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Rao L, Lu J, Wu HR, Zhao S, Lu BC, Li H. Automatic classification of fetal heart rate based on a multi-scale LSTM network. Front Physiol 2024; 15:1398735. [PMID: 38933361 PMCID: PMC11202091 DOI: 10.3389/fphys.2024.1398735] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2024] [Accepted: 05/02/2024] [Indexed: 06/28/2024] Open
Abstract
Introduction Fetal heart rate monitoring during labor can aid healthcare professionals in identifying alterations in the heart rate pattern. However, discrepancies in guidelines and obstetrician expertise present challenges in interpreting fetal heart rate, including failure to acknowledge findings or misinterpretation. Artificial intelligence has the potential to support obstetricians in diagnosing abnormal fetal heart rates. Methods Employ preprocessing techniques to mitigate the effects of missing signals and artifacts on the model, utilize data augmentation methods to address data imbalance. Introduce a multi-scale long short-term memory neural network trained with a variety of time-scale data for automatically classifying fetal heart rate. Carried out experimental on both single and multi-scale models. Results The results indicate that multi-scale LSTM models outperform regular LSTM models in various performance metrics. Specifically, in the single models tested, the model with a sampling rate of 10 exhibited the highest classification accuracy. The model achieves an accuracy of 85.73%, a specificity of 85.32%, and a precision of 85.53% on CTU-UHB dataset. Furthermore, the area under the receiver operating curve of 0.918 suggests that our model demonstrates a high level of credibility. Discussion Compared to previous research, our methodology exhibits superior performance across various evaluation metrics. By incorporating alternative sampling rates into the model, we observed improvements in all performance indicators, including ACC (85.73% vs. 83.28%), SP (85.32% vs. 82.47%), PR (85.53% vs. 82.84%), recall (86.13% vs. 84.09%), F1-score (85.79% vs. 83.42%), and AUC(0.9180 vs. 0.8667). The limitations of this research include the limited consideration of pregnant women's clinical characteristics and disregard the potential impact of varying gestational weeks.
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Affiliation(s)
- Lin Rao
- International Peace Maternity and Child Health Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Shanghai Key Laboratory of Embryo Original Diseases, Shanghai, China
| | - Jia Lu
- International Peace Maternity and Child Health Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Shanghai Key Laboratory of Embryo Original Diseases, Shanghai, China
| | - Hai-Rong Wu
- Key Laboratory of System Control and Information Processing, Ministry of Education of Shanghai Jiao Tong University, Shanghai, China
| | - Shu Zhao
- International Peace Maternity and Child Health Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Shanghai Key Laboratory of Embryo Original Diseases, Shanghai, China
| | - Bang-Chun Lu
- International Peace Maternity and Child Health Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Shanghai Key Laboratory of Embryo Original Diseases, Shanghai, China
| | - Hong Li
- International Peace Maternity and Child Health Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Shanghai Key Laboratory of Embryo Original Diseases, Shanghai, China
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Mendis L, Palaniswami M, Keenan E, Brownfoot F. Rapid detection of fetal compromise using input length invariant deep learning on fetal heart rate signals. Sci Rep 2024; 14:12615. [PMID: 38824217 PMCID: PMC11144251 DOI: 10.1038/s41598-024-63108-6] [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: 09/09/2023] [Accepted: 05/24/2024] [Indexed: 06/03/2024] Open
Abstract
Standard clinical practice to assess fetal well-being during labour utilises monitoring of the fetal heart rate (FHR) using cardiotocography. However, visual evaluation of FHR signals can result in subjective interpretations leading to inter and intra-observer disagreement. Therefore, recent studies have proposed deep-learning-based methods to interpret FHR signals and detect fetal compromise. These methods have typically focused on evaluating fixed-length FHR segments at the conclusion of labour, leaving little time for clinicians to intervene. In this study, we propose a novel FHR evaluation method using an input length invariant deep learning model (FHR-LINet) to progressively evaluate FHR as labour progresses and achieve rapid detection of fetal compromise. Using our FHR-LINet model, we obtained approximately 25% reduction in the time taken to detect fetal compromise compared to the state-of-the-art multimodal convolutional neural network while achieving 27.5%, 45.0%, 56.5% and 65.0% mean true positive rate at 5%, 10%, 15% and 20% false positive rate respectively. A diagnostic system based on our approach could potentially enable earlier intervention for fetal compromise and improve clinical outcomes.
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Affiliation(s)
- Lochana Mendis
- Department of Electrical and Electronic Engineering, The University of Melbourne, Parkville, 3010, VIC, Australia.
