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Yang X, Liu L, Yan Z, Yu J, Hu X, Yu X, Dong C, Chen J, Liu H, Yu Z, Deng X, Ni D, Gou Z, Huang X. Hierarchical online contrastive anomaly detection for fetal arrhythmia diagnosis in ultrasound. Med Image Anal 2024; 97:103229. [PMID: 38897033 DOI: 10.1016/j.media.2024.103229] [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: 07/19/2023] [Revised: 03/10/2024] [Accepted: 06/03/2024] [Indexed: 06/21/2024]
Abstract
Arrhythmia is a major cardiac abnormality in fetuses. Therefore, early diagnosis of arrhythmia is clinically crucial. Pulsed-wave Doppler ultrasound is a commonly used diagnostic tool for fetal arrhythmia. Its key step for diagnosis involves identifying adjacent measurable cardiac cycles (MCCs). As cardiac activity is complex and the experience of sonographers is often varied, automation can improve user-independence and diagnostic-validity. However, arrhythmias pose several challenges for automation because of complex waveform variations, which can cause major localization bias and missed or false detection of MCCs. Filtering out non-MCC anomalies is difficult because of large intra-class and small inter-class variations between MCCs and non-MCCs caused by agnostic morphological waveform variations. Moreover, rare arrhythmia cases are insufficient for classification algorithms to adequately learn discriminative features. Using only normal cases for training, we propose a novel hierarchical online contrastive anomaly detection (HOCAD) framework for arrhythmia diagnosis during test time. The contribution of this study is three-fold. First, we develop a coarse-to-fine framework inspired by hierarchical diagnostic logic, which can refine localization and avoid missed detection of MCCs. Second, we propose an online learning-based contrastive anomaly detection with two new anomaly scores, which can adaptively filter out non-MCC anomalies on a single image during testing. With these complementary efforts, we precisely determine MCCs for correct measurements and diagnosis. Third, to the best of our knowledge, this is the first reported study investigating intelligent diagnosis of fetal arrhythmia on a large-scale and multi-center ultrasound dataset. Extensive experiments on 3850 cases, including 266 cases covering three typical types of arrhythmias, demonstrate the effectiveness of the proposed framework.
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Affiliation(s)
- Xin Yang
- National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, School of Biomedical Engineering, Shenzhen University Medical School, Shenzhen University, Shenzhen, China, Shenzhen, China; Medical Ultrasound Image Computing (MUSIC) Laboratory, Shenzhen University, Shenzhen, China
| | - Lian Liu
- National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, School of Biomedical Engineering, Shenzhen University Medical School, Shenzhen University, Shenzhen, China, Shenzhen, China; Medical Ultrasound Image Computing (MUSIC) Laboratory, Shenzhen University, Shenzhen, China
| | - Zhongnuo Yan
- National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, School of Biomedical Engineering, Shenzhen University Medical School, Shenzhen University, Shenzhen, China, Shenzhen, China; Medical Ultrasound Image Computing (MUSIC) Laboratory, Shenzhen University, Shenzhen, China
| | - Junxuan Yu
- National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, School of Biomedical Engineering, Shenzhen University Medical School, Shenzhen University, Shenzhen, China, Shenzhen, China; Medical Ultrasound Image Computing (MUSIC) Laboratory, Shenzhen University, Shenzhen, China
| | - Xindi Hu
- Shenzhen RayShape Medical Technology Co., Ltd, Shenzhen, Guangdong, China
| | - Xuejuan Yu
- Department of Ultrasonography, Suzhou Xiangcheng People's Hospital, Suzhou, Jiangsu, China
| | - Caixia Dong
- Department of Ultrasonography, Wulin Hospital, Hangzhou, Zhejiang, China
| | - Ju Chen
- Department of Ultrasonography, Taicang First People's Hospital, Suzhou, Jiangsu, China
| | - Hongmei Liu
- Department of Ultrasonography, Panzhou Emerging Hospital, Panzhou, Guizhou, China
| | - Zhuan Yu
- Department of Ultrasonography, The Affiliated Hospital of Jiangsu University, Zhenjiang, Jiangsu, China
| | - Xuedong Deng
- Center for Medical Ultrasound, The Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou Municipal Hospital, Gusu School, Nanjing Medical University, Suzhou, Jiangsu, China
| | - Dong Ni
- National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, School of Biomedical Engineering, Shenzhen University Medical School, Shenzhen University, Shenzhen, China, Shenzhen, China; Medical Ultrasound Image Computing (MUSIC) Laboratory, Shenzhen University, Shenzhen, China
| | - Zhongshan Gou
- Center for Cardiovascular Disease, The Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou Municipal Hospital, Gusu School, Nanjing Medical University, Suzhou, Jiangsu, China.
