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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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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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3
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Cao Q, Sun H, Wang H, Liu X, Lu Y, Huo L. Comparative study of neonatal brain injury fetuses using machine learning methods for perinatal data. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2023; 240:107701. [PMID: 37480645 DOI: 10.1016/j.cmpb.2023.107701] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/04/2021] [Revised: 06/20/2023] [Accepted: 06/28/2023] [Indexed: 07/24/2023]
Abstract
OBJECTIVE CTG is used to record the fetus's fetal heart rate and uterine contraction signal during pregnancy. The prenatal fetal intrauterine monitoring level can be used to evaluate the fetal intrauterine safety status and reduce the morbidity and mortality of the perinatal fetus. Perinatal asphyxia is the leading cause of neonatal hypoxic-ischemic encephalopathy and one of the leading causes of neonatal death and disability. Severe asphyxia can cause brain and permanent nervous system damage and leave different degrees of nervous system sequelae. METHODS This paper evaluates the classification performance of several machine learning methods on CTG and provides the auxiliary ability of clinical judgment of doctors. This paper uses the data set on the public database UCI, with 2126 samples. RESULTS The accuracy of each model exceeds 80%, of which XGBoost has the highest accuracy of 91%. Other models are Random tree (90%), light (90%), Decision tree (83%), and KNN (81%). The performance of the model in other indicators is XGBoost (precision: 90%, recall: 93%, F1 score: 90%), Random tree (precision: 88%, recall: 91%, F1 score: 89%), lightGBM (precision: 87%, recall: 93%, F1 score: 90%), Decision tree (precision: 83%, recall: 86%, F1 score: 84%), KNN (precision: 77%, recall: 85%, F1 score: 81%). CONCLUSION The performance of XGBoost is the best of all models. This result also shows that using the machine learning method to evaluate the fetus's health status in CTG data is feasible. This will also provide and assist doctors with an objective assessment to assist in clinical diagnosis.
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Affiliation(s)
- Qingjun Cao
- Department of Pediatrics, Shengjing Hospital of China Medical University, Shenyang 110004, China
| | - Hongzan Sun
- Department of Radiology, Shengjing Hospital of China Medical University, Shenyang 110004, China
| | - Hua Wang
- Department of Pediatrics, Shengjing Hospital of China Medical University, Shenyang 110004, China
| | - Xueyan Liu
- Department of Pediatrics, Shengjing Hospital of China Medical University, Shenyang 110004, China
| | - Yu Lu
- College of Big Data and Internet, Shenzhen Technology University, Shenzhen 518118, China
| | - Liang Huo
- Department of Pediatrics, Shengjing Hospital of China Medical University, Shenyang 110004, China.
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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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Kim R, Lin T, Pang G, Liu Y, Tungate AS, Hendry PL, Kurz MC, Peak DA, Jones J, Rathlev NK, Swor RA, Domeier R, Velilla MA, Lewandowski C, Datner E, Pearson C, Lee D, Mitchell PM, McLean SA, Linnstaedt SD. Derivation and validation of risk prediction for posttraumatic stress symptoms following trauma exposure. Psychol Med 2023; 53:4952-4961. [PMID: 35775366 DOI: 10.1017/s003329172200191x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
BACKGROUND Posttraumatic stress symptoms (PTSS) are common following traumatic stress exposure (TSE). Identification of individuals with PTSS risk in the early aftermath of TSE is important to enable targeted administration of preventive interventions. In this study, we used baseline survey data from two prospective cohort studies to identify the most influential predictors of substantial PTSS. METHODS Self-identifying black and white American women and men (n = 1546) presenting to one of 16 emergency departments (EDs) within 24 h of motor vehicle collision (MVC) TSE were enrolled. Individuals with substantial PTSS (⩾33, Impact of Events Scale - Revised) 6 months after MVC were identified via follow-up questionnaire. Sociodemographic, pain, general health, event, and psychological/cognitive characteristics were collected in the ED and used in prediction modeling. Ensemble learning methods and Monte Carlo cross-validation were used for feature selection and to determine prediction accuracy. External validation was performed on a hold-out sample (30% of total sample). RESULTS Twenty-five percent (n = 394) of individuals reported PTSS 6 months following MVC. Regularized linear regression was the top performing learning method. The top 30 factors together showed good reliability in predicting PTSS in the external sample (Area under the curve = 0.79 ± 0.002). Top predictors included acute pain severity, recovery expectations, socioeconomic status, self-reported race, and psychological symptoms. CONCLUSIONS These analyses add to a growing literature indicating that influential predictors of PTSS can be identified and risk for future PTSS estimated from characteristics easily available/assessable at the time of ED presentation following TSE.
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Affiliation(s)
- Raphael Kim
- Institute for Trauma Recovery, University of North Carolina, Chapel Hill, NC, USA
- Department of Anesthesiology, University of North Carolina, Chapel Hill, NC, USA
- Department of Computer Science, University of North Carolina, Chapel Hill, NC, USA
- Department of Statistics and Operations Research, University of North Carolina, Chapel Hill, NC, USA
| | - Tina Lin
- Institute for Trauma Recovery, University of North Carolina, Chapel Hill, NC, USA
- Department of Anesthesiology, University of North Carolina, Chapel Hill, NC, USA
| | - Gehao Pang
- Institute for Trauma Recovery, University of North Carolina, Chapel Hill, NC, USA
- Department of Anesthesiology, University of North Carolina, Chapel Hill, NC, USA
| | - Yufeng Liu
- Department of Statistics and Operations Research, University of North Carolina, Chapel Hill, NC, USA
- Department of Biostatistics, University of North Carolina, Chapel Hill, NC, USA
- Department of Genetics, Carolina Center for Genome Sciences, Lineberger Comprehensive Cancer Center, University of North Carolina, Chapel Hill, NC, USA
| | - Andrew S Tungate
- Institute for Trauma Recovery, University of North Carolina, Chapel Hill, NC, USA
- Department of Anesthesiology, University of North Carolina, Chapel Hill, NC, USA
| | - Phyllis L Hendry
- Department of Emergency Medicine, University of Florida College of Medicine, Jacksonville, FL, USA
| | - Michael C Kurz
- Department of Emergency Medicine, University of Alabama, Birmingham, AL, USA
| | - David A Peak
- Department of Emergency Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Jeffrey Jones
- Department of Emergency Medicine, Spectrum Health Butterworth Campus, Grand Rapids, MI, USA
| | - Niels K Rathlev
- Department of Emergency Medicine, Baystate State Health System, Springfield, MA, USA
| | - Robert A Swor
- Department of Emergency Medicine, Beaumont Hospital, Royal Oak, MI, USA
| | - Robert Domeier
- Department of Emergency Medicine, St Joseph Mercy Health System, Ann Arbor, MI, USA
| | | | | | - Elizabeth Datner
- Department of Emergency Medicine, Albert Einstein Medical Center, Philadelphia, PA, USA
| | - Claire Pearson
- Department of Emergency Medicine, Detroit Receiving, Detroit, MI, USA
| | - David Lee
- Department of Emergency Medicine, North Shore University Hospital, Manhasset, NY, USA
| | - Patricia M Mitchell
- Department of Emergency Medicine, Boston University School of Medicine, Boston, MA, USA
| | - Samuel A McLean
- Institute for Trauma Recovery, University of North Carolina, Chapel Hill, NC, USA
- Department of Anesthesiology, University of North Carolina, Chapel Hill, NC, USA
- Department of Emergency Medicine, University of North Carolina, Chapel Hill, NC, USA
| | - Sarah D Linnstaedt
- Institute for Trauma Recovery, University of North Carolina, Chapel Hill, NC, USA
- Department of Anesthesiology, University of North Carolina, Chapel Hill, NC, USA
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Detection of Suspicious Cardiotocographic Recordings by Means of a Machine Learning Classifier. Bioengineering (Basel) 2023; 10:bioengineering10020252. [PMID: 36829746 PMCID: PMC9952623 DOI: 10.3390/bioengineering10020252] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2023] [Revised: 02/06/2023] [Accepted: 02/13/2023] [Indexed: 02/17/2023] Open
Abstract
Cardiotocography (CTG) is one of the fundamental prenatal diagnostic methods for both antepartum and intrapartum fetal surveillance. Although it has allowed a significant reduction in intrapartum and neonatal mortality and morbidity, its diagnostic accuracy is, however, still far from being fully satisfactory. In particular, the identification of uncertain and suspicious CTG traces remains a challenging task for gynecologists. The introduction of computerized analysis systems has enabled more objective evaluations, possibly leading to more accurate diagnoses. In this work, the problem of classifying suspicious CTG recordings was addressed through a machine learning approach. A machine-based labeling was proposed, and a binary classification was carried out using a support vector machine (SVM) classifier to distinguish between suspicious and normal CTG traces. The best classification metrics showed accuracy, sensitivity, and specificity values of 92%, 92%, and 90%, respectively. The main results were compared both with results obtained by considering a more unbalanced dataset and with relevant literature studies in the field. The use of the SVM proved to be promising in the field of CTG classification. However, appropriate feature selection and dataset balancing are crucial to achieve satisfactory performance of the classifier.
