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Kapila R, Saleti S. Optimizing fetal health prediction: Ensemble modeling with fusion of feature selection and extraction techniques for cardiotocography data. Comput Biol Chem 2023; 107:107973. [PMID: 37926049 DOI: 10.1016/j.compbiolchem.2023.107973] [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: 06/25/2023] [Revised: 09/12/2023] [Accepted: 10/19/2023] [Indexed: 11/07/2023]
Abstract
Cardiotocography (CTG) captured the fetal heart rate and the timing of uterine contractions. Throughout pregnancy, CTG intelligent categorization is crucial for monitoring fetal health and preserving proper fetal growth and development. Since CTG provides information on the fetal heartbeat and uterus contractions, which helps determine if the fetus is pathologic or not, obstetricians frequently use it to evaluate a child's physical health during pregnancy. In the past, obstetricians have artificially analyzed CTG data, which is time-consuming and inaccurate. So, developing a fetal health categorization model is crucial as it may help to speed up the diagnosis and treatment and conserve medical resources. The CTG dataset is used in this study. To diagnose the illness, 7 machine learning models are employed, as well as ensemble strategies including voting and stacking classifiers. In order to choose and extract the most significant and critical attributes from the dataset, Feature Selection (FS) techniques like ANOVA and Chi-square, as well as Feature Extraction (FE) strategies like Principal Component Analysis (PCA) and Independent Component Analysis (ICA), are being used. We used the Synthetic Minority Oversampling Technique (SMOTE) approach to balance the dataset because it is unbalanced. In order to forecast the illness, the top 5 models are selected, and these 5 models are used in ensemble methods such as voting and stacking classifiers. The utilization of Stacking Classifiers (SC), which involve Adaboost and Random Forest (RF) as meta-classifiers for disease detection. The performance of the proposed SC with meta-classifier as RF model, which incorporates Chi-square with PCA, outperformed all other state-of-the-art models, achieving scores of 98.79%,98.88%,98.69%,96.32%, and 98.77% for accuracy, precision, recall, specificity, and f1-score respectively.
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Affiliation(s)
- Ramdas Kapila
- Data Science Laboratory, Computer Science and Engineering, SRM University - AP, India.
| | - Sumalatha Saleti
- Data Science Laboratory, Computer Science and Engineering, SRM University - AP, India.
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2
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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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Kuzu A, Santur Y. Early Diagnosis and Classification of Fetal Health Status from a Fetal Cardiotocography Dataset Using Ensemble Learning. Diagnostics (Basel) 2023; 13:2471. [PMID: 37568833 PMCID: PMC10417593 DOI: 10.3390/diagnostics13152471] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2023] [Revised: 07/21/2023] [Accepted: 07/22/2023] [Indexed: 08/13/2023] Open
Abstract
(1) Background: According to the World Health Organization (WHO), 6.3 million intrauterine fetal deaths occur every year. The most common method of diagnosing perinatal death and taking early precautions for maternal and fetal health is a nonstress test (NST). Data on the fetal heart rate and uterus contractions from an NST device are interpreted based on a trace printer's output, allowing for a diagnosis of fetal health to be made by an expert. (2) Methods: in this study, a predictive method based on ensemble learning is proposed for the classification of fetal health (normal, suspicious, pathology) using a cardiotocography dataset of fetal movements and fetal heart rate acceleration from NST tests. (3) Results: the proposed predictor achieved an accuracy level above 99.5% on the test dataset. (4) Conclusions: from the experimental results, it was observed that a fetal health diagnosis can be made during NST using machine learning.