| | - Xiaoqiong Huang
- National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, School of Biomedical Engineering, Shenzhen University Medical School, Shenzhen University, Shenzhen, China, Shenzhen, China; Medical Ultrasound Image Computing (MUSIC) Laboratory, Shenzhen University, Shenzhen, China.
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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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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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Paul EA, Cohen J, Geiger MK. Cardiac problems in the fetus: a review for pediatric providers. Curr Opin Pediatr 2023; 35:523-530. [PMID: 37466056 DOI: 10.1097/mop.0000000000001274] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 07/20/2023]
Abstract
PURPOSE OF REVIEW The aim of this study was to provide pediatric providers with a review of the diagnosis and management of fetal cardiac disease in the current era. RECENT FINDINGS Prenatal detection of congenital heart disease (CHD) has improved but is still imperfect. In experienced hands, fetal echocardiography can detect severe CHD as early as the first trimester and a majority of more subtle conditions in the second and third trimesters. Beyond detection, a prenatal diagnosis allows for lesion-specific counseling for families as well as for development of a multidisciplinary perinatal management plan, which may involve in-utero treatment. Given the diversity of cardiac diagnoses and the rarity of some, collaborative multicenter fetal cardiac research has gained momentum in recent years. SUMMARY Accurate diagnosis of fetal cardiac disease allows for appropriate counseling, pregnancy and delivery planning, and optimization of immediate neonatal care. There is potential for improving fetal CHD detection rates. Fetal interventions are available for certain conditions, and fetal and pediatric cardiac centers have developed management plans specific to the expected postnatal physiology.
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Affiliation(s)
- Erin A Paul
- Division of Pediatric Cardiology, Mount Sinai Kravis Children's Hospital, Icahn School of Medicine at Mount Sinai, New York, New York, USA
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Unsupervised Learning-Based Non-Invasive Fetal ECG Muti-Level Signal Quality Assessment. BIOENGINEERING (BASEL, SWITZERLAND) 2023; 10:bioengineering10010066. [PMID: 36671638 PMCID: PMC9854747 DOI: 10.3390/bioengineering10010066] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/11/2022] [Revised: 12/16/2022] [Accepted: 12/26/2022] [Indexed: 01/06/2023]
Abstract
OBJECTIVE To monitor fetal health and growth, fetal heart rate is a critical indicator. The non-invasive fetal electrocardiogram is a widely employed measurement for fetal heart rate estimation, which is extracted from the electrodes placed on the surface of the maternal abdomen. The qualities of the fetal ECG recordings, however, are frequently affected by the noises from various interference sources. In general, the fetal heart rate estimates are unreliable when low-quality fetal ECG signals are used for fetal heart rate estimation, which makes accurate fetal heart rate estimation a challenging task. So, the signal quality assessment for the fetal ECG records is an essential step before fetal heart rate estimation. In other words, some low-quality fetal ECG signal segments are supposed to be detected and removed by utilizing signal quality assessment, so as to improve the accuracy of fetal heart rate estimation. A few supervised learning-based fetal ECG signal quality assessment approaches have been introduced and shown to accurately classify high- and low-quality fetal ECG signal segments, but large fetal ECG datasets with quality annotation are required in these methods. Yet, the labeled fetal ECG datasets are limited. Proposed methods: An unsupervised learning-based multi-level fetal ECG signal quality assessment approach is proposed in this paper for identifying three levels of fetal ECG signal quality. We extracted some features associated with signal quality, including entropy-based features, statistical features, and ECG signal quality indices. Additionally, an autoencoder-based feature is calculated, which is related to the reconstruction error of the spectrograms generated from fetal ECG signal segments. The high-, medium-, and low-quality fetal ECG signal segments are classified by inputting these features into a self-organizing map. MAIN RESULTS The experimental results showed that our proposal achieved a weighted average F1-score of 90% in three-level fetal ECG signal quality classification. Moreover, with the acceptable removal of detected low-quality signal segments, the errors of fetal heart rate estimation were reduced to a certain extent.
