1
|
de Vries IR, van Laar JOEH, van der Hout‐van der Jagt MB, Clur SB, Vullings R. Fetal electrocardiography and artificial intelligence for prenatal detection of congenital heart disease. Acta Obstet Gynecol Scand 2023; 102:1511-1520. [PMID: 37563851 PMCID: PMC10577634 DOI: 10.1111/aogs.14623] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2023] [Revised: 06/09/2023] [Accepted: 06/09/2023] [Indexed: 08/12/2023]
Abstract
INTRODUCTION This study aims to investigate non-invasive electrocardiography as a method for the detection of congenital heart disease (CHD) with the help of artificial intelligence. MATERIAL AND METHODS An artificial neural network was trained for the identification of CHD using non-invasively obtained fetal electrocardiograms. With the help of a Bayesian updating rule, multiple electrocardiographs were used to increase the algorithm's performance. RESULTS Using 122 measurements containing 65 healthy and 57 CHD cases, the accuracy, sensitivity, and specificity were found to be 71%, 63%, and 77%, respectively. The sensitivity was however 75% and 69% for CHD cases requiring an intervention in the neonatal period and first year of life, respectively. Furthermore, a positive effect of measurement length on the detection performance was observed, reaching optimal performance when using 14 electrocardiography segments (37.5 min) or more. A small negative trend between gestational age and accuracy was found. CONCLUSIONS The proposed method combining recent advances in obtaining non-invasive fetal electrocardiography with artificial intelligence for the automatic detection of CHD achieved a detection rate of 63% for all CHD and 75% for critical CHD. This feasibility study shows that detection rates of CHD might improve by using electrocardiography-based screening complementary to the standard ultrasound-based screening. More research is required to improve performance and determine the benefits to clinical practice.
Collapse
Affiliation(s)
- Ivar R. de Vries
- Department of Obstetrics and GynecologyMáxima Medical CenterVeldhovenThe Netherlands
- Department of Electrical EngineeringEindhoven University of TechnologyEindhovenThe Netherlands
| | - Judith O. E. H. van Laar
- Department of Obstetrics and GynecologyMáxima Medical CenterVeldhovenThe Netherlands
- Department of Electrical EngineeringEindhoven University of TechnologyEindhovenThe Netherlands
| | - Marieke B. van der Hout‐van der Jagt
- Department of Obstetrics and GynecologyMáxima Medical CenterVeldhovenThe Netherlands
- Department of Electrical EngineeringEindhoven University of TechnologyEindhovenThe Netherlands
- Department of Biomedical EngineeringEindhoven University of TechnologyEindhovenThe Netherlands
| | - Sally‐Ann B. Clur
- Department of Pediatric Cardiology, Emma Children's HospitalAmsterdam University Medical Centers, Academic Medical CenterAmsterdamThe Netherlands
- European Reference Network for rare, low prevalence and complex diseases of the heart ‐ ERN GUARD‐Heart (ERN GUARDHEART)AmsterdamThe Netherlands
| | - Rik Vullings
- Department of Electrical EngineeringEindhoven University of TechnologyEindhovenThe Netherlands
- Nemo Healthcare BVVeldhovenThe Netherlands
| |
Collapse
|
2
|
Noben L, Lempersz C, van den Heuvel ER, Zhan Z, Vandenbussche FPHA, Coumans ABC, Haak MC, Vullings R, Oei SG, Clur SAB, van Laar JOEH. The electrical heart axis in fetuses with congenital heart disease, measured with non-invasive fetal electrocardiography. PLoS One 2022; 17:e0275802. [PMID: 36264863 PMCID: PMC9584524 DOI: 10.1371/journal.pone.0275802] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2021] [Accepted: 09/23/2022] [Indexed: 11/16/2022] Open
Abstract
OBJECTIVES To determine if the electrical heart axis in different types of congenital heart defects (CHD) differs from that of a healthy cohort at mid-gestation. METHODS Non-invasive fetal electrocardiography (NI-fECG) was performed in singleton pregnancies with suspected CHD between 16 and 30 weeks of gestation. The mean electrical heart axis (MEHA) was determined from the fetal vectorcardiogram after correction for fetal orientation. Descriptive statistics were used to determine the MEHA with corresponding 95% confidence intervals (CI) in the frontal plane of all fetuses with CHD and the following subgroups: conotruncal anomalies (CTA), atrioventricular septal defects (AVSD) and hypoplastic right heart syndrome (HRHS). The MEHA of the CHD fetuses as well as the subgroups was compared to the healthy control group using a spherically projected multivariate linear regression analysis. Discriminant analysis was applied to calculate the sensitivity and specificity of the electrical heart axis for CHD detection. RESULTS The MEHA was determined in 127 fetuses. The MEHA was 83.0° (95% CI: 6.7°; 159.3°) in the total CHD group, and