtained fECG to estimate the fHR. Our method is evaluated on three publicly available databases demonstrating that it can provide significant performance improvement against comparative methodologies. Clinical relevance- The presented method has the advantage of estimating the fetal heart rate from a single-channel abdominal electrocardiogram without prior knowledge on the noise sources nor the maternal R-peak locations.
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Souriau R, Fontecave-Jallon J, Rivet B. Fetal ECG denoising using dynamic time warping template subtraction. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2022; 2022:4978-4981. [PMID: 36086193 DOI: 10.1109/embc48229.2022.9871318] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
The analysis of the fetal electrocardiogram (ECG) requires to remove the mother ECG (mECG) from the abdominal ECG signals. Template subtraction is a method that consists in modeling and removing the mECG's mean period i.e. the signal waveform defined as the Euclidean mean of all periods. This mean period is then subtracted to all periods to extract the fetal ECG (fECG). Such a method is not accurate because each mECG's period is not correctly aligned with the mean period. We propose to take account of the diffeomorphism of each period to improve the precision of the model and remove the mECG more efficiently. The soft-dynamic time warping (DTW) algorithm is used to compute the mean mECG period and the alignment between the mean period and all periods. Our approach is compared to a classic template subtraction on synthetic and real databases. Results show that considering the dynamic time warping allows a better removal of the mECG. Clinical relevance - The template subtraction is modified in this paper to consider the time warping for each mother ECG's period in order to improve the fetal ECG extraction from the abdominal ECG.
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Rad EM, Ilali HM, Majnoon MT, Zeinaloo A. Mechanical QT and JT intervals by M-mode echocardiography: An extrapolation from the concurrent electrocardiographic tracings. Ann Pediatr Cardiol 2022; 15:364-373. [PMID: 36935820 PMCID: PMC10015387 DOI: 10.4103/apc.apc_169_21] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/23/2021] [Revised: 05/03/2022] [Accepted: 06/11/2022] [Indexed: 01/07/2023] Open
Abstract
Background Congenital long QT syndrome (CLQTS) is a life-threatening ion channelopathy leading to syncope and sudden death. Early diagnosis during the prenatal period and timely intervention can prevent sudden cardiac death and catastrophic consequences of this genetic ion channelopathy. Fetal magnetocardiography and fetal electrocardiography (ECG) enable the measurement of fetal QT and JT intervals, but their inherently technically challenging and/or resource-intensiveness nature preclude their routine clinical application. On the other hand, the high-temporal resolution of M-mode echocardiography makes it a well-suited and widely available modality for the measurement of cardiac events. Aims and Objectives We aimed to investigate the mechanical counterparts of the electrical QT and JT intervals on M-mode echocardiographic images of the tricuspid, mitral and aortic valves, and aortic wall. Methods We performed a prospective study on consecutive children referred to the outpatient pediatric cardiology clinic at a tertiary children's hospital. We defined M-mode echocardiographic landmark points on tracings of tricuspid annular planar systolic excursion, mitral and aortic valves, and aortic wall with simultaneous electrocardiographic recording. We measured the mean±SD of the absolute time difference and RR-adjusted time difference in cases with non-coincident ECG events and echocardiographic landmarks. Results Fifty healthy children were enrolled in the study. In 47 (94%) out of the 50 children, Q was coincident with the starting point of the tricuspid annular plane systolic excursion. In all children, the Q was coincident with the mid-point of the A-C line of the mitral valve. In 38 (76%) cases, there was a bump on the anterior wall of the aortic root immediately before the change in the slope of the aortic wall. This was coincident with the Q wave in 100% of cases. In all cases, the J point coincided with the point of acceleration of velocity on TAPSE. In all children, the J point coincided with the initial maximal opening of the aortic cusps. The end of the T wave occurred coincident with the peak of the tricuspid annular planar systolic excursion in 47 children (94%). In 48 children (96%), the end of the T wave coincided with the aortic cusps' closure point. Conclusions Based on our findings, we propose to measure the averaged mechanical QT and JT intervals by using an angled M-mode tracing of the aortic and mitral valve in five consecutive beats in the parasternal long-axis view. This is the first study on mechanical QT and JT intervals in healthy children. The study opens the horizons into the in-utero diagnosis of congenital long QT syndrome by measuring fetal QT and JT intervals using the widely available M-mode echocardiography.