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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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Ekici H, Ökmen F, İmamoğlu M, İmamoğlu AG, Ergenoğlu AM. Fetal arrhythmias: Ten years’ experience and review of the literature. Turk J Obstet Gynecol 2022; 19:302-307. [DOI: 10.4274/tjod.galenos.2022.61818] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022] Open
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Deep Compressive Sensing on ECG Signals with Modified Inception Block and LSTM. ENTROPY 2022; 24:e24081024. [PMID: 35893004 PMCID: PMC9394370 DOI: 10.3390/e24081024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/31/2022] [Revised: 07/20/2022] [Accepted: 07/23/2022] [Indexed: 12/04/2022]
Abstract
In practical electrocardiogram (ECG) monitoring, there are some challenges in reducing the data burden and energy costs. Therefore, compressed sensing (CS) which can conduct under-sampling and reconstruction at the same time is adopted in the ECG monitoring application. Recently, deep learning used in CS methods improves the reconstruction performance significantly and can removes of some of the constraints in traditional CS. In this paper, we propose a deep compressive-sensing scheme for ECG signals, based on modified-Inception block and long short-term memory (LSTM). The framework is comprised of four modules: preprocessing; compression; initial; and final reconstruction. We adaptively compressed the normalized ECG signals, sequentially using three convolutional layers, and reconstructed the signals with a modified Inception block and LSTM. We conducted our experiments on the MIT-BIH Arrhythmia Database and Non-Invasive Fetal ECG Arrhythmia Database to validate the robustness of our model, adopting Signal-to-Noise Ratio (SNR) and percentage Root-mean-square Difference (PRD) as the evaluation metrics. The PRD of our scheme was the lowest and the SNR was the highest at all of the sensing rates in our experiments on both of the databases, and when the sensing rate was higher than 0.5, the PRD was lower than 2%, showing significant improvement in reconstruction performance compared to the comparative methods. Our method also showed good recovering quality in the noisy data.
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Jaba Deva Krupa A, Dhanalakshmi S, Kumar R. Joint time-frequency analysis and non-linear estimation for fetal ECG extraction. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2022.103569] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
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10
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Sepahvand M, Abdali-Mohammadi F. A novel method for reducing arrhythmia classification from 12-lead ECG signals to single-lead ECG with minimal loss of accuracy through teacher-student knowledge distillation. Inf Sci (N Y) 2022. [DOI: 10.1016/j.ins.2022.01.030] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
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Keenan E, Karmakar CK, Udhayakumar RK, Brownfoot FC, Lakhno IV, Shulgin V, Behar JA, Palaniswami M. Detection of fetal arrhythmias in non-invasive fetal ECG recordings using data-driven entropy profiling. Physiol Meas 2022; 43. [PMID: 35073532 DOI: 10.1088/1361-6579/ac4e6d] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2021] [Accepted: 01/24/2022] [Indexed: 11/11/2022]
Abstract
Objective:Fetal arrhythmias are a life-threatening disorder occurring in up to 2% of pregnancies. If identified, many fetal arrhythmias can be effectively treated using anti-arrhythmic therapies. In this paper, we present a novel method of detecting fetal arrhythmias in short length non-invasive fetal electrocardiography (NI-FECG) recordings.Approach:Our method consists of extracting a fetal heart rate (FHR) time series from each NI-FECG recording and computing an entropy profile using a data-driven range of the entropy tolerance parameter r. To validate our approach, we apply our entropy profiling method to a large clinical data set of 318 NI-FECG recordings.Main Results:We demonstrate that our method (TotalSampEn) provides strong performance for classifying arrhythmic fetuses (AUC of 0.83) and outperforms entropy measures such as SampEn (AUC of 0.68) and FuzzyEn (AUC of 0.72). We also find that NI-FECG recordings incorrectly classified using the investigated entropy measures have significantly lower signal quality, and that excluding recordings of low signal quality (13.5% of recordings) increases the classification accuracy of TotalSampEn (AUC of 0.90).Significance:The superior performance of our approach enables automated detection of fetal arrhythmias and warrants further investigation in a prospective clinical trial.