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Affiliation(s)
- Adem Kuzu
- Department of Software Engineering, Firat University, Elazig 23119, Turkey;
| | - Yunus Santur
- Department of Artificial Intelligence and Data Engineering, Firat University, Elazig 23119, Turkey
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Abiyev R, Idoko JB, Altıparmak H, Tüzünkan M. Fetal Health State Detection Using Interval Type-2 Fuzzy Neural Networks. Diagnostics (Basel) 2023; 13:diagnostics13101690. [PMID: 37238176 DOI: 10.3390/diagnostics13101690] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2023] [Accepted: 05/08/2023] [Indexed: 05/28/2023] Open
Abstract
Diagnosis of fetal health is a difficult process that depends on various input factors. Depending on the values or the interval of values of these input symptoms, the detection of fetal health status is implemented. Sometimes it is difficult to determine the exact values of the intervals for diagnosing the diseases and there may always be disagreement between the expert doctors. As a result, the diagnosis of diseases is often carried out in uncertain conditions and can sometimes cause undesirable errors. Therefore, the vague nature of diseases and incomplete patient data can lead to uncertain decisions. One of the effective approaches to solve such kind of problem is the use of fuzzy logic in the construction of the diagnostic system. This paper proposes a type-2 fuzzy neural system (T2-FNN) for the detection of fetal health status. The structure and design algorithms of the T2-FNN system are presented. Cardiotocography, which provides information about the fetal heart rate and uterine contractions, is employed for monitoring fetal status. Using measured statistical data, the design of the system is implemented. Comparisons of various models are presented to prove the effectiveness of the proposed system. The system can be utilized in clinical information systems to obtain valuable information about fetal health status.
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Affiliation(s)
- Rahib Abiyev
- Applied Artificial Intelligence Research Centre, Department of Computer Engineering, Near East University, Nicosia 99138, Turkey
| | - John Bush Idoko
- Department of Computer Engineering, Near East University, Nicosia 99138, Turkey
| | - Hamit Altıparmak
- Department of Computer Engineering, Near East University, Nicosia 99138, Turkey
| | - Murat Tüzünkan
- Applied Artificial Intelligence Research Centre, Near East University, Nicosia 99138, Turkey
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Kahankova R, Barnova K, Jaros R, Pavlicek J, Snasel V, Martinek R. Pregnancy in the time of COVID-19: towards Fetal monitoring 4.0. BMC Pregnancy Childbirth 2023; 23:33. [PMID: 36647041 PMCID: PMC9841500 DOI: 10.1186/s12884-023-05349-3] [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: 11/22/2022] [Accepted: 01/05/2023] [Indexed: 01/18/2023] Open
Abstract
On the outbreak of the global COVID-19 pandemic, high-risk and vulnerable groups in the population were at particular risk of severe disease progression. Pregnant women were one of these groups. The infectious disease endangered not only the physical health of pregnant women, but also their mental well-being. Improving the mental health of pregnant women and reducing their risk of an infectious disease could be achieved by using remote home monitoring solutions. These would allow the health of the mother and fetus to be monitored from the comfort of their home, a reduction in the number of physical visits to the doctor and thereby eliminate the need for the mother to venture into high-risk public places. The most commonly used technique in clinical practice, cardiotocography, suffers from low specificity and requires skilled personnel for the examination. For that and due to the intermittent and active nature of its measurements, it is inappropriate for continuous home monitoring. The pandemic has demonstrated that the future lies in accurate remote monitoring and it is therefore vital to search for an option for fetal monitoring based on state-of-the-art technology that would provide a safe, accurate, and reliable information regarding fetal and maternal health state. In this paper, we thus provide a technical and critical review of the latest literature and on this topic to provide the readers the insights to the applications and future directions in fetal monitoring. We extensively discuss the remaining challenges and obstacles in future research and in developing the fetal monitoring in the new era of Fetal monitoring 4.0, based on the pillars of Healthcare 4.0.