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Affiliation(s)
- Elaheh Malakan Rad
- Children's Medical Center (Pediatric Center of Excellence), Tehran University of Medical Sciences, End of Keshavarz Boulevard, Tehran, Iran
| | - Hamidreza Mirzaei Ilali
- Children's Medical Center (Pediatric Center of Excellence), Tehran University of Medical Sciences, End of Keshavarz Boulevard, Tehran, Iran
| | - Mohammad-Taghi Majnoon
- Children's Medical Center (Pediatric Center of Excellence), Tehran University of Medical Sciences, End of Keshavarz Boulevard, Tehran, Iran
| | - Aliakbar Zeinaloo
- Children's Medical Center (Pediatric Center of Excellence), Tehran University of Medical Sciences, End of Keshavarz Boulevard, Tehran, Iran
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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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Zhong W, Zhao W. Fetal ECG extraction using short time Fourier transform and generative adversarial networks. Physiol Meas 2021; 42. [PMID: 34713820 DOI: 10.1088/1361-6579/ac2c5b] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2021] [Accepted: 10/01/2021] [Indexed: 02/04/2023]
Abstract
Objective.Fetal ECG (FECG) plays an important role in fetal monitoring. However, the abdominal ECG (AECG) recorded at the maternal abdomen is affected by various noises, making the extraction of FECG a challenging task. The main objective is to present a novel approach to FECG extraction using short time Fourier transform (STFT) and generative adversarial networks (GAN).Methods.Firstly, the AECG signals are transformed from one-dimensional (1D) time domain to two-dimensional (2D) time-frequency domain by using the STFT. Secondly, the 2D-STFT coefficients of FECG are estimated by the GAN model in the time-frequency domain. Finally, after the inverse STFT, the FECG can be reconstructed in the time domain.Main results.Experimental results on two databases demonstrate the effectiveness of the proposed method. Specifically, the SE, PPV andF1of the proposed method on PCDB are 92.37 ± 3.78%, 93.69 ± 3.96% and 93.02 ± 3.81%, respectively. And the SE, PPV andF1on ADFECGDB are 90.32 ± 10.70%, 89.79 ± 9.26% and 90.05 ± 9.81%, respectively.Significance.Unlike the previous studies based on the elimination of maternal ECG in the 1D time domain, the novelty of the proposed method relies on extracting the FECG directly from the AECG in the 2D time-frequency domain. It sheds some light to the topic of FECG extraction.
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Affiliation(s)
- Wei Zhong
- Guangdong Police College, Guangzhou 510000, People's Republic of China
| | - Weibin Zhao
- Guangdong Police College, Guangzhou 510000, People's Republic of China
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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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Sulas E, Urru M, Tumbarello R, Raffo L, Sameni R, Pani D. A non-invasive multimodal foetal ECG-Doppler dataset for antenatal cardiology research. Sci Data 2021; 8:30. [PMID: 33500414 PMCID: PMC7838287 DOI: 10.1038/s41597-021-00811-3] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2020] [Accepted: 12/18/2020] [Indexed: 12/29/2022] Open
Abstract
Non-invasive foetal electrocardiography (fECG) continues to be an open topic for research. The development of standard algorithms for the extraction of the fECG from the maternal electrophysiological interference is limited by the lack of publicly available reference datasets that could be used to benchmark different algorithms while providing a ground truth for foetal heart activity when an invasive scalp lead is unavailable. In this work, we present the Non-Invasive Multimodal Foetal ECG-Doppler Dataset for Antenatal Cardiology Research (NInFEA), the first open-access multimodal early-pregnancy dataset in the field that features simultaneous non-invasive electrophysiological recordings and foetal pulsed-wave Doppler (PWD). The dataset is mainly conceived for researchers working on fECG signal processing algorithms. The dataset includes 60 entries from 39 pregnant women, between the 21st and 27th week of gestation. Each dataset entry comprises 27 electrophysiological channels (2048 Hz, 22 bits), a maternal respiration signal, synchronised foetal trans-abdominal PWD and clinical annotations provided by expert clinicians during signal acquisition. MATLAB snippets for data processing are also provided.
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Affiliation(s)
- Eleonora Sulas
- University of Cagliari, Department of Electrical and Electronic Engineering, Cagliari, 09123, Italy
| | - Monica Urru
- Brotzu Hospital, Pediatric Cardiology and Congenital Heart Disease Unit, Cagliari, 09134, Italy
| | - Roberto Tumbarello
- Brotzu Hospital, Pediatric Cardiology and Congenital Heart Disease Unit, Cagliari, 09134, Italy
| | - Luigi Raffo
- University of Cagliari, Department of Electrical and Electronic Engineering, Cagliari, 09123, Italy
| | - Reza Sameni
- Department of Biomedical Informatics, Emory University School of Medicine, Atlanta, GA, 30322, US
| | - Danilo Pani
- University of Cagliari, Department of Electrical and Electronic Engineering, Cagliari, 09123, Italy.
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