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Affiliation(s)
- Emerson Keenan
- Department of Electrical and Electronic Engineering, The University of Melbourne, Grattan Street, Melbourne, Victoria, 3010, AUSTRALIA
| | - Chandan K Karmakar
- School of Information Technology, Deakin University, 1 Gheringhap Street, Geelong, Victoria, 3220, AUSTRALIA
| | | | - Fiona Claire Brownfoot
- Department of Obstetrics and Gynaecology, The University of Melbourne, Level 4, 163 Studley Road, Heidelberg, Victoria, 3084, AUSTRALIA
| | - Igor Victorovich Lakhno
- Obstetrics and Gynecology Department, Kharkiv Medical Academy of Postgraduate Education, 58 Amosova Street, Kharkiv, 61176, UKRAINE
| | - Vyacheslav Shulgin
- Aerospace Radio-Electronic Systems Department, National Aerospace University Kharkiv Aviation Institute, 17 Chkalova Street, Kharkiv, 61000, UKRAINE
| | - Joachim Abraham Behar
- Biomedical Engineering Faculty, Technion Israel Institute of Technology, Technion City, Haifa, 3200003, ISRAEL
| | - Marimuthu Palaniswami
- Department of Electrical and Electronic Engineering, The University of Melbourne, Grattan Street, Melbourne, Victoria, 3010, AUSTRALIA
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Chivers SC, Vasavan T, Nandi M, Hayes-Gill BR, Jayawardane IA, Simpson JM, Williamson C, Fifer WP, Lucchini M. Measurement of the cardiac time intervals of the fetal ECG utilising a computerised algorithm: A retrospective observational study. JRSM Cardiovasc Dis 2022; 11:20480040221096209. [PMID: 35574238 PMCID: PMC9102181 DOI: 10.1177/20480040221096209] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2022] [Revised: 04/04/2022] [Accepted: 04/05/2022] [Indexed: 11/16/2022] Open
Abstract
Objective Establish whether the reliable measurement of cardiac time intervals of the fetal ECG can be automated and to address whether this approach could be used to investigate large datasets. Design Retrospective observational study. Setting Teaching hospitals in London UK, Nottingham UK and New York USA. Participants Singleton pregnancies with no known fetal abnormality. Methods Archived fetal ECG's performed using the MonicaAN24 monitor. A single ECG (PQRST) complex was generated from 5000 signal-averaged beats and electrical cardiac time intervals measured in an automated way and manually. Main Outcome measure Validation of a newly developed algorithm to measure the cardiac time intervals of the fetal ECG. Results 188/236 (79.7%) subjects with fECGs of suitable signal:noise ratio were included for analysis comparing manual with automated measurement. PR interval was measured in 173/188 (92%), QRS complex in 170/188 (90%) and QT interval in 123/188 (65.4%). PR interval was 107.6 (12.07) ms [mean(SD)] manual vs 109.11 (14.7) ms algorithm. QRS duration was 54.72(6.35) ms manual vs 58.34(5.73) ms algorithm. QT-interval was 268.93 (21.59) ms manual vs 261.63 (36.16) ms algorithm. QTc was 407.5(32.71) ms manual vs 396.4 (54.78) ms algorithm. The QRS-duration increased with gestational age in both manual and algorithm measurements. Conclusion Accurate measurement of fetal ECG cardiac time intervals can be automated with potential application to interpretation of larger datasets.