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Liang H, Lu Y. A CNN-RNN unified framework for intrapartum cardiotocograph classification. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2023; 229:107300. [PMID: 36566652 DOI: 10.1016/j.cmpb.2022.107300] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/17/2021] [Revised: 11/30/2022] [Accepted: 12/03/2022] [Indexed: 06/17/2023]
Abstract
BACKGROUND AND OBJECTIVE Prenatal fetal monitoring, which can monitor the growth and health of the fetus, is very vital for pregnant women before delivery. During pregnancy, it is crucial to judge whether the fetus is abnormal, which helps obstetricians carry out early intervention to avoid fetal hypoxia and even death. At present, clinical fetal monitoring widely used fetal heart rate monitoring equipment. Fetal heart rate and uterine contraction signals obtained by fetal heart monitoring equipment are important information to evaluate fetal health status. METHODS This paper is based on 1D-CNN (One Dimension Convolutional Neural Network) and GRU (Gate Recurrent Unit). We preprocess the obtained data and enhances them, to make the proportion of number of instances in different class in the training set is same. RESULTS In model performance evaluation, standard evaluation indicators are used, such as accuracy, sensitivity, specificity, and ROC (receiver operating characteristic). Finally, the accuracy of our model in the test set is 95.15%, the sensitivity is 96.20%, and the specificity is 94.09%. CONCLUSIONS In fetal heart rate monitoring, this paper proposes a 1D-CNN and bidirectional GRU hybrid models, and the fetal heart rate and uterine contraction signals given by monitoring are used as input feature to classify the fetal health status. The results show that our approach is effective in evaluating fetal health status and can assists obstetricians in clinical decision-making. And provide a baseline for the introduction of 1D-CNN and bidirectional GRU hybrid models into the evaluation of fetal health status.
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Affiliation(s)
- Huanwen Liang
- College of Big Data and Internet, Shenzhen Technology University, Shenzhen, China; College of Applied Science, Shenzhen University, Shenzhen, China
| | - Yu Lu
- College of Big Data and Internet, Shenzhen Technology University, Shenzhen, China.
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Aeberhard JL, Radan AP, Delgado-Gonzalo R, Strahm KM, Sigurthorsdottir HB, Schneider S, Surbek D. Artificial intelligence and machine learning in cardiotocography: A scoping review. Eur J Obstet Gynecol Reprod Biol 2023; 281:54-62. [PMID: 36535071 DOI: 10.1016/j.ejogrb.2022.12.008] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2022] [Revised: 10/19/2022] [Accepted: 12/05/2022] [Indexed: 12/13/2022]
Abstract
INTRODUCTION Artificial intelligence (AI) is gaining more interest in the field of medicine due to its capacity to learn patterns directly from data. This becomes interesting for the field of cardiotocography (CTG) interpretation, since it promises to remove existing biases and improve the well-known issues of inter- and intra-observer variability. MATERIAL AND METHODS The objective of this study was to map current knowledge in AI-assisted interpretation of CTG tracings and thus, to present different approaches with their strengths, gaps, and limitations. The search was performed on Ovid Medline and PubMed databases. The Preferred Reporting Items for Systematic Reviews and meta-Analysis for Scoping Reviews (PRISMA-ScR) guidelines were followed. RESULTS We summarized 40 different studies investigating at least one algorithm or system to classify CTG tracings. In addition, the Oxford Sonicaid system is presented because of its wide use in clinical practice. CONCLUSIONS There are several promising approaches in this area, but none of them has gained big acceptance in clinical practice. Further investigation and refinement of the algorithms and features are needed to achieve a validated decision-support system. For this purpose, larger quantities of curated and labeled data may be necessary.
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Affiliation(s)
| | - Anda-Petronela Radan
- Department of Obstetrics and Feto-maternal Medicine, University Hospital of Bern, Switzerland
| | | | - Karin Maya Strahm
- Department of Obstetrics and Feto-maternal Medicine, University Hospital of Bern, Switzerland
| | | | - Sophie Schneider
- Department of Obstetrics and Feto-maternal Medicine, University Hospital of Bern, Switzerland
| | - Daniel Surbek
- Department of Obstetrics and Feto-maternal Medicine, University Hospital of Bern, Switzerland
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Xiao Y, Lu Y, Liu M, Zeng R, Bai J. A deep feature fusion network for fetal state assessment. Front Physiol 2022; 13:969052. [PMID: 36531165 PMCID: PMC9748093 DOI: 10.3389/fphys.2022.969052] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2022] [Accepted: 11/15/2022] [Indexed: 09/05/2023] Open
Abstract
CTG (cardiotocography) has consistently been used to diagnose fetal hypoxia. It is susceptible to identifying the average fetal acid-base balance but lacks specificity in recognizing prenatal acidosis and neurological impairment. CTG plays a vital role in intrapartum fetal state assessment, which can prevent severe organ damage if fetal hypoxia is detected earlier. In this paper, we propose a novel deep feature fusion network (DFFN) for fetal state assessment. First, we extract spatial and temporal information from the fetal heart rate (FHR) signal using a multiscale CNN-BiLSTM network, increasing the features' diversity. Second, the multiscale CNN-BiLSM network and frequently used features are integrated into the deep learning model. The proposed DFFN model combines different features to improve classification accuracy. The multiscale convolutional kernels can identify specific essential information and consider signal's temporal information. The proposed method achieves 61.97%, 73.82%, and 66.93% of sensitivity, specificity, and quality index, respectively, on the public CTU-UHB database. The proposed method achieves the highest QI on the private database, verifying the proposed method's effectiveness and generalization. The proposed DFFN combines the advantages of feature engineering and deep learning models and achieves competitive accuracy in fetal state assessment compared with related works.