| | - Marimuthu Palaniswami
- Department of Electrical and Electronic Engineering, The University of Melbourne, Parkville, 3010, VIC, Australia
| | - Emerson Keenan
- Department of Electrical and Electronic Engineering, The University of Melbourne, Parkville, 3010, VIC, Australia
- Obstetric Diagnostics and Therapeutics Group, Department of Obstetrics and Gynaecology, The University of Melbourne, Heidelberg, 3084, VIC, Australia
| | - Fiona Brownfoot
- Obstetric Diagnostics and Therapeutics Group, Department of Obstetrics and Gynaecology, The University of Melbourne, Heidelberg, 3084, VIC, Australia
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Francis F, Luz S, Wu H, Stock SJ, Townsend R. Machine learning on cardiotocography data to classify fetal outcomes: A scoping review. Comput Biol Med 2024; 172:108220. [PMID: 38489990 DOI: 10.1016/j.compbiomed.2024.108220] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2023] [Revised: 02/02/2024] [Accepted: 02/25/2024] [Indexed: 03/17/2024]
Abstract
INTRODUCTION Uterine contractions during labour constrict maternal blood flow and oxygen delivery to the developing baby, causing transient hypoxia. While most babies are physiologically adapted to withstand such intrapartum hypoxia, those exposed to severe hypoxia or with poor physiological reserves may experience neurological injury or death during labour. Cardiotocography (CTG) monitoring was developed to identify babies at risk of hypoxia by detecting changes in fetal heart rate (FHR) patterns. CTG monitoring is in widespread use in intrapartum care for the detection of fetal hypoxia, but the clinical utility is limited by a relatively poor positive predictive value (PPV) of an abnormal CTG and significant inter and intra observer variability in CTG interpretation. Clinical risk and human factors may impact the quality of CTG interpretation. Misclassification of CTG traces may lead to both under-treatment (with the risk of fetal injury or death) or over-treatment (which may include unnecessary operative interventions that put both mother and baby at risk of complications). Machine learning (ML) has been applied to this problem since early 2000 and has shown potential to predict fetal hypoxia more accurately than visual interpretation of CTG alone. To consider how these tools might be translated for clinical practice, we conducted a review of ML techniques already applied to CTG classification and identified research gaps requiring investigation in order to progress towards clinical implementation. MATERIALS AND METHOD We used identified keywords to search databases for relevant publications on PubMed, EMBASE and IEEE Xplore. We used Preferred Reporting Items for Systematic Review and Meta-Analysis for Scoping Reviews (PRISMA-ScR). Title, abstract and full text were screened according to the inclusion criteria. RESULTS We included 36 studies that used signal processing and ML techniques to classify CTG. Most studies used an open-access CTG database and predominantly used fetal metabolic acidosis as the benchmark for hypoxia with varying pH levels. Various methods were used to process and extract CTG signals and several ML algorithms were used to classify CTG. We identified significant concerns over the practicality of using varying pH levels as the CTG classification benchmark. Furthermore, studies needed to be more generalised as most used the same database with a low number of subjects for an ML study. CONCLUSION ML studies demonstrate potential in predicting fetal hypoxia from CTG. However, more diverse datasets, standardisation of hypoxia benchmarks and enhancement of algorithms and features are needed for future clinical implementation.
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Affiliation(s)
| | | | - Honghan Wu
- Institute of Health Informatics, University College London, UK
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Mendis L, Palaniswami M, Brownfoot F, Keenan E. Computerised Cardiotocography Analysis for the Automated Detection of Fetal Compromise during Labour: A Review. Bioengineering (Basel) 2023; 10:1007. [PMID: 37760109 PMCID: PMC10525263 DOI: 10.3390/bioengineering10091007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2023] [Revised: 08/17/2023] [Accepted: 08/22/2023] [Indexed: 09/29/2023] Open
Abstract
The measurement and analysis of fetal heart rate (FHR) and uterine contraction (UC) patterns, known as cardiotocography (CTG), is a key technology for detecting fetal compromise during labour. This technology is commonly used by clinicians to make decisions on the mode of delivery to minimise adverse outcomes. A range of computerised CTG analysis techniques have been proposed to overcome the limitations of manual clinician interpretation. While these automated techniques can potentially improve patient outcomes, their adoption into clinical practice remains limited. This review provides an overview of current FHR and UC monitoring technologies, public and private CTG datasets, pre-processing steps, and classification algorithms used in automated approaches for fetal compromise detection. It aims to highlight challenges inhibiting the translation of automated CTG analysis methods from research to clinical application and provide recommendations to overcome them.
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Affiliation(s)
- Lochana Mendis
- Department of Electrical and Electronic Engineering, The University of Melbourne, Parkville, VIC 3010, Australia; (M.P.); (E.K.)
| | - Marimuthu Palaniswami
- Department of Electrical and Electronic Engineering, The University of Melbourne, Parkville, VIC 3010, Australia; (M.P.); (E.K.)
| | - Fiona Brownfoot
- Obstetric Diagnostics and Therapeutics Group, Department of Obstetrics and Gynaecology, The University of Melbourne, Heidelberg, VIC 3084, Australia;
| | - Emerson Keenan
- Department of Electrical and Electronic Engineering, The University of Melbourne, Parkville, VIC 3010, Australia; (M.P.); (E.K.)
- Obstetric Diagnostics and Therapeutics Group, Department of Obstetrics and Gynaecology, The University of Melbourne, Heidelberg, VIC 3084, Australia;
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Asfaw D, Jordanov I, Impey L, Namburete A, Lee R, Georgieva A. Multimodal Deep Learning for Predicting Adverse Birth Outcomes Based on Early Labour Data. Bioengineering (Basel) 2023; 10:730. [PMID: 37370663 DOI: 10.3390/bioengineering10060730] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2023] [Revised: 05/29/2023] [Accepted: 06/07/2023] [Indexed: 06/29/2023] Open
Abstract
Cardiotocography (CTG) is a widely used technique to monitor fetal heart rate (FHR) during labour and assess the health of the baby. However, visual interpretation of CTG signals is subjective and prone to error. Automated methods that mimic clinical guidelines have been developed, but they failed to improve detection of abnormal traces. This study aims to classify CTGs with and without severe compromise at birth using routinely collected CTGs from 51,449 births at term from the first 20 min of FHR recordings. Three 1D-CNN and LSTM based architectures are compared. We also transform the FHR signal into 2D images using time-frequency representation with a spectrogram and scalogram analysis, and subsequently, the 2D images are analysed using a 2D-CNNs. In the proposed multi-modal architecture, the 2D-CNN and the 1D-CNN-LSTM are connected in parallel. The models are evaluated in terms of partial area under the curve (PAUC) between 0-10% false-positive rate; and sensitivity at 95% specificity. The 1D-CNN-LSTM parallel architecture outperformed the other models, achieving a PAUC of 0.20 and sensitivity of 20% at 95% specificity. Our future work will focus on improving the classification performance by employing a larger dataset, analysing longer FHR traces, and incorporating clinical risk factors.