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Affiliation(s)
- Radana Kahankova
- grid.440850.d0000 0000 9643 2828Department of Cybernetics and Biomedical Engineering, Faculty of Electrical Engineering and Computer Science, VSB–Technical University of Ostrava, Ostrava, Czechia
| | - Katerina Barnova
- grid.440850.d0000 0000 9643 2828Department of Cybernetics and Biomedical Engineering, Faculty of Electrical Engineering and Computer Science, VSB–Technical University of Ostrava, Ostrava, Czechia
| | - Rene Jaros
- grid.440850.d0000 0000 9643 2828Department of Cybernetics and Biomedical Engineering, Faculty of Electrical Engineering and Computer Science, VSB–Technical University of Ostrava, Ostrava, Czechia
| | - Jan Pavlicek
- grid.412684.d0000 0001 2155 4545Department of Pediatrics, Faculty Hospital, Faculty of Medicine, Ostrava University, Ostrava, Czechia
| | - Vaclav Snasel
- grid.440850.d0000 0000 9643 2828Department of Computer Science, Faculty of Electrical Engineering and Computer Science, VSB–Technical University of Ostrava, Ostrava, Czechia
| | - Radek Martinek
- grid.440850.d0000 0000 9643 2828Department of Cybernetics and Biomedical Engineering, Faculty of Electrical Engineering and Computer Science, VSB–Technical University of Ostrava, Ostrava, Czechia
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Mostafa FA, Elrefaei LA, Fouda MM, Hossam A. A Survey on AI Techniques for Thoracic Diseases Diagnosis Using Medical Images. Diagnostics (Basel) 2022; 12:diagnostics12123034. [PMID: 36553041 PMCID: PMC9777249 DOI: 10.3390/diagnostics12123034] [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: 10/10/2022] [Revised: 11/20/2022] [Accepted: 11/22/2022] [Indexed: 12/12/2022] Open
Abstract
Thoracic diseases refer to disorders that affect the lungs, heart, and other parts of the rib cage, such as pneumonia, novel coronavirus disease (COVID-19), tuberculosis, cardiomegaly, and fracture. Millions of people die every year from thoracic diseases. Therefore, early detection of these diseases is essential and can save many lives. Earlier, only highly experienced radiologists examined thoracic diseases, but recent developments in image processing and deep learning techniques are opening the door for the automated detection of these diseases. In this paper, we present a comprehensive review including: types of thoracic diseases; examination types of thoracic images; image pre-processing; models of deep learning applied to the detection of thoracic diseases (e.g., pneumonia, COVID-19, edema, fibrosis, tuberculosis, chronic obstructive pulmonary disease (COPD), and lung cancer); transfer learning background knowledge; ensemble learning; and future initiatives for improving the efficacy of deep learning models in applications that detect thoracic diseases. Through this survey paper, researchers may be able to gain an overall and systematic knowledge of deep learning applications in medical thoracic images. The review investigates a performance comparison of various models and a comparison of various datasets.
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Affiliation(s)
- Fatma A. Mostafa
- Department of Electrical Engineering, Faculty of Engineering at Shoubra, Benha University, Cairo 11672, Egypt
| | - Lamiaa A. Elrefaei
- Department of Electrical Engineering, Faculty of Engineering at Shoubra, Benha University, Cairo 11672, Egypt
| | - Mostafa M. Fouda
- Department of Electrical and Computer Engineering, College of Science and Engineering, Idaho State University, Pocatello, ID 83209, USA
- Correspondence:
| | - Aya Hossam
- Department of Electrical Engineering, Faculty of Engineering at Shoubra, Benha University, Cairo 11672, Egypt
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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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Verma D, Agrawal S, Iwendi C, Sharma B, Bhatia S, Basheer S. A Novel Framework for Abnormal Risk Classification over Fetal Nuchal Translucency Using Adaptive Stochastic Gradient Descent Algorithm. Diagnostics (Basel) 2022; 12:2643. [PMID: 36359487 PMCID: PMC9689292 DOI: 10.3390/diagnostics12112643] [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: 07/20/2022] [Revised: 09/14/2022] [Accepted: 09/22/2022] [Indexed: 11/25/2023] Open
Abstract
In most maternity hospitals, an ultrasound scan in the mid-trimester is now a standard element of antenatal care. More fetal abnormalities are being detected in scans as technology advances and ability improves. Fetal anomalies are developmental abnormalities in a fetus that arise during pregnancy, birth defects and congenital abnormalities are related terms. Fetal abnormalities have been commonly observed in industrialized countries over the previous few decades. Three out of every 1000 pregnant mothers suffer a fetal anomaly. This research work proposes an Adaptive Stochastic Gradient Descent Algorithm to evaluate the risk of fetal abnormality. Findings of this work suggest that proposed innovative method can successfully classify the anomalies linked with nuchal translucency thickening. Parameters such an accuracy, recall, precision, and F1-score are analyzed. The accuracy achieved through the suggested technique is 98.642.%.