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Affiliation(s)
- Yahui Xiao
- Guangdong Provincial Key Laboratory of Traditional Chinese Medicine Informatization, Department of Electronic Engineering, College of Information Science and Technology, Jinan University, Guangzhou, China
| | - Yaosheng Lu
- Guangdong Provincial Key Laboratory of Traditional Chinese Medicine Informatization, Department of Electronic Engineering, College of Information Science and Technology, Jinan University, Guangzhou, China
| | - Mujun Liu
- College of Science and Engineering Jinan University, Guangzhou, China
| | - Rongdan Zeng
- Guangdong Provincial Key Laboratory of Traditional Chinese Medicine Informatization, Department of Electronic Engineering, College of Information Science and Technology, Jinan University, Guangzhou, China
| | - Jieyun Bai
- Guangdong Provincial Key Laboratory of Traditional Chinese Medicine Informatization, Department of Electronic Engineering, College of Information Science and Technology, Jinan University, Guangzhou, China
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Al-yousif S, Najm IA, Talab HS, Hasan Al Qahtani N, Alfiras M, Al-Rawi OYM, Subhi Al-Dayyeni W, Amer Ahmed Alrawi A, Jabbar Mnati M, Jarrar M, Ghabban F, Al-Shareefi NA, Musa Jaber M, H. Saleh A, Md Tahir N, Najim HT, Taher M. Intrapartum cardiotocography trace pattern pre-processing, features extraction and fetal health condition diagnoses based on RCOG guideline. PeerJ Comput Sci 2022; 8:e1050. [PMID: 36092005 PMCID: PMC9454876 DOI: 10.7717/peerj-cs.1050] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2021] [Accepted: 06/30/2022] [Indexed: 06/15/2023]
Abstract
CONTEXT The computerization of both fetal heart rate (FHR) and intelligent classification modeling of the cardiotocograph (CTG) is one of the approaches that are utilized in assisting obstetricians in conducting initial interpretation based on (CTG) analysis. CTG tracing interpretation is crucial for the monitoring of the fetal status during weeks into the pregnancy and childbirth. Most contemporary studies rely on computer-assisted fetal heart rate (FHR) feature extraction and CTG categorization to determine the best precise diagnosis for tracking fetal health during pregnancy. Furthermore, through the utilization of a computer-assisted fetal monitoring system, the FHR patterns can be precisely detected and categorized. OBJECTIVE The goal of this project is to create a reliable feature extraction algorithm for the FHR as well as a systematic and viable classifier for the CTG through the utilization of the MATLAB platform, all the while adhering to the recognized Royal College of Obstetricians and Gynecologists (RCOG) recommendations. METHOD The compiled CTG data from spiky artifacts were cleaned by a specifically created application and compensated for missing data using the guidelines provided by RCOG and the MATLAB toolbox after the implemented data has been processed and the FHR fundamental features have been extracted, for example, the baseline, acceleration, deceleration, and baseline variability. This is followed by the classification phase based on the MATLAB environment. Next, using the guideline provided by the RCOG, the signals patterns of CTG were classified into three categories specifically as normal, abnormal (suspicious), or pathological. Furthermore, to ensure the effectiveness of the created computerized procedure and confirm the robustness of the method, the visual interpretation performed by five obstetricians is compared with the results utilizing the computerized version for the 150 CTG signals. RESULTS The attained CTG signal categorization results revealed that there is variability, particularly a trivial dissimilarity of approximately (+/-4 and 6) beats per minute (b.p.m.). It was demonstrated that obstetricians' observations coincide with algorithms based on deceleration type and number, except for acceleration values that differ by up to (+/-4). DISCUSSION The results obtained based on CTG interpretation showed that the utilization of the computerized approach employed in infirmaries and home care services for pregnant women is indeed suitable. CONCLUSIONS The classification based on CTG that was used for the interpretation of the FHR attribute as discussed in this study is based on the RCOG guidelines. The system is evaluated and validated by experts based on their expert opinions and was compared with the CTG feature extraction and classification algorithms developed using MATLAB.
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Affiliation(s)
- Shahad Al-yousif
- Research Centre, The University of Almashreq, Baghdad, Iraq
- College of Engineering, Department of Electrical & Electronic Engineering, Gulf University, Almasnad, Kingdom of Bahrain
- Department of Medical Instrumentation Engineering Techniques, Dijlah University College, Baghdad, Iraq
| | - Ihab A. Najm
- College of Engineering, Tikrit University, Tikrit, Iraq
| | - Hossam Subhi Talab
- Children Welfare Teaching Hospital, Medical City, (MD, CABP, CAB Neonatology), Baghdad, Iraq
| | - Nourah Hasan Al Qahtani
- Department of Obstetrics and Gynecology, College of Medicine, Imam Abdulrahman Bin Faisal University, Al Dammam, Saudi Arabia
| | - M. Alfiras
- College of Engineering, Department of Electrical & Electronic Engineering, Gulf University, Almasnad, Kingdom of Bahrain
| | - Osama YM Al-Rawi
- College of Engineering, Department of Electrical & Electronic Engineering, Gulf University, Almasnad, Kingdom of Bahrain
| | | | | | - Mohannad Jabbar Mnati
- Department of Electronic Technology, Institute of Technology Baghdad, Middle Technical University, Baghdad, Iraq
| | - Mu’taman Jarrar
- College of Medicine, Imam Abdulrahman Bin Faisal University, Al Dammam, Saudi Arabia
| | - Fahad Ghabban
- Department of Information Systems College of Computer Science and Engineering, Taibah University, Al Madinah Al Munawwarah, Saudi Arabia
| | - Nael A. Al-Shareefi
- College of Biomedical Informatics, University of Information Technology and Communications (UOITC), Baghdad, Iraq
| | - Mustafa Musa Jaber
- Al-Turath University College, Department of Computer Engineering, Baghdad, Iraq
- Department of Medical Instruments Engineering Techniques, Al-Farahidi University, Baghdad, Iraq
| | | | - Nooritawati Md Tahir
- Electrical Engineering Department, College of Engineering, Universiti Teknologi MARA, Shah Alam, Malaysia
- Integrative Pharmacogenomics Institute (iPROMISE), Universiti Teknologi MARA, Shah Alam, Malaysia
- Institute of Big Data Analytics and Artificial Intelligence (IBDAAI), Universiti Teknologi MARA, Shah Alam, Malaysia
| | - Huda T. Najim
- Department of Biomedical Engineering, University of Technology, Baghdad, Iraq
| | - Mayada Taher
- Department of Laser and Optoelectronics Engineering, University of Technology, Baghdad, Iraq
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Liang H, Lu Y, Liu Q, Fu X. Fully Automatic Classification of Cardiotocographic Signals with 1D-CNN and Bi-directional GRU. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2022; 2022:4590-4594. [PMID: 36086166 DOI: 10.1109/embc48229.2022.9871253] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Prenatal fetal monitoring, which can monitor the growth and health of the fetus, is vital for pregnant women before delivery. During pregnancy, it is essential to classify whether the fetus is abnormal, which helps physicians carry out early intervention to avoid fetal heart hypoxia and even death. Fetal heart rate and uterine contraction signals obtained by fetal heart monitoring equipment are essential to estimate fetal health status. In this paper, we pre-process the obtained data set and enhance them using Hermite interpolation on the abnormal classification in the samples. We use the 1D-CNN and GRU hybrid models to extract the abstract features of fetal heart rate and uterine contraction signals. Several evaluation metrics are used for evaluation, and the accuracy is 96 %, while the sensitivity is 95 %, and the specificity is 96 %. The experiments show the effectiveness of the proposed method, which can provide physicians and users with more stable, efficient, and convenient diagnosis and decision support.
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12
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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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13
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Zhang Y, Zhao Z, Deng Y, Zhang X, Zhang Y. Reconstruction of missing samples in antepartum and intrapartum FHR measurements via mini-batch-based minimized sparse dictionary learning. IEEE J Biomed Health Inform 2021; 26:276-288. [PMID: 34191734 DOI: 10.1109/jbhi.2021.3093647] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
Fetal Heart Rate(FHR), an important recording in Cardiotocography(CTG)-based fetal health status monitoring, is the only information that clinical obstetricians can directly obtain and use. A challenge, however, is that missing samples are very common in FHR due to various causes such as fetal movements and sensor malfunctions. The aim is the development of an inpainting tool which is suitable for different missing lengths q and various total missing percentages Q, as well as for use in online mode. This study focused on two major impediments to existing inpainting methods: the longer the missing length, the more difficult it is to recover with mathematical methods; the reliance on tens of thousands of training samples, and the computational burden caused by full batch-based dictionary learning algorithms. We present a regularized minimization approach to signal recovery, which combines a L0.6-norm minimized sparse dictionary learning algorithm (MSDL) and a model optimization strategy for using a mini-batch version for signal recovery. Using 100 FHR recordings with 2 protocols designed to simulate missing clinical data scenarios, the combined method performed favorably in terms of 5 data analysis metrics and 3 clinical indicators. Comparing 4 inpainting methods, we were able to prove the superiority of the proposed algorithm for both large q and large Q. The experimental results showed the lowest values (2.64 (MAE), 4.68 (RMSE)) when Q=5% with short interval lengths. The developed architecture provides a reference value for the practical application of recovering missing samples online.