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Affiliation(s)
- Daniel Asfaw
- School of Computing, University of Portsmouth, Portsmouth PO1 3HE, UK
- Nuffield Department of Women's & Reproductive Health, University of Oxford, Oxford OX1 2JD, UK
| | - Ivan Jordanov
- School of Computing, University of Portsmouth, Portsmouth PO1 3HE, UK
| | - Lawrence Impey
- Nuffield Department of Women's & Reproductive Health, University of Oxford, Oxford OX1 2JD, UK
| | - Ana Namburete
- Department of Computer Science, University of Oxford, Oxford OX1 3QG, UK
| | - Raymond Lee
- Faculty of Technology, University of Portsmouth, Portsmouth PO1 2UP, UK
| | - Antoniya Georgieva
- Nuffield Department of Women's & Reproductive Health, University of Oxford, Oxford OX1 2JD, UK
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Deep learning based fetal distress detection from time frequency representation of cardiotocogram signal using Morse wavelet: research study. BMC Med Inform Decis Mak 2022; 22:329. [PMCID: PMC9749291 DOI: 10.1186/s12911-022-02068-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2022] [Accepted: 11/28/2022] [Indexed: 12/15/2022] Open
Abstract
Abstract
Background
Clinically cardiotocography is a technique which is used to monitor and evaluate the level of fetal distress. Even though, CTG is the most widely used device to monitor determine the fetus health, existence of high false positive result from the visual interpretation has a significant contribution to unnecessary surgical delivery or delayed intervention.
Objective
In the current study an innovative computer aided fetal distress diagnosing model is developed by using time frequency representation of FHR signal using generalized Morse wavelet and the concept of transfer learning of pre-trained ResNet 50 deep neural network model.
Method
From the CTG data that is obtained from the only open access CTU-UHB data base only FHR signal is extracted and preprocessed to remove noises and spikes. After preprocessing the time frequency information of FHR signal is extracted by using generalized Morse wavelet and fed to a pre-trained ResNet 50 model which is fine tuned and configured according to the dataset.
Main outcome measures
Sensitivity (Se), specificity (Sp) and accuracy (Acc) of the model adopted from binary confusion matrix is used as outcome measures.
Result
After successfully training the model, a comprehensive experimentation of testing is conducted for FHR data for which a recording is made during early stage of labor and last stage of labor. Thus, a promising classification result which is accuracy of 98.7%, sensitivity of 97.0% and specificity 100% are achieved for FHR signal of 1st stage of labor. For FHR recorded in last stage of labor, accuracy of 96.1%, sensitivity of 94.1% and specificity 97.7% are achieved.
Conclusion
The developed model can be used as a decision-making aid system for obstetrician and gynecologist.
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Zhang Y, Deng Y, Zhou Z, Zhang X, Jiao P, Zhao Z. Multimodal learning for fetal distress diagnosis using a multimodal medical information fusion framework. Front Physiol 2022; 13:1021400. [PMID: 36419838 PMCID: PMC9676934 DOI: 10.3389/fphys.2022.1021400] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2022] [Accepted: 10/25/2022] [Indexed: 09/08/2024] Open
Abstract
Cardiotocography (CTG) monitoring is an important medical diagnostic tool for fetal well-being evaluation in late pregnancy. In this regard, intelligent CTG classification based on Fetal Heart Rate (FHR) signals is a challenging research area that can assist obstetricians in making clinical decisions, thereby improving the efficiency and accuracy of pregnancy management. Most existing methods focus on one specific modality, that is, they only detect one type of modality and inevitably have limitations such as incomplete or redundant source domain feature extraction, and poor repeatability. This study focuses on modeling multimodal learning for Fetal Distress Diagnosis (FDD); however, exists three major challenges: unaligned multimodalities; failure to learn and fuse the causality and inclusion between multimodal biomedical data; modality sensitivity, that is, difficulty in implementing a task in the absence of modalities. To address these three issues, we propose a Multimodal Medical Information Fusion framework named MMIF, where the Category Constrained-Parallel ViT model (CCPViT) was first proposed to explore multimodal learning tasks and address the misalignment between multimodalities. Based on CCPViT, a cross-attention-based image-text joint component is introduced to establish a Multimodal Representation Alignment Network model (MRAN), explore the deep-level interactive representation between cross-modal data, and assist multimodal learning. Furthermore, we designed a simple-structured FDD test model based on the highly modal alignment MMIF, realizing task delegation from multimodal model training (image and text) to unimodal pathological diagnosis (image). Extensive experiments, including model parameter sensitivity analysis, cross-modal alignment assessment, and pathological diagnostic accuracy evaluation, were conducted to show our models' superior performance and effectiveness.