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Affiliation(s)
- Deepti Verma
- Department of Computer Application, SAGE University, Indore 452020, India
| | - Shweta Agrawal
- Institute of Advance Computing, SAGE University, Indore 452020, India
| | - Celestine Iwendi
- School of Creative Technologies, University of Bolton, Bolton BL3 5AB, UK
| | - Bhisham Sharma
- Department of Computer Science & Engineering, School of Engineering and Technology, Chitkara University, Baddi 174103, India
| | - Surbhi Bhatia
- Department of Information Systems, College of Computer Science and Information Technology, King Faisal University, Al Ahsa 36362, Saudi Arabia
| | - Shakila Basheer
- Department of Information Systems, College of Computer and Information Science, Princess Nourah Bint Abdulrahman University, P.O. BOX 84428, Riyadh 11671, Saudi Arabia
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Intelligent classification of antenatal cardiotocography signals via multimodal bidirectional gated recurrent units. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2022.104008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
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10
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Chen M, Yin Z. Classification of Cardiotocography Based on the Apriori Algorithm and Multi-Model Ensemble Classifier. Front Cell Dev Biol 2022; 10:888859. [PMID: 35646917 PMCID: PMC9130474 DOI: 10.3389/fcell.2022.888859] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2022] [Accepted: 03/31/2022] [Indexed: 12/04/2022] Open
Abstract
Cardiotocography (CTG) recorded fetal heart rate and its temporal relationship with uterine contractions. CTG intelligent classification plays an important role in evaluating fetal health and protecting fetal normal growth and development throughout pregnancy. At the feature selection level, this study uses the Apriori algorithm to search frequent item sets for feature extraction. At the level of the classification model, the combination model of AdaBoost and random forest with the highest classification accuracy is finally selected by comparing various models. The suspicious class data in the CTG data set affect the overall classification accuracy. The number of suspicious class data is predicted by the multi-model ensemble method. Finally, the data set is fused from three classifications to two classifications. The classification accuracy is 0.976, and the AUC is 0.98, which significantly improves the classification effect. In conclusion, the method used in this study has high accuracy in model classification, which is helpful to improve the accuracy of fetal abnormality detection.
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A novel multi-objective medical feature selection compass method for binary classification. Artif Intell Med 2022; 127:102277. [DOI: 10.1016/j.artmed.2022.102277] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2021] [Revised: 01/15/2022] [Accepted: 03/06/2022] [Indexed: 11/19/2022]
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Silva Neto MGD, Vale Madeiro JPD, Gomes DG. On designing a biosignal-based fetal state assessment system: A systematic mapping study. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2022; 216:106671. [PMID: 35144149 DOI: 10.1016/j.cmpb.2022.106671] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/11/2021] [Revised: 01/05/2022] [Accepted: 01/28/2022] [Indexed: 06/14/2023]
Abstract
BACKGROUND AND OBJECTIVE The patterns present in biosignals, such as fetal heart rate (FHR), are valuable indicators of fetal well-being. In designing biosignal analysis systems, the variety of approaches and technology usage impairs the decision-making for the fundamental units of the systems. There is a need for an updated overview of studies encompassing the biosignal-based fetal state assessment systems. Therefore, we propose a systematic mapping study to identify and synthesize recent research regarding the building blocks that compose these systems. METHODS We followed well-established guidelines to perform a systematic mapping of studies regarding the building-blocks that compose the fetal state assessment systems and published between January 2016 and January 2021. A search string was determined based on the mapping questions and the PI (population and intervention) divisions. The search string was applied in digital libraries covering the fields of computer science, engineering, and medical informatics. Then, we applied the forward snowballing technique to complement the resulting set. This process resulted in 75 primary studies selected from a total of 871 papers. RESULTS Selected studies were classified according to the publication types, systems design stages, datasets, and predictive capabilities. The results revealed that (i) The majority of the selected studies refer to the method as a type of publication and there is a lack of validation studies; (ii) The CTU-UHB was the most frequent biosignal-based dataset and UCI-CTG was the most frequent feature-based data; (iii) The selected studies made use of the system design stages alone or in a mixed-mode. CONCLUSION The results indicated that the well-established classification models achieved competitive results compared with the state-of-the-art methods in data-constrained system designs. Moreover, we identified the need for validation studies in the clinical environment.