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14
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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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15
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Zeng R, Lu Y, Long S, Wang C, Bai J. Cardiotocography signal abnormality classification using time-frequency features and Ensemble Cost-sensitive SVM classifier. Comput Biol Med 2021; 130:104218. [PMID: 33484945 DOI: 10.1016/j.compbiomed.2021.104218] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2020] [Revised: 01/10/2021] [Accepted: 01/11/2021] [Indexed: 01/14/2023]
Abstract
BACKGROUND Cardiotocography (CTG) signal abnormality classification plays an important role in the diagnosis of abnormal fetuses. This classification problem is made difficult by the non-stationary nature of CTG and the dataset imbalance. This paper introduces a novel application of Time-frequency (TF) features and Ensemble Cost-sensitive Support Vector Machine (ECSVM) classifier to tackle these problems. METHODS Firstly, CTG signals are converted into TF-domain representations by Continuous Wavelet Transform (CWT), Wavelet Coherence (WTC), and Cross-wavelet Transform (XWT). From these representations, a novel image descriptor is used to extract the TF features. Then, the linear feature is derived from the time-domain representation of the CTG signal. The linear and TF features are fed to the ECSVM classifier for prediction and classification of fetal outcome. RESULTS The TF features show the significant difference (p-value<0.05) in distinguishing abnormal CTG signals, but not for traditional nonlinear features. In ECSVM abnormality classification, using only linear features, the sensitivity, specificity, and quality index are 59.3%, 78.3%, and 68.1%, respectively, whereas more effective results (sensitivity: 85.2%, specificity: 66.1%, and quality index: 75.0%) are obtained using a combination of linear and TF features, with a performance improvement index of 10.1%. Especially, the area under the receiver operating characteristic curve (0.77 vs. 0.64) is significantly increased with the ECSVM vs. SVM. CONCLUSION Our method can greatly improve the classification results, especially for sensitivity. It improves the true positive rate of CTG abnormality classification and reduces the false positive rate, which may help detect and treat abnormal fetuses during labor.
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Affiliation(s)
- Rongdan Zeng
- Department of Electronic Engineering, College of Information Science and Technology, Jinan University, Guangzhou, China
| | - Yaosheng Lu
- Department of Electronic Engineering, College of Information Science and Technology, Jinan University, Guangzhou, China
| | - Shun Long
- Department of Computer Science, College of Information Science and Technology, Jinan University, Guangzhou, China
| | - Chuan Wang
- Department of Electronic Engineering, College of Information Science and Technology, Jinan University, Guangzhou, China
| | - Jieyun Bai
- Department of Electronic Engineering, College of Information Science and Technology, Jinan University, Guangzhou, China.
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16
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Fuentealba P, Illanes A, Ortmeier F. Cardiotocograph Data Classification Improvement by Using Empirical Mode Decomposition .. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2020; 2019:5646-5649. [PMID: 31947134 DOI: 10.1109/embc.2019.8856673] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
This work proposes to study the fetal heart rate (FHR) signal based on information about its dynamics as a signal resulting from the modulation by the autonomic nervous system. The analysis is performed using the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) technique. The main idea is to extract a set of signal features based on that technique and also conventional time-domain features proposed in the literature in order to study their performance by using a support vector machine (SVM) as a classifier. As a hypothesis, we postulate that by including CEEMDAN based features, the classification performance should improve compared with the performance achieved by conventional features. The proposed method has been evaluated using real FHR data extracted from the open access CTU-UHB database. Results show that the classification performance improved from 67, 6% using only conventional features, to 71, 7% by incorporating CEEMDAN based features.
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17
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Subasi A, Kadasa B, Kremic E. Classification of the Cardiotocogram Data for Anticipation of Fetal Risks using Bagging Ensemble Classifier. ACTA ACUST UNITED AC 2020. [DOI: 10.1016/j.procs.2020.02.248] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
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18
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Independent Analysis of Decelerations and Resting Periods through CEEMDAN and Spectral-Based Feature Extraction Improves Cardiotocographic Assessment. APPLIED SCIENCES-BASEL 2019. [DOI: 10.3390/app9245421] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/30/2023]
Abstract
Fetal monitoring is commonly based on the joint recording of the fetal heart rate (FHR) and uterine contraction signals obtained with a cardiotocograph (CTG). Unfortunately, CTG analysis is difficult, and the interpretation problems are mainly associated with the analysis of FHR decelerations. From that perspective, several approaches have been proposed to improve its analysis; however, the results obtained are not satisfactory enough for their implementation in clinical practice. Current clinical research indicates that a correct CTG assessment requires a good understanding of the fetal compensatory mechanisms. In previous works, we have shown that the complete ensemble empirical mode decomposition with adaptive noise, in combination with time-varying autoregressive modeling, may be useful for the analysis of those characteristics. In this work, based on this methodology, we propose to analyze the FHR deceleration episodes separately. The main hypothesis is that the proposed feature extraction strategy applied separately to the complete signal, deceleration episodes, and resting periods (between contractions), improves the CTG classification performance compared with the analysis of only the complete signal. Results reveal that by considering the complete signal, the classification performance achieved 81.7% quality. Then, including information extracted from resting periods, it improved to 83.2%.
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19
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Hybrid Cascade Forward Neural Network with Elman Neural Network for Disease Prediction. ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING 2019. [DOI: 10.1007/s13369-019-03829-3] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/27/2022]
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20
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Iraji MS. Prediction of fetal state from the cardiotocogram recordings using neural network models. Artif Intell Med 2019; 96:33-44. [PMID: 31164209 DOI: 10.1016/j.artmed.2019.03.005] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2018] [Revised: 02/02/2019] [Accepted: 03/17/2019] [Indexed: 02/05/2023]
Abstract
The combination of machine vision and soft computing approaches in the clinical decisions, using training data, can improve medical decisions and treatments. The cardiotocography (CTG) monitoring and uterine activity (UA) provides useful information about the condition of the fetus and the cesarean or natural delivery. The visual assessment by the pathologists takes a lot of time and may be incompatible. Therefore, creating a computer intelligent method to assess fetal wellbeing before the mother labour is very important. In this study, many diverse approaches are suggested for predicting fetal state classes based on artificial intelligence. The various topologies of multi-layer architecture of a sub-adaptive neuro fuzzy inference system (MLA-ANFIS) using multiple input features, neural networks (NN), deep stacked sparse auto-encoders (DSSAEs), and deep-ANFIS models are implemented on a CTG data set. Experimental results contributing to DSSAE are more accurate than other suggested techniques to predict fetal state. The proposed method achieved a sensitivity of 99.716, specificity of 97.500 and geometric mean of 98.602 with accuracy of 99.503.
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Affiliation(s)
- Mohammad Saber Iraji
- Faculty Member of Department of Computer Engineering and Information Technology, Payame Noor University (PNU), Iran.
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21
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Saleem S, Naqvi SS, Manzoor T, Saeed A, ur Rehman N, Mirza J. A Strategy for Classification of "Vaginal vs. Cesarean Section" Delivery: Bivariate Empirical Mode Decomposition of Cardiotocographic Recordings. Front Physiol 2019; 10:246. [PMID: 30941054 PMCID: PMC6433745 DOI: 10.3389/fphys.2019.00246] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2018] [Accepted: 02/25/2019] [Indexed: 11/13/2022] Open
Abstract
We propose objective and robust measures for the purpose of classification of "vaginal vs. cesarean section" delivery by investigating temporal dynamics and complex interactions between fetal heart rate (FHR) and maternal uterine contraction (UC) recordings from cardiotocographic (CTG) traces. Multivariate extension of empirical mode decomposition (EMD) yields intrinsic scales embedded in UC-FHR recordings while also retaining inter-channel (UC-FHR) coupling at multiple scales. The mode alignment property of EMD results in the matched signal decomposition, in terms of frequency content, which paves the way for the selection of robust and objective time-frequency features for the problem at hand. Specifically, instantaneous amplitude and instantaneous frequency of multivariate intrinsic mode functions are utilized to construct a class of features which capture nonlinear and nonstationary interactions from UC-FHR recordings. The proposed features are fed to a variety of modern machine learning classifiers (decision tree, support vector machine, AdaBoost) to delineate vaginal and cesarean dynamics. We evaluate the performance of different classifiers on a real world dataset by investigating the following classifying measures: sensitivity, specificity, area under the ROC curve (AUC) and mean squared error (MSE). It is observed that under the application of all proposed 40 features AdaBoost classifier provides the best accuracy of 91.8% sensitivity, 95.5% specificity, 98% AUC, and 5% MSE. To conclude, the utilization of all proposed time-frequency features as input to machine learning classifiers can benefit clinical obstetric practitioners through a robust and automatic approach for the classification of fetus dynamics.