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Affiliation(s)
- Yefei Zhang
- College of Electronics and Information Engineering, Hangzhou Dianzi University, Hangzhou, China
| | - Yanjun Deng
- College of Electronics and Information Engineering, Hangzhou Dianzi University, Hangzhou, China
| | - Zhixin Zhou
- College of Electronics and Information Engineering, Hangzhou Dianzi University, Hangzhou, China
| | - Xianfei Zhang
- College of Electronics and Information Engineering, Hangzhou Dianzi University, Hangzhou, China
| | - Pengfei Jiao
- School of Cyberspace, Hangzhou Dianzi University, Hangzhou, China
| | - Zhidong Zhao
- School of Cyberspace, Hangzhou Dianzi University, Hangzhou, China
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Silva Neto MGD, Vale Madeiro JPD, Gomes DG. On designing a biosignal-based fetal state assessment system: A systematic mapping study. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2022; 216:106671. [PMID: 35144149 DOI: 10.1016/j.cmpb.2022.106671] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/11/2021] [Revised: 01/05/2022] [Accepted: 01/28/2022] [Indexed: 06/14/2023]
Abstract
BACKGROUND AND OBJECTIVE The patterns present in biosignals, such as fetal heart rate (FHR), are valuable indicators of fetal well-being. In designing biosignal analysis systems, the variety of approaches and technology usage impairs the decision-making for the fundamental units of the systems. There is a need for an updated overview of studies encompassing the biosignal-based fetal state assessment systems. Therefore, we propose a systematic mapping study to identify and synthesize recent research regarding the building blocks that compose these systems. METHODS We followed well-established guidelines to perform a systematic mapping of studies regarding the building-blocks that compose the fetal state assessment systems and published between January 2016 and January 2021. A search string was determined based on the mapping questions and the PI (population and intervention) divisions. The search string was applied in digital libraries covering the fields of computer science, engineering, and medical informatics. Then, we applied the forward snowballing technique to complement the resulting set. This process resulted in 75 primary studies selected from a total of 871 papers. RESULTS Selected studies were classified according to the publication types, systems design stages, datasets, and predictive capabilities. The results revealed that (i) The majority of the selected studies refer to the method as a type of publication and there is a lack of validation studies; (ii) The CTU-UHB was the most frequent biosignal-based dataset and UCI-CTG was the most frequent feature-based data; (iii) The selected studies made use of the system design stages alone or in a mixed-mode. CONCLUSION The results indicated that the well-established classification models achieved competitive results compared with the state-of-the-art methods in data-constrained system designs. Moreover, we identified the need for validation studies in the clinical environment.
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Affiliation(s)
| | - João Paulo do Vale Madeiro
- Department of Engineering of Teleinformatics, Federal University of Ceará, Ceará, Fortaleza 60455-900, Brazil
| | - Danielo G Gomes
- Department of Engineering of Teleinformatics, Federal University of Ceará, Ceará, Fortaleza 60455-900, Brazil
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9
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Automated Digitization of the Cardiotocography Signals from Real Scene Image of Binary Clinic Report. Ing Rech Biomed 2022. [DOI: 10.1016/j.irbm.2022.02.001] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
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10
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Esteban-Escaño J, Castán B, Castán S, Chóliz-Ezquerro M, Asensio C, Laliena AR, Sanz-Enguita G, Sanz G, Esteban LM, Savirón R. Machine Learning Algorithm to Predict Acidemia Using Electronic Fetal Monitoring Recording Parameters. ENTROPY 2021; 24:e24010068. [PMID: 35052094 PMCID: PMC8775221 DOI: 10.3390/e24010068] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/18/2021] [Revised: 12/18/2021] [Accepted: 12/27/2021] [Indexed: 12/17/2022]
Abstract
Background: Electronic fetal monitoring (EFM) is the universal method for the surveillance of fetal well-being in intrapartum. Our objective was to predict acidemia from fetal heart signal features using machine learning algorithms. Methods: A case–control 1:2 study was carried out compromising 378 infants, born in the Miguel Servet University Hospital, Spain. Neonatal acidemia was defined as pH < 7.10. Using EFM recording logistic regression, random forest and neural networks models were built to predict acidemia. Validation of models was performed by means of discrimination, calibration, and clinical utility. Results: Best performance was attained using a random forest model built with 100 trees. The discrimination ability was good, with an area under the Receiver Operating Characteristic curve (AUC) of 0.865. The calibration showed a slight overestimation of acidemia occurrence for probabilities above 0.4. The clinical utility showed that for 33% cutoff point, missing 5% of acidotic cases, 46% of unnecessary cesarean sections could be prevented. Logistic regression and neural networks showed similar discrimination ability but with worse calibration and clinical utility. Conclusions: The combination of the variables extracted from EFM recording provided a predictive model of acidemia that showed good accuracy and provides a practical tool to prevent unnecessary cesarean sections.
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Affiliation(s)
- Javier Esteban-Escaño
- Department of Electronic Engineering and Communications, Escuela Universitaria Politécnica de La Almunia, Universidad de Zaragoza, Calle Mayor 5, 50100 La Almunia de Doña Godina, Spain;
| | - Berta Castán
- Department of Obstetrics and Gynecology, San Pedro Hospital, Calle Piqueras 98, 26006 Logroño, Spain;
| | - Sergio Castán
- Department of Obstetrics and Gynecology, Miguel Servet University Hospital, Paseo Isabel La Católica 3, 50009 Zaragoza, Spain
- Correspondence: (S.C.); (L.M.E.)
| | - Marta Chóliz-Ezquerro
- Department of Obstetrics, Dexeus University Hospital, Gran Via de Carles III 71-75, 08028 Barcelona, Spain;
| | - César Asensio
- Department of Applied Mathematics, Escuela Universitaria Politécnica de La Almunia, Universidad de Zaragoza, Calle Mayor 5, 50100 La Almunia de Doña Godina, Spain; (C.A.); (A.R.L.)
