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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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Kazemnejad A, Karimi S, Gordany P, Clifford GD, Sameni R. An open-access simultaneous electrocardiogram and phonocardiogram database. Physiol Meas 2024; 45:055005. [PMID: 38663430 DOI: 10.1088/1361-6579/ad43af] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2024] [Accepted: 04/25/2024] [Indexed: 05/16/2024]
Abstract
Objective.The EPHNOGRAM project aimed to develop a low-cost, low-power device for simultaneous electrocardiogram (ECG) and phonocardiogram (PCG) recording, with additional channels for environmental audio to enhance PCG through active noise cancellation. The objective was to study multimodal electro-mechanical activities of the heart, offering insights into the differences and synergies between these modalities during various cardiac activity levels.Approach.We developed and tested several hardware prototypes of a simultaneous ECG-PCG acquisition device. Using this technology, we collected simultaneous ECG and PCG data from 24 healthy adults during different physical activities, including resting, walking, running, and stationary biking, in an indoor fitness center. The data were annotated using a robust software that we developed for detecting ECG R-peaks and PCG S1 and S2 components, and overseen by a human expert. We also developed machine learning models using ECG-based, PCG-based, and joint ECG-PCG features, like R-R and S1-S2 intervals, to classify physical activities and analyze electro-mechanical dynamics.Main results.The results show a significant coupling between ECG and PCG components, especially during high-intensity exercise. Notable micro-variations in S2-based heart rate show differences in the heart's electrical and mechanical functions. The Lomb-Scargle periodogram and approximate entropy analyses confirm the higher volatility of S2-based heart rate compared to ECG-based heart rate. Correlation analysis shows stronger coupling between R-R and R-S1 intervals during high-intensity activities. Hybrid ECG-PCG features, like the R-S2 interval, were identified as more informative for physical activity classification through mRMR feature selection and SHAP value analysis.Significance.The EPHNOGRAM database, is available on PhysioNet. The database enhances our understanding of cardiac function, enabling future studies on the heart's mechanical and electrical interrelationships. The results of this study can contribute to improved cardiac condition diagnoses. Additionally, the designed hardware has the potential for integration into wearable devices and the development of multimodal stress test technologies.
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Affiliation(s)
| | - Sajjad Karimi
- Department of Biomedical Informatics, Emory University, Atlanta, GA, United States of America
| | | | - Gari D Clifford
- Department of Biomedical Informatics, Emory University, Atlanta, GA, United States of America
- Department of Biomedical Engineering, Georgia Institute of Technology, Atlanta, GA, United States of America
| | - Reza Sameni
- Department of Biomedical Informatics, Emory University, Atlanta, GA, United States of America
- Department of Biomedical Engineering, Georgia Institute of Technology, Atlanta, GA, United States of America
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Baldazzi G, Sulas E, Vullings R, Urru M, Tumbarello R, Raffo L, Pani D. Automatic signal quality assessment of raw trans-abdominal biopotential recordings for non-invasive fetal electrocardiography. Front Bioeng Biotechnol 2023; 11:1059119. [PMID: 36923461 PMCID: PMC10009887 DOI: 10.3389/fbioe.2023.1059119] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2022] [Accepted: 02/13/2023] [Indexed: 03/02/2023] Open
Abstract
Introduction: Wearable monitoring systems for non-invasive multi-channel fetal electrocardiography (fECG) can support fetal surveillance and diagnosis during pregnancy, thus enabling prompt treatment. In these embedded systems, power saving is the key to long-term monitoring. In this regard, the computational burden of signal processing methods implemented for the fECG extraction from the multi-channel trans-abdominal recordings plays a non-negligible role. In this work, a supervised machine-learning approach for the automatic selection of the most informative raw abdominal recordings in terms of fECG content, i.e., those potentially leading to good-quality, non-invasive fECG signals from a low number of channels, is presented and evaluated. Methods: For this purpose, several signal quality indexes from the scientific literature were adopted as features to train an ensemble tree classifier, which was asked to perform a binary classification between informative and non-informative abdominal channels. To reduce the dimensionality of the classification problem, and to improve the performance, a feature selection approach was also implemented for the identification of a subset of optimal features. 10336 5-s long signal segments derived from a real dataset of multi-channel trans-abdominal recordings acquired from 55 voluntary pregnant women between the 21st and the 27th week of gestation, with healthy fetuses, were adopted to train and test the classification approach in a stratified 10-time 10-fold cross-validation scheme. Abdominal recordings were firstly pre-processed and then labeled as informative or non-informative, according to the signal-to-noise ratio exhibited by the extracted fECG, thus producing a balanced dataset of bad and good quality abdominal channels. Results and Discussion: Classification performance revealed an accuracy above 86%, and more than 88% of those channels labeled as informative were correctly identified. Furthermore, by applying the proposed method to 50 annotated 24-channel recordings from the NInFEA dataset, a significant improvement was observed in fetal QRS detection when only the channels selected by the proposed approach were considered, compared with the use of all the available channels. As such, our findings support the hypothesis that performing a channel selection by looking directly at the raw abdominal signals, regardless of the fetal presentation, can produce a reliable measurement of fetal heart rate with a lower computational burden.