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Affiliation(s)
- Saqib Saleem
- Department of Electrical and Computer Engineering, COMSATS University Islamabad, Sahiwal, Pakistan
| | - Syed Saud Naqvi
- Department of Electrical and Computer Engineering, COMSATS University Islamabad, Islamabad, Pakistan
| | - Tareq Manzoor
- Energy Research Center, COMSATS University Islamabad, Islamabad, Pakistan
| | - Ahmed Saeed
- School of Computing, Ulster University, Newtownabbey, United Kingdom
| | - Naveed ur Rehman
- Department of Electrical and Computer Engineering, COMSATS University Islamabad, Islamabad, Pakistan
| | - Jawad Mirza
- Department of Electrical and Computer Engineering, COMSATS University Islamabad, Islamabad, Pakistan
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22
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Georgoulas G, Karvelis P, Gavrilis D, Stylios CD, Nikolakopoulos G. An ordinal classification approach for CTG categorization. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2018; 2017:2642-2645. [PMID: 29060442 DOI: 10.1109/embc.2017.8037400] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
Evaluation of cardiotocogram (CTG) is a standard approach employed during pregnancy and delivery. But, its interpretation requires high level expertise to decide whether the recording is Normal, Suspicious or Pathological. Therefore, a number of attempts have been carried out over the past three decades for development automated sophisticated systems. These systems are usually (multiclass) classification systems that assign a category to the respective CTG. However most of these systems usually do not take into consideration the natural ordering of the categories associated with CTG recordings. In this work, an algorithm that explicitly takes into consideration the ordering of CTG categories, based on binary decomposition method, is investigated. Achieved results, using as a base classifier the C4.5 decision tree classifier, prove that the ordinal classification approach is marginally better than the traditional multiclass classification approach, which utilizes the standard C4.5 algorithm for several performance criteria.
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23
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Marzbanrad F, Stroux L, Clifford GD. Cardiotocography and beyond: a review of one-dimensional Doppler ultrasound application in fetal monitoring. Physiol Meas 2018; 39:08TR01. [PMID: 30027897 PMCID: PMC6237616 DOI: 10.1088/1361-6579/aad4d1] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/03/2023]
Abstract
One-dimensional Doppler ultrasound (1D-DUS) provides a low-cost and simple method for acquiring a rich signal for use in cardiovascular screening. However, despite the use of 1D-DUS in cardiotocography (CTG) for decades, there are still challenges that limit the effectiveness of its users in reducing fetal and neonatal morbidities and mortalities. This is partly due to the noisy, transient, complex and nonstationary nature of the 1D-DUS signals. Current challenges also include lack of efficient signal quality metrics, insufficient signal processing techniques for extraction of fetal heart rate and other vital parameters with adequate temporal resolution, and lack of appropriate clinical decision support for CTG and Doppler interpretation. Moreover, the almost complete lack of open research in both hardware and software in this field, as well as commercial pressures to market the much more expensive and difficult to use Doppler imaging devices, has hampered innovation. This paper reviews the basics of fetal cardiac function, 1D-DUS signal generation and processing, its application in fetal monitoring and assessment of fetal development and wellbeing. It also provides recommendations for future development of signal processing and modeling approaches, to improve the application of 1D-DUS in fetal monitoring, as well as the need for annotated open databases.
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Affiliation(s)
- Faezeh Marzbanrad
- Department of Electrical and Computer Systems Engineering, Monash University, Clayton, VIC, Australia
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24
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Li G, Zhang S, Yang L, Li S, Wang Y, Zhao Y, Yang Y, Hao D, Li X, Zhang L, Xu M. Analysis of heart rate variation and foetal quiet sleep cycle correlation for normal and suspicious foetuses. BIOTECHNOL BIOTEC EQ 2018. [DOI: 10.1080/13102818.2018.1443401] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022] Open
Affiliation(s)
- Guangfei Li
- College of Life Science and Bio-engineering, Beijing University of Technology, Beijing, PR China
| | - Song Zhang
- College of Life Science and Bio-engineering, Beijing University of Technology, Beijing, PR China
| | - Lin Yang
- College of Life Science and Bio-engineering, Beijing University of Technology, Beijing, PR China
| | - Shufang Li
- Obstetrics and Gynecology, Peking University Third Hospital, Beijing, PR China
| | - Yan Wang
- Obstetrics and Gynecology, Peking University Third Hospital, Beijing, PR China
| | - Yangyu Zhao
- Obstetrics and Gynecology, Peking University Third Hospital, Beijing, PR China
| | - Yimin Yang
- College of Life Science and Bio-engineering, Beijing University of Technology, Beijing, PR China
| | - Dongmei Hao
- College of Life Science and Bio-engineering, Beijing University of Technology, Beijing, PR China
| | - Xuwen Li
- College of Life Science and Bio-engineering, Beijing University of Technology, Beijing, PR China
| | - Lei Zhang
- Beijing Yes Medical Devices Co., Ltd., Beijing, PR China
| | - Mingzhou Xu
- Beijing Aerospace Changfeng Co., Ltd., Beijing, PR China
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25
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Cömert Z, Kocamaz AF, Subha V. Prognostic model based on image-based time-frequency features and genetic algorithm for fetal hypoxia assessment. Comput Biol Med 2018; 99:85-97. [PMID: 29894897 DOI: 10.1016/j.compbiomed.2018.06.003] [Citation(s) in RCA: 31] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2018] [Revised: 05/20/2018] [Accepted: 06/03/2018] [Indexed: 11/25/2022]
Abstract
Cardiotocography (CTG) is applied routinely for fetal monitoring during the perinatal period to decrease the rates of neonatal mortality and morbidity as well as unnecessary interventions. The analysis of CTG traces has become an indispensable part of present clinical practices; however, it also has serious drawbacks, such as poor specificity and variability in its interpretation. The automated CTG analysis is seen as the most promising way to overcome these disadvantages. In this study, a novel prognostic model is proposed for predicting fetal hypoxia from CTG traces based on an innovative approach called image-based time-frequency (IBTF) analysis comprised of a combination of short time Fourier transform (STFT) and gray level co-occurrence matrix (GLCM). More specifically, from a graphical representation of the fetal heart rate (FHR) signal, the spectrogram is obtained by using STFT. The spectrogram images are converted into 8-bit grayscale images, and IBTF features such as contrast, correlation, energy, and homogeneity are utilized for identifying FHR signals. At the final stage of the analysis, different subsets of the feature space are applied as the input to the least square support vector machine (LS-SVM) classifier to determine the most informative subset. For this particular purpose, the genetic algorithm is employed. The prognostic model was performed on the open-access intrapartum CTU-UHB CTG database. The sensitivity and specificity obtained using only conventional features were 57.33% and 67.24%, respectively, whereas the most effective results were achieved using a combination of conventional and IBTF features, with a sensitivity of 63.45% and a specificity of 65.88%. Conclusively, this study provides a new promising approach for feature extraction of FHR signals. In addition, the experimental outcomes showed that IBTF features provided an increase in the classification accuracy.
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Affiliation(s)
- Zafer Cömert
- Bitlis Eren University, Department of Computer Engineering, Bitlis, Turkey.
| | | | - Velappan Subha
- Manonmaniam Sundaranar University, Department of Computer Science and Engineering, India.