| | - Antonio R. Laliena
- Department of Applied Mathematics, Escuela Universitaria Politécnica de La Almunia, Universidad de Zaragoza, Calle Mayor 5, 50100 La Almunia de Doña Godina, Spain; (C.A.); (A.R.L.)
| | - Gerardo Sanz-Enguita
- Department of Applied Physics, Escuela Universitaria Politécnica de La Almunia, Universidad de Zaragoza, Calle Mayor 5, 50100 La Almunia de Doña Godina, Spain;
| | - Gerardo Sanz
- Department of Statistical Methods and Institute for Biocomputation and Physics of Complex Systems-BIFI, University of Zaragoza, Calle Pedro Cerbuna 12, 50009 Zaragoza, Spain;
| | - Luis Mariano Esteban
- Department of Applied Mathematics, Escuela Universitaria Politécnica de La Almunia, Universidad de Zaragoza, Calle Mayor 5, 50100 La Almunia de Doña Godina, Spain; (C.A.); (A.R.L.)
- Correspondence: (S.C.); (L.M.E.)
| | - Ricardo Savirón
- Department of Obstetrics and Gynecology, Hospital Clínico San Carlos and Instituto de Investigación Sanitaria San Carlos (IdISSC), Universidad Complutense, Calle del Prof Martín Lagos s/n, 28040 Madrid, Spain;
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Ponsiglione AM, Amato F, Romano M. Multiparametric Investigation of Dynamics in Fetal Heart Rate Signals. Bioengineering (Basel) 2021; 9:bioengineering9010008. [PMID: 35049717 PMCID: PMC8772900 DOI: 10.3390/bioengineering9010008] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2021] [Revised: 12/15/2021] [Accepted: 12/21/2021] [Indexed: 11/16/2022] Open
Abstract
In the field of electronic fetal health monitoring, computerized analysis of fetal heart rate (FHR) signals has emerged as a valid decision-support tool in the assessment of fetal wellbeing. Despite the availability of several approaches to analyze the variability of FHR signals (namely the FHRV), there are still shadows hindering a comprehensive understanding of how linear and nonlinear dynamics are involved in the control of the fetal heart rhythm. In this study, we propose a straightforward processing and modeling route for a deeper understanding of the relationships between the characteristics of the FHR signal. A multiparametric modeling and investigation of the factors influencing the FHR accelerations, chosen as major indicator of fetal wellbeing, is carried out by means of linear and nonlinear techniques, blockwise dimension reduction, and artificial neural networks. The obtained results show that linear features are more influential compared to nonlinear ones in the modeling of HRV in healthy fetuses. In addition, the results suggest that the investigation of nonlinear dynamics and the use of predictive tools in the field of FHRV should be undertaken carefully and limited to defined pregnancy periods and FHR mean values to provide interpretable and reliable information to clinicians and researchers.
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12
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Huang L, Jiang Z, Cai R, Li L, Chen Q, Hong J, Hao Z, Wei H. Investigating the interpretability of fetal status assessment using antepartum cardiotocographic records. BMC Med Inform Decis Mak 2021; 21:355. [PMID: 34930216 PMCID: PMC8686372 DOI: 10.1186/s12911-021-01714-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2020] [Accepted: 12/06/2021] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Cardiotocography (CTG) interpretation plays a critical role in prenatal fetal monitoring. However, the interpretation of fetal status assessment using CTG is mainly confined to clinical research. To the best of our knowledge, there is no study on data analysis of CTG records to explore the causal relationships between the important CTG features and fetal status evaluation. METHODS For analyses, 2126 cardiotocograms were automatically processed and the respective diagnostic features measured by the Sisporto program. In this paper, we aim to explore the causal relationships between the important CTG features and fetal status evaluation. First, we utilized data visualization and Spearman correlation analysis to explore the relationship among CTG features and their importance on fetal status assessment. Second, we proposed a forward-stepwise-selection association rule analysis (ARA) to supplement the fetal status assessment rules based on sparse pathological cases. Third, we established structural equation models (SEMs) to investigate the latent causal factors and their causal coefficients to fetal status assessment. RESULTS Data visualization and the Spearman correlation analysis found that thirteen CTG features were relevant to the fetal state evaluation. The forward-stepwise-selection ARA further validated and complemented the CTG interpretation rules in the fetal monitoring guidelines. The measurement models validated the five latent variables, which were baseline category (BCat), variability category (VCat), acceleration category (ACat), deceleration category (DCat) and uterine contraction category (UCat) based on fetal monitoring knowledge and the above analyses. Furthermore, the interpretable models discovered the cause factors of fetal status assessment and their causal coefficients to fetal status assessment. For instance, VCat could predict BCat, and UCat could predict DCat as well. ACat, BCat and DCat directly affected fetal status assessment, where ACat was the important causal factor. CONCLUSIONS The analyses revealed the interpretation rules and discovered the causal factors and their causal coefficients for fetal status assessment. Moreover, the results are consistent with the computerized fetal monitoring and clinical knowledge. Our approaches are conducive to evidence-based medical research and realizing intelligent fetal monitoring.