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Affiliation(s)
- SC Chivers
- Department of Women and Children’s Health, King’s College London, London, UK
- Department of Fetal cardiology, Evelina London Children’s Hospital, London, UK
| | - T Vasavan
- Department of Women and Children’s Health, King’s College London, London, UK
| | - M Nandi
- School of Cancer and Pharmaceutical Sciences, King’s College London, London, UK
| | - BR Hayes-Gill
- Faculty of Engineering, University of Nottingham, Nottingham, UK
| | - IA Jayawardane
- Faculty of Engineering, University of Nottingham, Nottingham, UK
| | - JM Simpson
- Department of Fetal cardiology, Evelina London Children’s Hospital, London, UK
| | - C Williamson
- Department of Women and Children’s Health, King’s College London, London, UK
| | - WP Fifer
- Department of Pediatrics, Columbia University Medical Center, Morgan Stanley Children’s Hospital, New York, USA
- Department of Psychiatry, Columbia University, New York, USA
| | - M Lucchini
- Department of Pediatrics, Columbia University Medical Center, Morgan Stanley Children’s Hospital, New York, USA
- Department of Psychiatry, Columbia University, New York, USA
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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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Suganthy M, Joy SI, Anandan P. Detection of fetal arrhythmia by adaptive single channel electrocardiogram extraction. Phys Eng Sci Med 2021; 44:683-692. [PMID: 34170500 DOI: 10.1007/s13246-021-01016-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2020] [Accepted: 05/16/2021] [Indexed: 10/21/2022]
Abstract
Fetal arrhythmia, the abnormal heartbeat of a fetus is broadly classified as tachy arrhythmia (too fast > 160 beats/min) and brady arrhythmia (too slow < 120 beats/min). Detection of this irregular heart beat rhythm of the fetus during pregnancy is still a challenging task for the clinicians. Heart rate detection through electrocardiography has always been accurate for identifying cardiac defect in humans. Adult ECG has achieved several developments in the modern medicine whereas noninvasive fetal ECG (FECG) continues to be a big challenge. Automatic detection of fetal heart rate is vital for monitoring the unborn infant during pregnancy. The non-invasive placement of electrodes over the abdomen region of pregnant women records the ECG signal of both mother and fetus. The arrhythmia affected FECG signals (n = 14) are processed from the physionet database. This raw ECG signal is preprocessed using a Savitzky-Golay filter and symlet wavelet transform to remove the basic noises. Adaptive recursive least square filter is preferably chosen for extracting the FECG, using mother's thorax ECG as a reference. An accurate PQRST wave-shape of the FECG is required for the proper diagnosis of fetal cardiac defects. Using a single channel abdominal ECG signal, the proposed work generates extracted fetal ECG and an automated visual display of fetal heart rate. The presence of arrhythmia and fetal distress can be analyzed through fetal heart rate display and abnormal conductivity of PQRST wave respectively. We have analyzed fetal arrhythmias through ECG extraction and the same was compared with the echocardiograph results given by pediatric cardiologist. This study helps to identify the fetal distress at early gestational age that helps the obstetricians to make quick decisions before or immediately after delivery.
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Affiliation(s)
- M Suganthy
- Department of Electronics and Communication Engineering, Vel Tech Multi Tech Dr Rangarajan Dr Sakunthala Engineering College, Chennai, Tamil Nadu, India.
| | - S Immaculate Joy
- Department of Electronics and Communication Engineering, Vel Tech Multi Tech Dr Rangarajan Dr Sakunthala Engineering College, Chennai, Tamil Nadu, India
| | - P Anandan
- Department of Electronics and Communication Engineering, C. Abdul Hakeem College of Engineering and Technology, Melvishram, Tamil Nadu, India
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15
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Biloborodova T, Scislo L, Skarga-Bandurova I, Sachenko A, Molgad A, Povoroznjuk O, Yevsieiva Y. Fetal ECG signal processing and identification of hypoxic pregnancy conditions in-utero. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2021; 18:4919-4942. [PMID: 34198472 DOI: 10.3934/mbe.2021250] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
The fetal heart rate (fHR) variability and fetal electrocardiogram (fECG) are considered the most important sources of information about fetal wellbeing. Non-invasive fetal monitoring and analysis of fECG are paramount for clinical trials. They enable examining the fetal health status and detecting the heart rate changes associated with insufficient oxygenation to cut the likelihood of hypoxic fetal injury. Despite the fact that significant advances have been achieved in electrocardiography and adult ECG signal processing, the analysis of fECG is still in its infancy. Due to accurate fetal morphology extraction techniques have not been properly developed, many areas require particular attention on the way of fully understanding the changes in variability in the fetus and implementation of the non-invasive techniques suitable for remote home care which is increasingly in demand for high-risk pregnancy monitoring. In this paper, we introduce an integrated approach for fECG signal extraction and processing based on various methods for fetal welfare investigation and hypoxia risk estimation. To the best of our knowledge, this is the first attempt to introduce the auto-generated risk scoring in fECG to achieve early warning on fetus' safety and provide the physician with additional information about the possible fetal complications. The proposed method includes the following stages: fECG extraction, fHR and fetal heart rate variability (fHRV) calculation, hypoxia index (HI) evaluation and risk estimation. The extracted signals were examined by assessing Signal to Noise Ratio (SNR) and mean square error (MSE) values. The results obtained demonstrated great potential, but more profound research and validation, as well as a consistent clinical study, are needed before implementation into the hospital and at-home monitoring.