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26
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Warmerdam GJJ, Vullings R, Van Laar JOEH, Van der Hout-Van der Jagt MB, Bergmans JWM, Schmitt L, Oei SG. Detection rate of fetal distress using contraction-dependent fetal heart rate variability analysis. Physiol Meas 2018; 39:025008. [PMID: 29350194 DOI: 10.1088/1361-6579/aaa925] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
OBJECTIVE Monitoring of the fetal condition during labor is currently performed by cardiotocograpy (CTG). Despite the use of CTG in clinical practice, CTG interpretation suffers from a high inter- and intra-observer variability and a low specificity. In addition to CTG, analysis of fetal heart rate variability (HRV) has been shown to provide information on fetal distress. However, fetal HRV can be strongly influenced by uterine contractions, particularly during the second stage of labor. Therefore, the aim of this study is to examine if distinguishing contractions from rest periods can improve the detection rate of HRV features for fetal distress during the second stage of labor. APPROACH We used a dataset of 100 recordings, containing 20 cases of fetuses with adverse outcome. The most informative HRV features were selected by a genetic algorithm and classification performance was evaluated using support vector machines. MAIN RESULTS Classification performance of fetal heart rate segments closest to birth improved from a geometric mean of 70% to 79%. If the classifier was used to indicate fetal distress over time, the geometric mean at 15 minutes before birth improved from 60% to 72%. SIGNIFICANCE Our results show that combining contraction-dependent HRV features with HRV features calculated over the entire fetal heart rate signal improves the detection rate of fetal distress.
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Affiliation(s)
- G J J Warmerdam
- Faculty of Electrical Engineering, Eindhoven University of Technology, Eindhoven, Netherlands
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27
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Fergus P, Selvaraj M, Chalmers C. Machine learning ensemble modelling to classify caesarean section and vaginal delivery types using Cardiotocography traces. Comput Biol Med 2017; 93:7-16. [PMID: 29248699 DOI: 10.1016/j.compbiomed.2017.12.002] [Citation(s) in RCA: 30] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2017] [Revised: 12/06/2017] [Accepted: 12/07/2017] [Indexed: 10/18/2022]
Abstract
Human visual inspection of Cardiotocography traces is used to monitor the foetus during labour and avoid neonatal mortality and morbidity. The problem, however, is that visual interpretation of Cardiotocography traces is subject to high inter and intra observer variability. Incorrect decisions, caused by miss-interpretation, can lead to adverse perinatal outcomes and in severe cases death. This study presents a review of human Cardiotocography trace interpretation and argues that machine learning, used as a decision support system by obstetricians and midwives, may provide an objective measure alongside normal practices. This will help to increase predictive capacity and reduce negative outcomes. A robust methodology is presented for feature set engineering using an open database comprising 552 intrapartum recordings. State-of-the-art in signal processing techniques is applied to raw Cardiotocography foetal heart rate traces to extract 13 features. Those with low discriminative capacity are removed using Recursive Feature Elimination. The dataset is imbalanced with significant differences between the prior probabilities of both normal deliveries and those delivered by caesarean section. This issue is addressed by oversampling the training instances using a synthetic minority oversampling technique to provide a balanced class distribution. Several simple, yet powerful, machine-learning algorithms are trained, using the feature set, and their performance is evaluated with real test data. The results are encouraging using an ensemble classifier comprising Fishers Linear Discriminant Analysis, Random Forest and Support Vector Machine classifiers, with 87% (95% Confidence Interval: 86%, 88%) for Sensitivity, 90% (95% CI: 89%, 91%) for Specificity, and 96% (95% CI: 96%, 97%) for the Area Under the Curve, with a 9% (95% CI: 9%, 10%) Mean Square Error.
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Affiliation(s)
- Paul Fergus
- Liverpool John Moores University, Faculty of Engineering and Technology, Data Science Research Centre, Department of Computer Science, Byron Street, Liverpool, L3 3AF, United Kingdom.
| | - Malarvizhi Selvaraj
- Liverpool John Moores University, Faculty of Engineering and Technology, Data Science Research Centre, Department of Computer Science, Byron Street, Liverpool, L3 3AF, United Kingdom.
| | - Carl Chalmers
- Liverpool John Moores University, Faculty of Engineering and Technology, Data Science Research Centre, Department of Computer Science, Byron Street, Liverpool, L3 3AF, United Kingdom.
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28
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Kadi I, Idri A, Fernandez-Aleman JL. Systematic mapping study of data mining–based empirical studies in cardiology. Health Informatics J 2017; 25:741-770. [DOI: 10.1177/1460458217717636] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Data mining provides the methodology and technology to transform huge amount of data into useful information for decision making. It is a powerful process to extract knowledge and discover new patterns embedded in large data sets. Data mining has been increasingly used in medicine, particularly in cardiology. In fact, data mining applications can greatly benefits all parts involved in cardiology such as patients, cardiologists and nurses. This article aims to perform a systematic mapping study so as to analyze and synthesize empirical studies on the application of data mining techniques in cardiology. A total of 142 articles published between 2000 and 2015 were therefore selected, studied and analyzed according to the four following criteria: year and channel of publication, research type, medical task and empirical type. The results of this mapping study are discussed and a list of recommendations for researchers and cardiologists is provided.
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Affiliation(s)
| | - Ali Idri
- Mohammed V University in Rabat, Morocco
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Georgoulas G, Karvelis P, Spilka J, Chudáček V, Stylios CD, Lhotská L. Investigating pH based evaluation of fetal heart rate (FHR) recordings. HEALTH AND TECHNOLOGY 2017; 7:241-254. [PMID: 29201590 PMCID: PMC5686283 DOI: 10.1007/s12553-017-0201-7] [Citation(s) in RCA: 25] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2016] [Accepted: 05/30/2017] [Indexed: 11/30/2022]
Abstract
Cardiotocography (CTG) is a standard tool for the assessment of fetal well-being during pregnancy and delivery. However, its interpretation is associated with high inter- and intra-observer variability. Since its introduction there have been numerous attempts to develop computerized systems assisting the evaluation of the CTG recording. Nevertheless these systems are still hardly used in a delivery ward. Two main approaches to computerized evaluation are encountered in the literature; the first one emulates existing guidelines, while the second one is more of a data-driven approach using signal processing and computational methods. The latter employs preprocessing, feature extraction/selection and a classifier that discriminates between two or more classes/conditions. These classes are often formed using the umbilical cord artery pH value measured after delivery. In this work an approach to Fetal Heart Rate (FHR) classification using pH is presented that could serve as a benchmark for reporting results on the unique open-access CTU-UHB CTG database, the largest and the only freely available database of this kind. The overall results using a very small number of features and a Least Squares Support Vector Machine (LS-SVM) classifier, are in accordance to the ones encountered in the literature and outperform the results of a baseline classification scheme proving the utility of using advanced data processing methods. Therefore the achieved results can be used as a benchmark for future research involving more informative features and/or better classification algorithms.