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Affiliation(s)
- Liting Huang
- School of Computer, Guangdong University of Technology, Waihuan West Road, Guangzhou, China
- School of Medical Information Engineering, Guangzhou University of Chinese Medicine, Waihuandong Road, Guangzhou, China
| | - Zhiying Jiang
- School of Medical Information Engineering, Guangzhou University of Chinese Medicine, Waihuandong Road, Guangzhou, China
| | - Ruichu Cai
- School of Computer, Guangdong University of Technology, Waihuan West Road, Guangzhou, China
| | - Li Li
- The First Affiliated Hospital of Jinan University, Tianhe District People’s Hospital, Dongpu Road, Guangzhou, China
- Guangzhou Sanrui Medical Equipment Co, Gaoke Road, Guangzhou, China
| | - Qinqun Chen
- School of Medical Information Engineering, Guangzhou University of Chinese Medicine, Waihuandong Road, Guangzhou, China
| | - Jiaming Hong
- School of Medical Information Engineering, Guangzhou University of Chinese Medicine, Waihuandong Road, Guangzhou, China
| | - Zhifeng Hao
- Department of Mathematics, College of Science, Shantou University, Daxue Road, Shantou, 515063 China
| | - Hang Wei
- School of Medical Information Engineering, Guangzhou University of Chinese Medicine, Waihuandong Road, Guangzhou, China
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Ribeiro M, Monteiro-Santos J, Castro L, Antunes L, Costa-Santos C, Teixeira A, Henriques TS. Non-linear Methods Predominant in Fetal Heart Rate Analysis: A Systematic Review. Front Med (Lausanne) 2021; 8:661226. [PMID: 34917624 PMCID: PMC8669823 DOI: 10.3389/fmed.2021.661226] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2021] [Accepted: 11/04/2021] [Indexed: 12/19/2022] Open
Abstract
The analysis of fetal heart rate variability has served as a scientific and diagnostic tool to quantify cardiac activity fluctuations, being good indicators of fetal well-being. Many mathematical analyses were proposed to evaluate fetal heart rate variability. We focused on non-linear analysis based on concepts of chaos, fractality, and complexity: entropies, compression, fractal analysis, and wavelets. These methods have been successfully applied in the signal processing phase and increase knowledge about cardiovascular dynamics in healthy and pathological fetuses. This review summarizes those methods and investigates how non-linear measures are related to each paper's research objectives. Of the 388 articles obtained in the PubMed/Medline database and of the 421 articles in the Web of Science database, 270 articles were included in the review after all exclusion criteria were applied. While approximate entropy is the most used method in classification papers, in signal processing, the most used non-linear method was Daubechies wavelets. The top five primary research objectives covered by the selected papers were detection of signal processing, hypoxia, maturation or gestational age, intrauterine growth restriction, and fetal distress. This review shows that non-linear indices can be used to assess numerous prenatal conditions. However, they are not yet applied in clinical practice due to some critical concerns. Some studies show that the combination of several linear and non-linear indices would be ideal for improving the analysis of the fetus's well-being. Future studies should narrow the research question so a meta-analysis could be performed, probing the indices' performance.
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Affiliation(s)
- Maria Ribeiro
- Institute for Systems and Computer Engineering, Technology and Science, Porto, Portugal.,Computer Science Department, Faculty of Sciences, University of Porto, Porto, Portugal
| | - João Monteiro-Santos
- Centre for Health Technology and Services Research, Faculty of Medicine University of Porto, Porto, Portugal.,Department of Community Medicine, Information and Health Decision Sciences, Faculty of Medicine, University of Porto, Porto, Portugal
| | - Luísa Castro
- Centre for Health Technology and Services Research, Faculty of Medicine University of Porto, Porto, Portugal.,Department of Community Medicine, Information and Health Decision Sciences, Faculty of Medicine, University of Porto, Porto, Portugal.,School of Health of Polytechnic of Porto, Porto, Portugal
| | - Luís Antunes
- Institute for Systems and Computer Engineering, Technology and Science, Porto, Portugal.,Computer Science Department, Faculty of Sciences, University of Porto, Porto, Portugal
| | - Cristina Costa-Santos
- Centre for Health Technology and Services Research, Faculty of Medicine University of Porto, Porto, Portugal.,Department of Community Medicine, Information and Health Decision Sciences, Faculty of Medicine, University of Porto, Porto, Portugal
| | - Andreia Teixeira
- Centre for Health Technology and Services Research, Faculty of Medicine University of Porto, Porto, Portugal.,Department of Community Medicine, Information and Health Decision Sciences, Faculty of Medicine, University of Porto, Porto, Portugal.,Instituto Politécnico de Viana do Castelo, Viana do Castelo, Portugal
| | - Teresa S Henriques
- Centre for Health Technology and Services Research, Faculty of Medicine University of Porto, Porto, Portugal.,Department of Community Medicine, Information and Health Decision Sciences, Faculty of Medicine, University of Porto, Porto, Portugal
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Ponsiglione AM, Cosentino C, Cesarelli G, Amato F, Romano M. A Comprehensive Review of Techniques for Processing and Analyzing Fetal Heart Rate Signals. SENSORS (BASEL, SWITZERLAND) 2021; 21:6136. [PMID: 34577342 PMCID: PMC8469481 DOI: 10.3390/s21186136] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/28/2021] [Revised: 09/04/2021] [Accepted: 09/10/2021] [Indexed: 02/07/2023]
Abstract
The availability of standardized guidelines regarding the use of electronic fetal monitoring (EFM) in clinical practice has not effectively helped to solve the main drawbacks of fetal heart rate (FHR) surveillance methodology, which still presents inter- and intra-observer variability as well as uncertainty in the classification of unreassuring or risky FHR recordings. Given the clinical relevance of the interpretation of FHR traces as well as the role of FHR as a marker of fetal wellbeing autonomous nervous system development, many different approaches for computerized processing and analysis of FHR patterns have been proposed in the literature. The objective of this review is to describe the techniques, methodologies, and algorithms proposed in this field so far, reporting their main achievements and discussing the value they brought to the scientific and clinical community. The review explores the following two main approaches to the processing and analysis of FHR signals: traditional (or linear) methodologies, namely, time and frequency domain analysis, and less conventional (or nonlinear) techniques. In this scenario, the emerging role and the opportunities offered by Artificial Intelligence tools, representing the future direction of EFM, are also discussed with a specific focus on the use of Artificial Neural Networks, whose application to the analysis of accelerations in FHR signals is also examined in a case study conducted by the authors.