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Affiliation(s)
- Tetiana Biloborodova
- Department of Computer Science and Engineering, Volodymyr Dahl East Ukrainian National University, 43 Donetska Street, Severodonetsk 93400, Ukraine
| | - Lukasz Scislo
- Faculty of Electrical and Computer Engineering, Cracow University of Technology, Warszawska 24 Street, Cracow 31155, Poland
| | - Inna Skarga-Bandurova
- School of Engineering, Computing and Mathematics, Oxford Brookes University, Wheatley Campus, Oxford, OX33 1HX, UK
| | - Anatoliy Sachenko
- Department of Informatics, Kazimierz Pulaski University of Technology and Humanities in Radom, Radom 26600, Poland
- Research Institute for Intelligent Computer Systems, West Ukrainian National University, Ternopil 46009, Ukraine
| | - Agnieszka Molgad
- Department of Informatics, Kazimierz Pulaski University of Technology and Humanities in Radom, Radom 26600, Poland
| | - Oksana Povoroznjuk
- Department of Computer Engineering and Programming, National Technical University "Kharkiv Polytechnic Institute," 2 Kyrpychova Street, Kharkiv 61002, Ukraine
| | - Yelyzaveta Yevsieiva
- School of Medicine, V. N. Karazin Kharkiv National University, 4 Svobody Square, Kharkiv 61002, Ukraine
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16
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Jaba Deva Krupa A, Dhanalakshmi S, R K. An improved parallel sub-filter adaptive noise canceler for the extraction of fetal ECG. ACTA ACUST UNITED AC 2021; 66:503-514. [PMID: 33946135 DOI: 10.1515/bmt-2020-0313] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2020] [Accepted: 04/20/2021] [Indexed: 11/15/2022]
Abstract
Non-invasive extraction of fetal electrocardiogram (FECG) by processing the abdominal signals is emerging as a promising approach in the areas of obstetrics and gynecology. This paper presents a two-stage improved non-linear adaptive filter for FECG extraction. The reference input to the adaptive noise canceler (ANC) is first processed using an adaptive neuro-fuzzy inference system (ANFIS) to estimate the non-linear maternal component in abdominal signals. A parallel sub-filter (PSF) ANC is proposed to assess the fetal ECG from the abdominal signal. The PSF-ANC decomposes a single adaptive filter into multiple sub-filters to improve the convergence performance. The filter coefficients of PSF-ANC adaptively obtained using normalised least mean square algorithm by minimizing the mean square error. Different error and common error algorithms are proposed based on the computation of the error signal. A synthetic data from the FECG synthetic database is used to evaluate the convergence performance. Two real-time data from the Daisy database and the Non-invasive FECG database from Physionet are used to evaluate the proposed ANFIS-PSF's performance qualitative and quantitatively. The results justify the performance improvement of proposed ANFIS-PSF ANC compared to the state of art techniques. The proposed scheme achieves a sensitivity of 97.92%, 94.52% accuracy, a positive predictive value of 94.66%, and an F1 score of 96.12%.
