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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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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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3
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Non-invasive diagnosis of fetal arrhythmia based on multi-domain feature and hierarchical extreme learning machine. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2022.104191] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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Jilani D, Le T, Etchells T, Lau MP, Cao H. Lullaby: A Novel Algorithm to Extract Fetal QRS in Real Time Using Periodic Trend Feature. IEEE SENSORS LETTERS 2022; 6:7003204. [PMID: 37637479 PMCID: PMC10456990 DOI: 10.1109/lsens.2022.3200072] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/29/2023]
Abstract
Fetal heart rate (fHR) is an important indicator for monitoring of fetal cardiac health and development. The widely-used method based on ultrasound, however, is not continuous and often requires an expert to perform; thus, it is mostly used in clinics during checkups. The advances in wearable technology have paved the way for home assessment of fHR via the extraction of the mother's abdominal electrocardiogram (ECG) acquired by novel patches. Several methods have been developed for such; however, the computation is either too slow for real-time monitoring or too heavy to be performed in a wearable. In this work, we develop and validate the Lullaby algorithm - a novel method for fetal QRS extraction from aECG. The results showed that Lullaby is almost 7 times faster than existing methods with a better F1-score of 0.815, holding promise to transform perinatal monitoring.
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Affiliation(s)
- Daniel Jilani
- Department of Electrical Engineering, University of California Irvine, Irvine, CA 92697, USA
- Sensoriis, Inc., Edmonds, WA 98026, USA
| | - Tai Le
- Department of Biomedical Engineering, University of California Irvine, Irvine, CA 92697, USA
| | | | | | - Hung Cao
- Department of Electrical Engineering, University of California Irvine, Irvine, CA 92697, USA
- Sensoriis, Inc., Edmonds, WA 98026, USA
- Department of Biomedical Engineering, University of California Irvine, Irvine, CA 92697, USA
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Wang M, Yin X, Zhu Y, Hu J. Representation Learning and Pattern Recognition in Cognitive Biometrics: A Survey. SENSORS (BASEL, SWITZERLAND) 2022; 22:5111. [PMID: 35890799 PMCID: PMC9320620 DOI: 10.3390/s22145111] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/17/2022] [Revised: 07/01/2022] [Accepted: 07/05/2022] [Indexed: 01/27/2023]
Abstract
Cognitive biometrics is an emerging branch of biometric technology. Recent research has demonstrated great potential for using cognitive biometrics in versatile applications, including biometric recognition and cognitive and emotional state recognition. There is a major need to summarize the latest developments in this field. Existing surveys have mainly focused on a small subset of cognitive biometric modalities, such as EEG and ECG. This article provides a comprehensive review of cognitive biometrics, covering all the major biosignal modalities and applications. A taxonomy is designed to structure the corresponding knowledge and guide the survey from signal acquisition and pre-processing to representation learning and pattern recognition. We provide a unified view of the methodological advances in these four aspects across various biosignals and applications, facilitating interdisciplinary research and knowledge transfer across fields. Furthermore, this article discusses open research directions in cognitive biometrics and proposes future prospects for developing reliable and secure cognitive biometric systems.
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Affiliation(s)
- Min Wang
- School of Engineering and Information Technology, University of New South Wales, Canberra, ACT 2612, Australia; (M.W.); (X.Y.)
| | - Xuefei Yin
- School of Engineering and Information Technology, University of New South Wales, Canberra, ACT 2612, Australia; (M.W.); (X.Y.)
| | - Yanming Zhu
- School of Computer Science and Engineering, University of New South Wales, Sydney, NSW 2052, Australia;
| | - Jiankun Hu
- School of Engineering and Information Technology, University of New South Wales, Canberra, ACT 2612, Australia; (M.W.); (X.Y.)
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Fetal Electrocardiogram Signal Extraction Based on Fast Independent Component Analysis and Singular Value Decomposition. SENSORS 2022; 22:s22103705. [PMID: 35632114 PMCID: PMC9146186 DOI: 10.3390/s22103705] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/07/2022] [Revised: 05/06/2022] [Accepted: 05/09/2022] [Indexed: 02/04/2023]
Abstract
Fetal electrocardiograms (FECGs) provide important clinical information for early diagnosis and intervention. However, FECG signals are extremely weak and are greatly influenced by noises. FECG signal extraction and detection are still challenging. In this work, we combined the fast independent component analysis (FastICA) algorithm with singular value decomposition (SVD) to extract FECG signals. The improved wavelet mode maximum method was applied to detect QRS waves and ST segments of FECG signals. We used the abdominal and direct fetal ECG database (ADFECGDB) and the Cardiology Challenge Database (PhysioNet2013) to verify the proposed algorithm. The signal-to-noise ratio of the best channel signal reached 45.028 dB and the issue of missing waveforms was addressed. The sensitivity, positive predictive value and F1 score of fetal QRS wave detection were 96.90%, 98.23%, and 95.24%, respectively. The proposed algorithm may be used as a new method for FECG signal extraction and detection.
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Fetal heart rate estimation using fractional Fourier transform and wavelet analysis. Biocybern Biomed Eng 2021. [DOI: 10.1016/j.bbe.2021.09.006] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
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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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Alshebly Y, Nafea M. Isolation of Fetal ECG Signals from Abdominal ECG Using Wavelet Analysis. Ing Rech Biomed 2020. [DOI: 10.1016/j.irbm.2019.12.002] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
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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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Yuan L, Yuan Y, Zhou Z, Bai Y, Wu S. A Fetal ECG Monitoring System Based on the Android Smartphone. SENSORS 2019; 19:s19030446. [PMID: 30678252 PMCID: PMC6386851 DOI: 10.3390/s19030446] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/20/2018] [Revised: 01/18/2019] [Accepted: 01/21/2019] [Indexed: 11/16/2022]
Abstract
In this paper, a fetal electrocardiogram (ECG) monitoring system based on the Android smartphone was proposed. We designed a portable low-power fetal ECG collector, which collected maternal abdominal ECG signals in real time. The ECG data were sent to a smartphone client via Bluetooth. Smartphone app software was developed based on the Android system. The app integrated the fast fixed-point algorithm for independent component analysis (FastICA) and the sample entropy algorithm, for the sake of real-time extraction of fetal ECG signals from the maternal abdominal ECG signals. The fetal heart rate was computed using the extracted fetal ECG signals. Experimental results showed that the FastICA algorithm can extract a clear fetal ECG, and the sample entropy can correctly determine the channel where the fetal ECG is located. The proposed fetal ECG monitoring system may be feasible for non-invasive, real-time monitoring of fetal ECGs.
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Affiliation(s)
- Li Yuan
- College of Life Science and Bioengineering, Beijing University of Technology, Beijing 100124, China.
| | - Yanchao Yuan
- College of Life Science and Bioengineering, Beijing University of Technology, Beijing 100124, China.
| | - Zhuhuang Zhou
- College of Life Science and Bioengineering, Beijing University of Technology, Beijing 100124, China.
| | - Yanping Bai
- College of Life Science and Bioengineering, Beijing University of Technology, Beijing 100124, China.
| | - Shuicai Wu
- College of Life Science and Bioengineering, Beijing University of Technology, Beijing 100124, China.
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