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Affiliation(s)
- Alfonso Maria Ponsiglione
- Department of Electrical Engineering and Information Technology (DIETI), University of Naples Federico II, Via Claudio 21, 80125 Naples, Italy; (A.M.P.); (F.A.)
| | - Carlo Cosentino
- Department of Experimental and Clinical Medicine ‘Gaetano Salvatore’, University Magna Graecia of Catanzaro, Viale Tommaso Campanella 185, 88100 Catanzaro, Italy;
| | - Giuseppe Cesarelli
- Department of Chemical, Materials and Production Engineering (DICMaPI), University of Naples Federico II, Piazzale Tecchio 80, 80125 Naples, Italy;
| | - Francesco Amato
- Department of Electrical Engineering and Information Technology (DIETI), University of Naples Federico II, Via Claudio 21, 80125 Naples, Italy; (A.M.P.); (F.A.)
| | - Maria Romano
- Department of Electrical Engineering and Information Technology (DIETI), University of Naples Federico II, Via Claudio 21, 80125 Naples, Italy; (A.M.P.); (F.A.)
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Choi JY, Seo K, Cho JS, Moon KD. Applying convolutional neural networks to assess the external quality of strawberries. J Food Compost Anal 2021. [DOI: 10.1016/j.jfca.2021.104071] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
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Xie W, Wei S, Zheng Z, Jiang Y, Yang D. Recognition of Defective Carrots Based on Deep Learning and Transfer Learning. FOOD BIOPROCESS TECH 2021. [DOI: 10.1007/s11947-021-02653-8] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
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Alhudhaif A, Cömert Z, Polat K. Otitis media detection using tympanic membrane images with a novel multi-class machine learning algorithm. PeerJ Comput Sci 2021; 7:e405. [PMID: 33817048 PMCID: PMC7959604 DOI: 10.7717/peerj-cs.405] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2020] [Accepted: 01/30/2021] [Indexed: 05/10/2023]
Abstract
BACKGROUND Otitis media (OM) is the infection and inflammation of the mucous membrane covering the Eustachian with the airy cavities of the middle ear and temporal bone. OM is also one of the most common ailments. In clinical practice, the diagnosis of OM is carried out by visual inspection of otoscope images. This vulnerable process is subjective and error-prone. METHODS In this study, a novel computer-aided decision support model based on the convolutional neural network (CNN) has been developed. To improve the generalized ability of the proposed model, a combination of the channel and spatial model (CBAM), residual blocks, and hypercolumn technique is embedded into the proposed model. All experiments were performed on an open-access tympanic membrane dataset that consists of 956 otoscopes images collected into five classes. RESULTS The proposed model yielded satisfactory classification achievement. The model ensured an overall accuracy of 98.26%, sensitivity of 97.68%, and specificity of 99.30%. The proposed model produced rather superior results compared to the pre-trained CNNs such as AlexNet, VGG-Nets, GoogLeNet, and ResNets. Consequently, this study points out that the CNN model equipped with the advanced image processing techniques is useful for OM diagnosis. The proposed model may help to field specialists in achieving objective and repeatable results, decreasing misdiagnosis rate, and supporting the decision-making processes.
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Affiliation(s)
- Adi Alhudhaif
- Department of Computer Science, College of Computer Engineering and Sciences in Al-kharj, Prince Sattam bin Abdulaziz University, Alkharj, Saudi Arabia
| | - Zafer Cömert
- Department of Software Engineering, Samsun University, Samsun, Turkey
| | - Kemal Polat
- Department of Electrical and Electronics Engineering, Faculty of Engineering, Bolu Abant Izzet Baysal University, Bolu, Turkey
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Phase Entropy Analysis of Electrohysterographic Data at the Third Trimester of Human Pregnancy and Active Parturition. ENTROPY 2020; 22:e22080798. [PMID: 33286569 PMCID: PMC7517370 DOI: 10.3390/e22080798] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/28/2020] [Revised: 07/08/2020] [Accepted: 07/11/2020] [Indexed: 12/14/2022]
Abstract
Phase Entropy (PhEn) was recently introduced for evaluating the nonlinear features of physiological time series. PhEn has been demonstrated to be a robust approach in comparison to other entropy-based methods to achieve this goal. In this context, the present study aimed to analyze the nonlinear features of raw electrohysterogram (EHG) time series collected from women at the third trimester of pregnancy (TT) and later during term active parturition (P) by PhEn. We collected 10-min longitudinal transabdominal recordings of 24 low-risk pregnant women at TT (from 35 to 38 weeks of pregnancy) and P (>39 weeks of pregnancy). We computed the second-order difference plots (SODPs) for the TT and P stages, and we evaluated the PhEn by modifying the k value, a coarse-graining parameter. Our results pointed out that PhEn in TT is characterized by a higher likelihood of manifesting nonlinear dynamics compared to the P condition. However, both conditions maintain percentages of nonlinear series higher than 66%. We conclude that the nonlinear features appear to be retained for both stages of pregnancy despite the uterine and cervical reorganization process that occurs in the transition from the third trimester to parturition.