not significantly different from the control group (122.7° (95% CI: 101.7°; 143.6°). The MEHA was 105.6° (95% CI: 46.8°; 164.4°) in the CTA group (n = 54), -27.4° (95% CI: -118.6°; 63.9°) in the AVSD group (n = 9) and 26.0° (95% CI: -34.1°; 86.1°) in the HRHS group (n = 5). The MEHA of the AVSD and the HRHS subgroups were significantly different from the control group (resp. p = 0.04 and p = 0.02). The sensitivity and specificity of the MEHA for the diagnosis of CHD was 50.6% (95% CI 47.5% - 53.7%) and 60.1% (95% CI 57.1% - 63.1%) respectively. CONCLUSION The MEHA alone does not discriminate between healthy fetuses and fetuses with CHD. However, the left-oriented electrical heart axis in fetuses with AVSD and HRHS was significantly different from the control group suggesting altered cardiac conduction along with the structural defect. TRIAL REGISTRATION Clinical trial registration number: NL48535.015.14.
Collapse
Affiliation(s)
- L. Noben
- Department of Obstetrics and Gynecology, Máxima Medical Center, Veldhoven, The Netherlands
- Eindhoven MedTech Innovation Center (e/MTIC), Eindhoven, The Netherlands
- * E-mail:
| | - C. Lempersz
- Department of Obstetrics and Gynecology, Máxima Medical Center, Veldhoven, The Netherlands
- Eindhoven MedTech Innovation Center (e/MTIC), Eindhoven, The Netherlands
| | - E. R. van den Heuvel
- Department of Mathematics and Computer Science, Eindhoven University of Technology, Eindhoven, The Netherlands
| | - Z. Zhan
- Department of Mathematics and Computer Science, Eindhoven University of Technology, Eindhoven, The Netherlands
| | - F. P. H. A. Vandenbussche
- Department of Obstetrics and Gynecology, Radboud University Medical Center, Nijmegen, The Netherlands
| | - A. B. C. Coumans
- Department of Obstetrics and Gynecology, Maastricht University Medical Center, Maastricht, The Netherlands
| | - M. C. Haak
- Department of Obstetrics and Gynecology, Leiden University Medical Center, Leiden, The Netherlands
| | - R. Vullings
- Department of Obstetrics and Gynecology, Máxima Medical Center, Veldhoven, The Netherlands
- Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands
| | - S. G. Oei
- Department of Obstetrics and Gynecology, Máxima Medical Center, Veldhoven, The Netherlands
- Eindhoven MedTech Innovation Center (e/MTIC), Eindhoven, The Netherlands
- Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands
| | - S. A. B. Clur
- Department of Pediatric Cardiology, Amsterdam University Medical Centers, Amsterdam, The Netherlands
| | - J. O. E. H. van Laar
- Department of Obstetrics and Gynecology, Máxima Medical Center, Veldhoven, The Netherlands
- Eindhoven MedTech Innovation Center (e/MTIC), Eindhoven, The Netherlands
- Department of Pediatric Cardiology, Amsterdam University Medical Centers, Amsterdam, The Netherlands
| |
Collapse
|
3
|
Wakefield C, Yao L, Self S, Frasch MG. Wearable technology for health monitoring during pregnancy: an observational cross-sectional survey study. Arch Gynecol Obstet 2022; 308:73-78. [PMID: 35831759 PMCID: PMC9281287 DOI: 10.1007/s00404-022-06705-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2022] [Accepted: 07/03/2022] [Indexed: 11/26/2022]
Abstract
PURPOSE To evaluate how women of child-bearing age perceive the use of remote fetal ECG monitoring technologies. Telemedicine has advanced to the forefront of healthcare delivery, including maternal-fetal medicine. Smart wearable electrocardiogram (ECG) devices can enable pregnant women to monitor their health and that of their fetuses. Such technology would be a logical extension of the telemedicine ecosystem. METHODS We conducted an observational cross-sectional study via online surveying in the United States. Study participants were recruited using the SurveyMonkey Audience Polling system and responded virtually. In all, the sample consisted of 507 women, aged 18-45 from 45 states, who are expecting to become pregnant in the next five years. Women were asked to identify their willingness to use a wearable ECG device the size of a patch-sized large band-aid on their abdomen. Ten binary or multiple-choice questions were used to gauge population interest and related demographics toward the usage of a wearable ECG device. RESULTS Of the 507 participants, 461 (91%) women expressed an acceptance of wearable ECG technology throughout the pregnancy as a mechanism for increased frequency of monitoring of maternal and fetal health outside the hospital. 395 (78%) women demonstrated a willingness to wear devices day and night or at least during sleep and 213 (42%) of the women would spend up to $200 on such a device. CONCLUSION Even though conducted prior to the COVID-19 pandemic, this study clearly indicates a high degree of readiness of prospective pregnant women for telemedicine with continuous health monitoring of the mother-fetus dyad.