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Affiliation(s)
| | - João Paulo do Vale Madeiro
- Department of Engineering of Teleinformatics, Federal University of Ceará, Ceará, Fortaleza 60455-900, Brazil
| | - Danielo G Gomes
- Department of Engineering of Teleinformatics, Federal University of Ceará, Ceará, Fortaleza 60455-900, Brazil
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Haweel MT, Zahran O, Abd El-Samie FE. Polynomial FLANN Classifier for Fetal Cardiotocography Monitoring. 2021 38TH NATIONAL RADIO SCIENCE CONFERENCE (NRSC) 2021. [DOI: 10.1109/nrsc52299.2021.9509832] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/01/2023]
Affiliation(s)
- Mohammad T. Haweel
- Shaqra University,Electrical Engineering Department,Dawadmi,Riyadh,Saudi Arabia
| | - O. Zahran
- Menoufia University,Faculty of Electronic Engineering,Egypt
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14
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Ricciardi C, Improta G, Amato F, Cesarelli G, Romano M. Classifying the type of delivery from cardiotocographic signals: A machine learning approach. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2020; 196:105712. [PMID: 32877811 DOI: 10.1016/j.cmpb.2020.105712] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/28/2020] [Accepted: 08/12/2020] [Indexed: 06/11/2023]
Abstract
BACKGROUND AND OBJECTIVE Cardiotocography (CTG) is the most employed methodology to monitor the foetus in the prenatal phase. Since the evaluation of CTG is often visual, and hence qualitative and too subjective, some automated methods have been introduced for its assessment. METHODS In this paper, a custom-made software is exploited to extract 17 features from the available CTG. A preliminary univariate statistical analysis is performed; then, five machine learning algorithms, exploiting ensemble learning, were implemented (J48, Random Forests (RF), Ada-boosting of decision tree (ADA-B), Gradient Boosting and Decorate) through Knime analytics platform to classify patients according to their delivery: vaginal or caesarean section. The dataset is composed by 370 signals collected between 2000 and 2009 in both public and private hospitals. The performance of the algorithms was evaluated using 10 folds cross validation with different evaluation metrics: accuracy, precision, sensitivity, specificity, area under the curve receiver operating characteristic (AUCROC). RESULTS While only two features were significantly different (gestation week and power expressed by the high frequency band of FHR power spectrum), from the statistical point of view, machine learning results were great. The RF obtained the best results: accuracy (91.1%), sensitivity (90.0%) and AUCROC (96.7%). The ADA-B achieved the highest precision (92.6%) and specificity (93.1%). As expected, the lowest scores were obtained by J48 that was the base classifier employed in all the others empowered implementations. Excluding the J48 results, the AUCROC of all the algorithms was greater than 94.9%. CONCLUSION In the light of the obtained results, that are greater than those ones found in the literature from comparable researches, it can be stated that the machine learning approach can actually help the physicians in their decision process when evaluating the foetal well-being.
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Affiliation(s)
- C Ricciardi
- Department of Advanced Biomedical Sciences, University Hospital of Naples Federico II, Naples, Italy
| | - G Improta
- Department of Public Health, University Hospital of Naples Federico II, Naples, Italy; Centro Interdipartimentale di Ricerca in Management Sanitario e Innovazione in Sanità (CIRMIS)
| | - F Amato
- Centro Interdipartimentale di Ricerca in Management Sanitario e Innovazione in Sanità (CIRMIS); Department of Electrical Engineering and Information Technology, DIETI, University of Naples Federico II, Naples 80125, Italy.
| | - G Cesarelli
- Department of Chemical, Materials and Production Engineering, University of Naples "Federico II", Naples, Italy; Istituto Italiano di Tecnologia, Naples, Italy
| | - M Romano
- Department of Experimental and Clinical Medicine (DMSC), University "Magna Graecia" of Catanzaro, Italy
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