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Affiliation(s)
- George Georgoulas
- Control Engineering Group Department of Computer Science, Electrical and Space Engineering Luleå University of Technology, SE-97187 Luleå, Sweden
| | - Petros Karvelis
- Laboratory of Knowledge and Intelligent Computing, Department of Computer Engineering, Technological Educational Institute of Epirus, Arta, Kostakioi Greece
| | - Jiří Spilka
- CIIRC, Czech Technical, University in Prague, Czech Republic, Prague, Czech Republic
| | - Václav Chudáček
- CIIRC, Czech Technical, University in Prague, Czech Republic, Prague, Czech Republic
| | - Chrysostomos D Stylios
- Laboratory of Knowledge and Intelligent Computing, Department of Computer Engineering, Technological Educational Institute of Epirus, Arta, Kostakioi Greece
| | - Lenka Lhotská
- CIIRC, Czech Technical, University in Prague, Czech Republic, Prague, Czech Republic
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30
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Kim SH, Yang HJ, Lee SW. FitMine: automatic mining for time-evolving signals of cardiotocography monitoring. Data Min Knowl Discov 2017. [DOI: 10.1007/s10618-017-0493-2] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
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31
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Kadi I, Idri A, Fernandez-Aleman J. Knowledge discovery in cardiology: A systematic literature review. Int J Med Inform 2017; 97:12-32. [DOI: 10.1016/j.ijmedinf.2016.09.005] [Citation(s) in RCA: 49] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2016] [Revised: 09/01/2016] [Accepted: 09/11/2016] [Indexed: 11/24/2022]
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32
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Yılmaz E. Fetal State Assessment from Cardiotocogram Data Using Artificial Neural Networks. J Med Biol Eng 2016. [DOI: 10.1007/s40846-016-0191-3] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
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33
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Warmerdam GJJ, Vullings R, Van Laar JOEH, Van der Hout-Van der Jagt MB, Bergmans JWM, Schmitt L, Oei SG. Using uterine activity to improve fetal heart rate variability analysis for detection of asphyxia during labor. Physiol Meas 2016; 37:387-400. [PMID: 26862891 DOI: 10.1088/0967-3334/37/3/387] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
During labor, uterine contractions can cause temporary oxygen deficiency for the fetus. In case of severe and prolonged oxygen deficiency this can lead to asphyxia. The currently used technique for detection of asphyxia, cardiotocography (CTG), suffers from a low specificity. Recent studies suggest that analysis of fetal heart rate variability (HRV) in addition to CTG can provide information on fetal distress. However, interpretation of fetal HRV during labor is difficult due to the influence of uterine contractions on fetal HRV. The aim of this study is therefore to investigate whether HRV features differ during contraction and rest periods, and whether these differences can improve the detection of asphyxia. To this end, a case-control study was performed, using 14 cases with asphyxia that were matched with 14 healthy fetuses. We did not find significant differences for individual HRV features when calculated over the fetal heart rate without separating contractions and rest periods (p > 0.30 for all HRV features). Separating contractions from rest periods did result in a significant difference. In particular the ratio between HRV features calculated during and outside contractions can improve discrimination between fetuses with and without asphyxia (p < 0.04 for three out of four ratio HRV features that were studied in this paper).
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Affiliation(s)
- G J J Warmerdam
- Faculty of Electrical Engineering, Eindhoven University of Technology, 5612 AZ Eindhoven, The Netherlands
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34
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Spilka J, Frecon J, Leonarduzzi R, Pustelnik N, Abry P, Doret M. Intrapartum fetal heart rate classification from trajectory in Sparse SVM feature space. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2016; 2015:2335-8. [PMID: 26736761 DOI: 10.1109/embc.2015.7318861] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
Intrapartum fetal heart rate (FHR) constitutes a prominent source of information for the assessment of fetal reactions to stress events during delivery. Yet, early detection of fetal acidosis remains a challenging signal processing task. The originality of the present contribution are three-fold: multiscale representations and wavelet leader based multifractal analysis are used to quantify FHR variability ; Supervised classification is achieved by means of Sparse-SVM that aim jointly to achieve optimal detection performance and to select relevant features in a multivariate setting ; Trajectories in the feature space accounting for the evolution along time of features while labor progresses are involved in the construction of indices quantifying fetal health. The classification performance permitted by this combination of tools are quantified on a intrapartum FHR large database (≃ 1250 subjects) collected at a French academic public hospital.
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35
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Illanes A, Haritopoulos M. Fetal heart rate feature extraction from cardiotocographic recordings through autoregressive model's power spectral- and pole-based analysis. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2016; 2015:5842-5. [PMID: 26737620 DOI: 10.1109/embc.2015.7319720] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
The main objective of this work is to perform an autoregressive model (AR)-based power spectral analysis of the fetal heart rate (FHR) signal for the extraction of significant features for fetal welfare assessment. A group of features is directly computed from the AR-based spectrum while another group is computed from the poles representation. The presented method is applied to real cardiotocographic (CTG) signals and for different frequency bands, and the obtained results are very promising as they exhibit direct correlations between the extracted features and the fetal welfare in terms of umbilical pH.
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36
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Georgoulas G, Spilka J, Karvelis P, Chudáček V, Stylios C, Lhotská L. A three class treatment of the FHR classification problem using latent class analysis labeling. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2015; 2014:46-9. [PMID: 25569893 DOI: 10.1109/embc.2014.6943525] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
Electronic Fetal Monitoring in the form of cardiotocography is routinely used for fetal assessment both during pregnancy and delivery. However its interpretation requires a high level of expertise and even then the assessment is somewhat subjective as it has been proven by the high inter and intra-observer variability. Therefore the scientific community seeks for more objective methods for its interpretation. Along this path, presented work proposes a classification approach, which is based on a latent class analysis method that attempts to produce more objective labeling of the training cases, a step which is vital in a classification problem. The method is combined with a simple logistic regression approach under two different schemes: a standard multi-class classification formulation and an ordinal classification one. The results are promising suggesting that more effort should be put in this proposed approach.
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37
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Gavrilis D, Nikolakopoulos G, Georgoulas G. A one-class approach to cardiotocogram assessment. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2015; 2015:518-521. [PMID: 26736313 DOI: 10.1109/embc.2015.7318413] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/05/2023]
Abstract
Cardiotocogram (CTG) is the most widely used means for the assessment of fetal condition. CTG consists of two traces one depicting the Fetal Heart Rate (FHR), and the other the Uterine Contractions (UC) activity. Many automatic methods have been proposed for the interpretation of the CTG. Most of them rely either on a binary classification approach or on a multiclass approach to come up with a decision about the class that the tracing belongs to. This work investigates the use of a one-class approach to the assessment of CTGs building a model only for the healthy data. The preliminary results are promising indicating that normal traces could be used as part of an automatic system that can detect deviations from normality.
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38
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Classification of the cardiotocogram data for anticipation of fetal risks using machine learning techniques. Appl Soft Comput 2015. [DOI: 10.1016/j.asoc.2015.04.038] [Citation(s) in RCA: 66] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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39
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Karvelis P, Spilka J, Georgoulas G, Chudáček V, Stylios CD, Lhotská L. Combining latent class analysis labeling with multiclass approach for fetal heart rate categorization. Physiol Meas 2015; 36:1001-24. [PMID: 25894994 DOI: 10.1088/0967-3334/36/5/1001] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
The most common approach to assess fetal well-being during delivery is monitoring of fetal heart rate and uterine contractions-the cardiotocogram (CTG). Nevertheless, 40 years since the introduction of CTG to clinical practice, its evaluation is still challenging with high inter- and intra-observer variability. Therefore the development of more objective methods has become an issue of major importance in the field. Unlike the usually proposed approaches to assign classes for classification methods that rely either on biochemical parameters (e.g. pH value) or a simple aggregation of expert judgment, this work investigates the use of an alternative labeling system using latent class analysis (LCA) along with an ordinal classification scheme. The study is performed on a well-documented open-access database, where nine expert obstetricians provided CTG annotations. The LCA is proposed here to produce more objective class labels while the ordinal classification aims to explore the natural ordering, and representation of increased severity, for obtaining the final results. The results are promising suggesting that more effort should be put into this proposed approach.
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Affiliation(s)
- P Karvelis
- Laboratory of Knowledge and Intelligent Computing, Department of Computer Engineering, Technological Educational Institute of Epirus, Arta, Greece
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40
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A novel clinical decision support system using improved adaptive genetic algorithm for the assessment of fetal well-being. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2015; 2015:283532. [PMID: 25793009 PMCID: PMC4352501 DOI: 10.1155/2015/283532] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/12/2014] [Revised: 11/16/2014] [Accepted: 11/16/2014] [Indexed: 11/18/2022]
Abstract
A novel clinical decision support system is proposed in this paper for evaluating the fetal well-being from the cardiotocogram (CTG) dataset through an Improved Adaptive Genetic Algorithm (IAGA) and Extreme Learning Machine (ELM). IAGA employs a new scaling technique (called sigma scaling) to avoid premature convergence and applies adaptive crossover and mutation techniques with masking concepts to enhance population diversity. Also, this search algorithm utilizes three different fitness functions (two single objective fitness functions and multi-objective fitness function) to assess its performance. The classification results unfold that promising classification accuracy of 94% is obtained with an optimal feature subset using IAGA. Also, the classification results are compared with those of other Feature Reduction techniques to substantiate its exhaustive search towards the global optimum. Besides, five other benchmark datasets are used to gauge the strength of the proposed IAGA algorithm.