Collapse
Affiliation(s)
- Colin Wakefield
- Department of Obstetrics and Gynecology, Center on Human Development and Disability (CHDD), University of Washington, 1959 NE Pacific St, Box 356460, Seattle, WA, 98195, USA
| | - Lena Yao
- Fetal Precision, LLC, Seattle, WA, USA
| | | | - Martin G Frasch
- Department of Obstetrics and Gynecology, Center on Human Development and Disability (CHDD), University of Washington, 1959 NE Pacific St, Box 356460, Seattle, WA, 98195, USA.
- Fetal Precision, LLC, Seattle, WA, USA.
| |
Collapse
|
4
|
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.
Collapse
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
| |
Collapse
|
5
|
Lempersz C, Noben L, Clur SAB, van den Heuvel E, Zhan Z, Haak M, Oei SG, Vullings R, van Laar JOEH. The electrical heart axis of the fetus between 18 and 24 weeks of gestation: A cohort study. PLoS One 2021; 16:e0256115. [PMID: 34914710 PMCID: PMC8675734 DOI: 10.1371/journal.pone.0256115] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2021] [Accepted: 07/31/2021] [Indexed: 11/18/2022] Open
Abstract
Introduction A fetal anomaly scan in mid-pregnancy is performed, to check for the presence of congenital anomalies, including congenital heart disease (CHD). Unfortunately, 40% of CHD is still missed. The combined use of ultrasound and electrocardiography might boost detection rates. The electrical heart axis is one of the characteristics which can be deduced from an electrocardiogram (ECG). The aim of this study was to determine reference values for the electrical heart axis in healthy fetuses around 20 weeks of gestation. Material and methods Non-invasive fetal electrocardiography was performed subsequent to the fetal anomaly scan in pregnant women carrying a healthy singleton fetus between 18 and 24 weeks of gestation. Eight adhesive electrodes were applied on the maternal abdomen including one ground and one reference electrode, yielding six channels of bipolar electrophysiological measurements. After removal of interferences, a fetal vectorcardiogram was calculated and then corrected for fetal orientation. The orientation of the electrical heart axis was determined from this normalized fetal vectorcardiogram. Descriptive statistics were used on normalized cartesian coordinates to determine the average electrical heart axis in the frontal plane. Furthermore, 90% prediction intervals (PI) for abnormality were calculated. Results Of the 328 fetal ECGs performed, 281 were included in the analysis. The average electrical heart axis in the frontal plane was determined at 122.7° (90% PI: -25.6°; 270.9°). Discussion The average electrical heart axis of healthy fetuses around mid-gestation is oriented to the right, which is, due to the unique fetal circulation, in line with muscle distribution in the fetal heart.