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Affiliation(s)
- Abel Jaba Deva Krupa
- Faculty of Engineering and Technology, Department of ECE, College of Engineering and Technology, SRM Institute of Science and Technology, Kancheepuram,Tamil Nadu, India
| | - Samiappan Dhanalakshmi
- Faculty of Engineering and Technology, Department of ECE, College of Engineering and Technology, SRM Institute of Science and Technology, Kancheepuram,Tamil Nadu, India
| | - Kumar R
- Faculty of Engineering and Technology, Department of ECE, College of Engineering and Technology, SRM Institute of Science and Technology, Kancheepuram,Tamil Nadu, India
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17
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Lakhno IV. Non-invasive fetal electrocardiography ameliorates fetal outcome in chorioangioma: A case report. J Neonatal Perinatal Med 2021; 14:149-152. [PMID: 32007964 DOI: 10.3233/npm-190329] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
Abstract
Chorioangioma is a rare vascular placental tumour. Large chorioangiomas are known to have many maternal and perinatal complications. The case of placental chorioangioma detected via ultrasound is presented. This paper is focused on non-invasive fetal electrocardiography (NI-FECG) clinical use for diagnosing fetal anemia in chorioangioma.A 22-year-old primigravida was admitted to the department of fetomaternal medicine at 30 weeks of gestation. She had threatened preterm labour, polyhydramnios, and breech presentation. The large echogenic mass of 77 mm×66 mm×83 mm, located in the uterine bottom, protruded into the amniotic cavity, and connected to the marginal sinus of the placenta was determined via ultrasound. The sinusoidal pattern of beat-to-beat variations was diagnosed via NI-FECG in spite of normal blood flow velocity in the fetal middle cerebral artery. Therefore, NI-FECG was superior in the detection of fetal anemia. The female baby weighing 1500 g and measuring 42 cm in length, with a head circumference of 30 cm and Apgar score 3⟶5, was delivered by caesarean section. The baby had severe anemia and respiratory distress syndrome.NI-FECG was a good option for the clinician for the timely and accurate diagnosis of fetal anemia and fetal compromise in placental chorioangioma.
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Affiliation(s)
- Igor V Lakhno
- Kharkiv Medical Academy of Postgraduate Education, Kharkiv, Ukraine
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18
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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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19
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Keenan E, Udhayakumar RK, Karmakar CK, Brownfoot FC, Palaniswami M. Entropy Profiling for Detection of Fetal Arrhythmias in Short Length Fetal Heart Rate Recordings. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2020; 2020:621-624. [PMID: 33018064 DOI: 10.1109/embc44109.2020.9175892] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
The use of fetal heart rate (FHR) recordings for assessing fetal wellbeing is an integral component of obstetric care. Recently, non-invasive fetal electrocardiography (NI-FECG) has demonstrated utility for accurately diagnosing fetal arrhythmias via clinician interpretation. In this work, we introduce the use of data-driven entropy profiling to automatically detect fetal arrhythmias in short length FHR recordings obtained via NI-FECG. Using an open access dataset of 11 normal and 11 arrhythmic fetuses, our method (TotalSampEn) achieves excellent classification performance (AUC = 0.98) for detecting fetal arrhythmias in a short time window (i.e. under 10 minutes). We demonstrate that our method outperforms SampEn (AUC = 0.72) and FuzzyEn (AUC = 0.74) based estimates, proving its effectiveness for this task. The rapid detection provided by our approach may enable efficient triage of concerning FHR recordings for clinician review.
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20
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Vavrinsky E, Subjak J, Donoval M, Wagner A, Zavodnik T, Svobodova H. Application of Modern Multi-Sensor Holter in Diagnosis and Treatment. SENSORS (BASEL, SWITZERLAND) 2020; 20:E2663. [PMID: 32392697 PMCID: PMC7273207 DOI: 10.3390/s20092663] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/31/2020] [Revised: 05/04/2020] [Accepted: 05/04/2020] [Indexed: 12/11/2022]
Abstract
Modern Holter devices are very trendy tools used in medicine, research, or sport. They monitor a variety of human physiological or pathophysiological signals. Nowadays, Holter devices have been developing very fast. New innovative products come to the market every day. They have become smaller, smarter, cheaper, have ultra-low power consumption, do not limit everyday life, and allow comfortable measurements of humans to be accomplished in a familiar and natural environment, without extreme fear from doctors. People can be informed about their health and 24/7 monitoring can sometimes easily detect specific diseases, which are normally passed during routine ambulance operation. However, there is a problem with the reliability, quality, and quantity of the collected data. In normal life, there may be a loss of signal recording, abnormal growth of artifacts, etc. At this point, there is a need for multiple sensors capturing single variables in parallel by different sensing methods to complement these methods and diminish the level of artifacts. We can also sense multiple different signals that are complementary and give us a coherent picture. In this article, we describe actual interesting multi-sensor principles on the grounds of our own long-year experiences and many experiments.
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Affiliation(s)
- Erik Vavrinsky
- Institute of Electronics and Photonics, Faculty of Electrical Engineering and Information Technology, Slovak University of Technology, Ilkovicova 3, 81219 Bratislava, Slovakia; (J.S.); (M.D.); (T.Z.)