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41
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Chudacek V, Talmon R, Anden J, Mallat S, Coifman RR, Abry P, Doret M. Low dimensional manifold embedding for scattering coefficients of intrapartum fetale heart rate variability. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2015; 2014:6373-6. [PMID: 25571454 DOI: 10.1109/embc.2014.6945086] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
Intrapartum fetal surveillance for early detection of fetal acidosis in clinical practice focuses on reducing neonatal morbidity via early detection. It is the subject of on going research studies attempting notably to improve detection performance by reducing false positive rate. In that context, the present contribution tailors to fetal heart rate variability analysis a graph-based dimensionality reduction procedure performed on scattering coefficients. Applied to a high quality and well-documented database constituted by obstetricians from a French academic hospital, the low dimensional embedding enables to distinguish between the temporal dynamics of healthy and acidotic fetuses, as well as to achieve satisfactory detection performance detection compared to those obtained by the clinical-benchmark FIGO criteria.
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42
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Dong S, Azemi G, Boashash B. Improved characterization of HRV signals based on instantaneous frequency features estimated from quadratic time–frequency distributions with data-adapted kernels. Biomed Signal Process Control 2014. [DOI: 10.1016/j.bspc.2013.11.008] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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43
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Hannah Inbarani H, Nizar Banu PK, Azar AT. Feature selection using swarm-based relative reduct technique for fetal heart rate. Neural Comput Appl 2014. [DOI: 10.1007/s00521-014-1552-x] [Citation(s) in RCA: 36] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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44
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Chudáček V, Spilka J, Burša M, Janků P, Hruban L, Huptych M, Lhotská L. Open access intrapartum CTG database. BMC Pregnancy Childbirth 2014; 14:16. [PMID: 24418387 PMCID: PMC3898997 DOI: 10.1186/1471-2393-14-16] [Citation(s) in RCA: 102] [Impact Index Per Article: 10.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2013] [Accepted: 12/06/2013] [Indexed: 11/10/2022] Open
Abstract
Background Cardiotocography (CTG) is a monitoring of fetal heart rate and uterine contractions. Since 1960 it is routinely used by obstetricians to assess fetal well-being. Many attempts to introduce methods of automatic signal processing and evaluation have appeared during the last 20 years, however still no significant progress similar to that in the domain of adult heart rate variability, where open access databases are available (e.g. MIT-BIH), is visible. Based on a thorough review of the relevant publications, presented in this paper, the shortcomings of the current state are obvious. A lack of common ground for clinicians and technicians in the field hinders clinically usable progress. Our open access database of digital intrapartum cardiotocographic recordings aims to change that. Description The intrapartum CTG database consists in total of 552 intrapartum recordings, which were acquired between April 2010 and August 2012 at the obstetrics ward of the University Hospital in Brno, Czech Republic. All recordings were stored in electronic form in the OB TraceVue®;system. The recordings were selected from 9164 intrapartum recordings with clinical as well as technical considerations in mind. All recordings are at most 90 minutes long and start a maximum of 90 minutes before delivery. The time relation of CTG to delivery is known as well as the length of the second stage of labor which does not exceed 30 minutes. The majority of recordings (all but 46 cesarean sections) is – on purpose – from vaginal deliveries. All recordings have available biochemical markers as well as some more general clinical features. Full description of the database and reasoning behind selection of the parameters is presented in the paper. Conclusion A new open-access CTG database is introduced which should give the research community common ground for comparison of results on reasonably large database. We anticipate that after reading the paper, the reader will understand the context of the field from clinical and technical perspectives which will enable him/her to use the database and also understand its limitations.
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Affiliation(s)
- Václav Chudáček
- Department of Cybernetics, Faculty of Electrical Engineering, Czech Technical University in Prague, Prague, Czech Republic.
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45
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Discriminating Normal from “Abnormal” Pregnancy Cases Using an Automated FHR Evaluation Method. ACTA ACUST UNITED AC 2014. [DOI: 10.1007/978-3-319-07064-3_45] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/09/2023]
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46
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Dong S, Boashash B, Azemi G, Lingwood BE, Colditz PB. Automated detection of perinatal hypoxia using time-frequency-based heart rate variability features. Med Biol Eng Comput 2013; 52:183-91. [PMID: 24272142 DOI: 10.1007/s11517-013-1129-3] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2013] [Accepted: 11/08/2013] [Indexed: 11/29/2022]
Abstract
Perinatal hypoxia is a cause of cerebral injury in foetuses and neonates. Detection of foetal hypoxia during labour based on the pattern recognition of heart rate signals suffers from high observer variability and low specificity. We describe a new automated hypoxia detection method using time-frequency analysis of heart rate variability (HRV) signals. This approach uses features extracted from the instantaneous frequency and instantaneous amplitude of HRV signal components as well as features based on matrix decomposition of the signals' time-frequency distributions using singular value decomposition and non-negative matrix factorization. The classification between hypoxia and non-hypoxia data is performed using a support vector machine classifier. The proposed method is tested on a dataset obtained from a newborn piglet model with a controlled hypoxic insult. The chosen HRV features show strong performance compared to conventional spectral features and other existing methods of hypoxia detection with a sensitivity 93.3 %, specificity 98.3 % and accuracy 95.8 %. The high predictive value of this approach to detecting hypoxia is a substantial step towards developing a more accurate and reliable hypoxia detection method for use in human foetal monitoring.
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Affiliation(s)
- Shiying Dong
- UQ Centre for Clinical Research, The University of Queensland, Herston, QLD, Australia,
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47
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Determination of fetal state from cardiotocogram using LS-SVM with particle swarm optimization and binary decision tree. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2013; 2013:487179. [PMID: 24288574 PMCID: PMC3830816 DOI: 10.1155/2013/487179] [Citation(s) in RCA: 35] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/26/2013] [Accepted: 09/06/2013] [Indexed: 11/18/2022]
Abstract
We use least squares support vector machine (LS-SVM) utilizing a binary decision tree for classification of cardiotocogram to determine the fetal state. The parameters of LS-SVM are optimized by particle swarm optimization. The robustness of the method is examined by running 10-fold cross-validation. The performance of the method is evaluated in terms of overall classification accuracy. Additionally, receiver operation characteristic analysis and cobweb representation are presented in order to analyze and visualize the performance of the method. Experimental results demonstrate that the proposed method achieves a remarkable classification accuracy rate of 91.62%.
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48
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Garde A, Voss A, Caminal P, Benito S, Giraldo BF. SVM-based feature selection to optimize sensitivity–specificity balance applied to weaning. Comput Biol Med 2013; 43:533-40. [DOI: 10.1016/j.compbiomed.2013.01.014] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2011] [Revised: 01/20/2013] [Accepted: 01/21/2013] [Indexed: 11/17/2022]
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49
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Comparison of real beat-to-beat signals with commercially available 4 Hz sampling on the evaluation of foetal heart rate variability. Med Biol Eng Comput 2013; 51:665-76. [DOI: 10.1007/s11517-013-1036-7] [Citation(s) in RCA: 37] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2012] [Accepted: 01/10/2013] [Indexed: 10/27/2022]
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50
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Ocak H. A Medical Decision Support System Based on Support Vector Machines and the Genetic Algorithm for the Evaluation of Fetal Well-Being. J Med Syst 2013; 37:9913. [DOI: 10.1007/s10916-012-9913-4] [Citation(s) in RCA: 78] [Impact Index Per Article: 7.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2012] [Accepted: 12/29/2012] [Indexed: 11/29/2022]
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