Collapse
Affiliation(s)
- Carlijn Lempersz
- Department of Obstetrics and Gynecology, Máxima Medical Center, Veldhoven, The Netherlands.,Eindhoven MedTech Innovation Center (e/MTIC), Eindhoven, The Netherlands
| | - Lore Noben
- Department of Obstetrics and Gynecology, Máxima Medical Center, Veldhoven, The Netherlands.,Eindhoven MedTech Innovation Center (e/MTIC), Eindhoven, The Netherlands
| | - Sally-Ann B Clur
- Department of Pediatric Cardiology, Amsterdam University Medical Center, Amsterdam, The Netherlands
| | - Edwin van den Heuvel
- Department of Mathematics and Computer Science, Eindhoven University of Technology, Eindhoven, The Netherlands
| | - Zhouzhao Zhan
- Department of Mathematics and Computer Science, Eindhoven University of Technology, Eindhoven, The Netherlands
| | - Monique Haak
- Department of Obstetrics and Gynecology, Leiden University Medical Center, Leiden, The Netherlands
| | - S Guid Oei
- Department of Obstetrics and Gynecology, Máxima Medical Center, Veldhoven, The Netherlands.,Eindhoven MedTech Innovation Center (e/MTIC), Eindhoven, The Netherlands.,Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands
| | - Rik Vullings
- Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands
| | - Judith O E H van Laar
- Department of Obstetrics and Gynecology, Máxima Medical Center, Veldhoven, The Netherlands.,Eindhoven MedTech Innovation Center (e/MTIC), Eindhoven, The Netherlands.,Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands
| |
Collapse
|
6
|
Fotiadou E, Vullings R. Multi-Channel Fetal ECG Denoising With Deep Convolutional Neural Networks. Front Pediatr 2020; 8:508. [PMID: 32984218 PMCID: PMC7480014 DOI: 10.3389/fped.2020.00508] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/08/2020] [Accepted: 07/17/2020] [Indexed: 11/13/2022] Open
Abstract
Non-invasive fetal electrocardiography represents a valuable alternative continuous fetal monitoring method that has recently received considerable attention in assessing fetal health. However, the non-invasive fetal electrocardiogram (ECG) is typically severely contaminated by a considerable amount of various noise sources, rendering fetal ECG denoising a very challenging task. This work employs a deep learning approach for removing the residual noise from multi-channel fetal ECG after the maternal ECG has been suppressed. We propose a deep convolutional encoder-decoder network with symmetric skip-layer connections, learning end-to-end mappings from noise-corrupted fetal ECG signals to clean ones. Experiments on simulated data show an average signal-to-noise ratio (SNR) improvement of 9.5 dB for fetal ECG signals with input SNR ranging between -20 and 20 dB. The method is additionally evaluated on a large set of real signals, demonstrating that it can provide significant quality improvement of the noisy fetal ECG signals. We further show that employment of multi-channel signal information by the network provides superior and more reliable performance as opposed to its single-channel network counterpart. The presented method is able to preserve beat-to-beat morphological variations and does not require any prior information on the power spectra of the noise or the pulse location.
Collapse
Affiliation(s)
- Eleni Fotiadou
- Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, Netherlands
| | - Rik Vullings
- Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, Netherlands
| |
Collapse
|
7
|
Vullings R, van Laar JOEH. Non-invasive Fetal Electrocardiography for Intrapartum Cardiotocography. Front Pediatr 2020; 8:599049. [PMID: 33363064 PMCID: PMC7755891 DOI: 10.3389/fped.2020.599049] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/26/2020] [Accepted: 11/19/2020] [Indexed: 11/19/2022] Open
Abstract
Fetal monitoring is important to diagnose complications that can occur during pregnancy. If detected timely, these complications might be resolved before they lead to irreversible damage. Current fetal monitoring mainly relies on cardiotocography, the simultaneous registration of fetal heart rate and uterine activity. Unfortunately, the technology to obtain the cardiotocogram has limitations. In current clinical practice the fetal heart rate is obtained via either an invasive scalp electrode, that poses risks and can only be applied during labor and after rupture of the fetal membranes, or via non-invasive Doppler ultrasound technology that is inaccurate and suffers from loss of signal, in particular in women with high body mass, during motion, or in preterm pregnancies. In this study, transabdominal electrophysiological measurements are exploited to provide fetal heart rate non-invasively and in a more reliable manner than Doppler ultrasound. The performance of the fetal heart rate detection is determined by comparing the fetal heart rate to that obtained with an invasive scalp electrode during intrapartum monitoring. The performance is gauged by comparing it to performances mentioned in literature on Doppler ultrasound and on two commercially-available devices that are also based on transabdominal fetal electrocardiography.
Collapse
Affiliation(s)
- Rik Vullings
- Biomedical Diagnostics Lab Eindhoven, Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, Netherlands.,Nemo Healthcare, Veldhoven, Netherlands
| | | |
Collapse
|