- Institute of Medical Physics, Biophysics, Informatics and Telemedicine, Faculty of Medicine, Comenius University, Sasinkova 2, 81272 Bratislava, Slovakia
| | - Jan Subjak
- Institute of Electronics and Photonics, Faculty of Electrical Engineering and Information Technology, Slovak University of Technology, Ilkovicova 3, 81219 Bratislava, Slovakia; (J.S.); (M.D.); (T.Z.)
| | - Martin Donoval
- Institute of Electronics and Photonics, Faculty of Electrical Engineering and Information Technology, Slovak University of Technology, Ilkovicova 3, 81219 Bratislava, Slovakia; (J.S.); (M.D.); (T.Z.)
| | - Alexandra Wagner
- Department of Simulation and Virtual Medical Education, Faculty of Medicine, Comenius University, Sasinkova 4, 81272 Bratislava, Slovakia; (A.W.); (H.S.)
| | - Tomas Zavodnik
- Institute of Electronics and Photonics, Faculty of Electrical Engineering and Information Technology, Slovak University of Technology, Ilkovicova 3, 81219 Bratislava, Slovakia; (J.S.); (M.D.); (T.Z.)
| | - Helena Svobodova
- Department of Simulation and Virtual Medical Education, Faculty of Medicine, Comenius University, Sasinkova 4, 81272 Bratislava, Slovakia; (A.W.); (H.S.)
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21
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Su PC, Miller S, Idriss S, Barker P, Wu HT. Recovery of the fetal electrocardiogram for morphological analysis from two trans-abdominal channels via optimal shrinkage. Physiol Meas 2019; 40:115005. [PMID: 31585453 DOI: 10.1088/1361-6579/ab4b13] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
OBJECTIVE We propose a novel algorithm to recover fetal electrocardiogram (ECG) for both the fetal heart rate analysis and morphological analysis of its waveform from two or three trans-abdominal maternal ECG channels. APPROACH We design an algorithm based on the optimal-shrinkage under the wave-shape manifold model. For the fetal heart rate analysis, the algorithm is evaluated on publicly available database, 2013 PhyioNet/Computing in Cardiology Challenge, set A (CinC2013). For the morphological analysis, we analyze CinC2013 and another publicly available database, non-invasive fetal ECG arrhythmia database (nifeadb), and propose to simulate semi-real databases by mixing the MIT-BIH normal sinus rhythm database and MITDB arrhythmia database. MAIN RESULTS For the fetal R peak detection, the proposed algorithm outperforms all algorithms under comparison. For the morphological analysis, the algorithm provides an encouraging result in recovery of the fetal ECG waveform, including PR, QT and ST intervals, even when the fetus has arrhythmia, both in real and simulated databases. SIGNIFICANCE To the best of our knowledge, this is the first work focusing on recovering the fetal ECG for morphological analysis from two or three channels with an algorithm potentially applicable for continuous fetal electrocardiographic monitoring, which creates the potential for long term monitoring purpose.
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Affiliation(s)
- Pei-Chun Su
- Department of Mathematics, Duke University, Durham, NC, United States of America
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22
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Kahankova R, Martinek R, Jaros R, Behbehani K, Matonia A, Jezewski M, Behar JA. A Review of Signal Processing Techniques for Non-Invasive Fetal Electrocardiography. IEEE Rev Biomed Eng 2019; 13:51-73. [PMID: 31478873 DOI: 10.1109/rbme.2019.2938061] [Citation(s) in RCA: 32] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
Fetal electrocardiography (fECG) is a promising alternative to cardiotocography continuous fetal monitoring. Robust extraction of the fetal signal from the abdominal mixture of maternal and fetal electrocardiograms presents the greatest challenge to effective fECG monitoring. This is mainly due to the low amplitude of the fetal versus maternal electrocardiogram and to the non-stationarity of the recorded signals. In this review, we highlight key developments in advanced signal processing algorithms for non-invasive fECG extraction and the available open access resources (databases and source code). In particular, we highlight the advantages and limitations of these algorithms as well as key parameters that must be set to ensure their optimal performance. Improving or combining the current or developing new advanced signal processing methods may enable morphological analysis of the fetal electrocardiogram, which today is only possible using the invasive scalp electrocardiography method.
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