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Affiliation(s)
- Giulia Baldazzi
- Department of Electrical and Electronic Engineering, University of Cagliari, Cagliari, Italy
| | - Eleonora Sulas
- Department of Electrical and Electronic Engineering, University of Cagliari, Cagliari, Italy
| | - Rik Vullings
- Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, Netherlands
| | - Monica Urru
- Pediatric Cardiology and Congenital Heart Disease Unit, ARNAS G. Brotzu Hospital, Cagliari, Italy
| | - Roberto Tumbarello
- Pediatric Cardiology and Congenital Heart Disease Unit, ARNAS G. Brotzu Hospital, Cagliari, Italy
| | - Luigi Raffo
- Department of Electrical and Electronic Engineering, University of Cagliari, Cagliari, Italy
| | - Danilo Pani
- Department of Electrical and Electronic Engineering, University of Cagliari, Cagliari, Italy
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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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Fotiadou E, van Sloun RJG, van Laar JOEH, Vullings R. A dilated inception CNN-LSTM network for fetal heart rate estimation. Physiol Meas 2021; 42. [PMID: 33853039 DOI: 10.1088/1361-6579/abf7db] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2020] [Accepted: 04/14/2021] [Indexed: 01/16/2023]
Abstract
Objective. Fetal heart rate (HR) monitoring is routinely used during pregnancy and labor to assess fetal well-being. The noninvasive fetal electrocardiogram (ECG), obtained by electrodes on the maternal abdomen, is a promising alternative to standard fetal monitoring. Subtraction of the maternal ECG from the abdominal measurements results in fetal ECG signals, in which the fetal HR can be determined typically through R-peak detection. However, the low signal-to-noise ratio and the nonstationary nature of the fetal ECG make R-peak detection a challenging task.Approach. We propose an alternative approach that instead of performing R-peak detection employs deep learning to directly determine the fetal HR from the extracted fetal ECG signals. We introduce a combination of dilated inception convolutional neural networks (CNN) with long short-term memory networks to capture both short-term and long-term temporal dynamics of the fetal HR. The robustness of the method is reinforced by a separate CNN-based classifier that estimates the reliability of the outcome.Main results. Our method achieved a positive percent agreement (within 10% of the actual fetal HR value) of 97.3% on a dataset recorded during labor and 99.6% on set-A of the 2013 Physionet/Computing in Cardiology Challenge exceeding top-performing state-of-the-art algorithms from the literature.Significance. The proposed method can potentially improve the accuracy and robustness of fetal HR extraction in clinical practice.
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Affiliation(s)
- E Fotiadou
- Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, 5612 AP, The Netherlands
| | - R J G van Sloun
- Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, 5612 AP, The Netherlands
| | - J O E H van Laar
- Department of Obstetrics and Gynaecology, Máxima Medical Center, Veldhoven, 5504 DB, The Netherlands
| | - R Vullings
- Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, 5612 AP, The Netherlands
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Detection of Fetal Cardiac Anomaly from Composite Abdominal Electrocardiogram. Biomed Signal Process Control 2021. [DOI: 10.1016/j.bspc.2020.102308] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
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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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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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Jamshidian-Tehrani F, Sameni R, Jutten C. Temporally Nonstationary Component Analysis; Application to Noninvasive Fetal Electrocardiogram Extraction. IEEE Trans Biomed Eng 2019; 67:1377-1386. [PMID: 31442967 DOI: 10.1109/tbme.2019.2936943] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Abstract
OBJECTIVE Mixtures of temporally nonstationary signals are very common in biomedical applications. The nonstationarity of the source signals can be used as a discriminative property for signal separation. Herein, a semi-blind source separation algorithm is proposed for the extraction of temporally nonstationary components from linear multichannel mixtures of signals and noises. METHODS A hypothesis test is proposed for the detection and fusion of temporally nonstationary events, by using ad hoc indexes for monitoring the first and second order statistics of the innovation process. As proof of concept, the general framework is customized and tested over noninvasive fetal cardiac recordings acquired from the maternal abdomen, over publicly available datasets, using two types of nonstationarity detectors: 1) a local power variations detector, and 2) a model-deviations detector using the innovation process properties of an extended Kalman filter. RESULTS The performance of the proposed method is assessed in presence of white and colored noise, in different signal-to-noise ratios. CONCLUSION AND SIGNIFICANCE The proposed scheme is general and it can be used for the extraction of nonstationary events and sample deviations from a presumed model in multivariate data, which is a recurrent problem in many machine learning applications.
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