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Toğaçar M, Ergen B, Cömert Z. Detection of lung cancer on chest CT images using minimum redundancy maximum relevance feature selection method with convolutional neural networks. Biocybern Biomed Eng 2020. [DOI: 10.1016/j.bbe.2019.11.004] [Citation(s) in RCA: 59] [Impact Index Per Article: 14.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
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Wolf H, Bruin C, Dobbe JGG, Gordijn SJ, Ganzevoort W. Computerized fetal cardiotocography analysis in early preterm fetal growth restriction - a quantitative comparison of two applications. J Perinat Med 2019; 47:439-447. [PMID: 31005952 DOI: 10.1515/jpm-2018-0412] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/10/2018] [Accepted: 02/08/2019] [Indexed: 11/15/2022]
Abstract
Background We developed an open-source software for the computerized analysis of antenatal fetal cardiotocography (CTG) without limitation of duration of the registration, enabling batch processing and adaptation to any digital storage system. Methods STVcalc was developed based on literature about the FetalCare system (Huntleigh Healthcare Ltd, Cardiff, UK). For comparison with FetalCare, we selected the CTGs of all women who delivered in 2011 a small-for-gestational-age (SGA) fetus between 24 and 31 weeks by cesarean section (CS) for fetal distress, or had fetal death, before labor onset. Results In 471 CTGs from 39 women, the agreement was 99% for a short-term variation (STV) cut-off of 2.6 ms below 29 weeks and 3.0 ms thereafter, and 95% for 3.5 and 4.0 ms, respectively. In 18 (4%) cases, the proportional difference in STV between FetalCare and STVcalc was more than 10%. Conclusion As only slight differences were observed between the proposed feature-rich application and the FetalCare system, it can be considered valuable for clinical practice and research purposes.
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Affiliation(s)
- Hans Wolf
- Department of Obstetrics, Amsterdam University Medical CenterAmsterdam, The Netherlands
| | - Claartje Bruin
- Department of Obstetrics, Amsterdam University Medical CenterAmsterdam, The Netherlands
| | - Johannes G G Dobbe
- Department of Biomedical Engineering and Physics, Amsterdam University Medical Center, Amsterdam, The Netherlands
| | - Sanne J Gordijn
- Department of Obstetrics, University Medical Center Groningen, Groningen, The Netherlands
| | - Wessel Ganzevoort
- Department of Obstetrics, Amsterdam University Medical CenterAmsterdam, The Netherlands
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Zarmehri MN, Castro L, Santos J, Bernardes J, Costa A, Santos CC. On the prediction of foetal acidaemia: A spectral analysis-based approach. Comput Biol Med 2019; 109:235-241. [PMID: 31085380 DOI: 10.1016/j.compbiomed.2019.04.041] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2018] [Revised: 04/30/2019] [Accepted: 04/30/2019] [Indexed: 10/26/2022]
Abstract
A computational analysis of physiological systems has been used to support the understanding of how these systems work, and in the case of foetal heart rate, many different approaches have been developed in the last decades. Our objective was to apply a new method of classification, which is based on spectral analysis, in foetal heart rate (FHR) traces to predict foetal acidosis diagnosed with umbilical arterial blood pH ≤ 7.05. Fast Fourier transform was applied to a real database for the classification approach. To evaluate the models, sensitivity, specificity, and area under the receiver operating characteristic (ROC) curve were used. Sensitivity equal to 1, specificity equal to 0.85 and an area under the ROC curve of 0.94 were found. In addition, when the definition of metabolic acidosis of umbilical arterial blood pH ≤ 7.05 and base excess ≤ -10 mmol/L was used, the proposed methodology obtained sensitivity = 1, specificity = 0.97 and area under the ROC curve = 0.98. The proposed methodology relies exclusively on the spectral frequency decomposition of the FHR signal. After further successful validation in more datasets, this approach can be incorporated easily in clinical practice due to its simple implementation. Likewise, the incorporation of this novel technique in an intrapartum monitoring station should be straightforward, thus enabling the assistance of labour professionals in the anticipated detection of acidaemia.
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Affiliation(s)
| | - Luísa Castro
- INESC TEC, Porto, Portugal; Center for Health Technology and Services Research - CINTESIS, University of Porto, Porto, Portugal.
| | - João Santos
- Center for Health Technology and Services Research - CINTESIS, University of Porto, Porto, Portugal
| | - João Bernardes
- Center for Health Technology and Services Research - CINTESIS, University of Porto, Porto, Portugal; Department of Obstetrics and Gynecology, Faculty of Medicine, University of Porto, Portugal
| | - Antónia Costa
- Hospital Pedro Hispano, Unidade Local de Saúde de Matosinhos, Portugal
| | - Cristina Costa Santos
- Center for Health Technology and Services Research - CINTESIS, University of Porto, Porto, Portugal; Health Information and Decision Sciences Department - MEDCIDS, Faculty of Medicine, University of Porto, Porto, Portugal
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Romagnoli S, Sbrollini A, Burattini L, Marcantoni I, Morettini M, Burattini L. Digital cardiotocography: What is the optimal sampling frequency? Biomed Signal Process Control 2019. [DOI: 10.1016/j.bspc.2019.02.016] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/27/2022]
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