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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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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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Naftali S, Ashkenazi YN, Ratnovsky A. A novel approach based on machine learning analysis of flow velocity waveforms to identify unseen abnormalities of the umbilical cord. Placenta 2022; 127:20-28. [DOI: 10.1016/j.placenta.2022.07.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/11/2022] [Revised: 06/13/2022] [Accepted: 07/14/2022] [Indexed: 11/24/2022]
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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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Huang L, Jiang Z, Cai R, Li L, Chen Q, Hong J, Hao Z, Wei H. Investigating the interpretability of fetal status assessment using antepartum cardiotocographic records. BMC Med Inform Decis Mak 2021; 21:355. [PMID: 34930216 PMCID: PMC8686372 DOI: 10.1186/s12911-021-01714-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2020] [Accepted: 12/06/2021] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Cardiotocography (CTG) interpretation plays a critical role in prenatal fetal monitoring. However, the interpretation of fetal status assessment using CTG is mainly confined to clinical research. To the best of our knowledge, there is no study on data analysis of CTG records to explore the causal relationships between the important CTG features and fetal status evaluation. METHODS For analyses, 2126 cardiotocograms were automatically processed and the respective diagnostic features measured by the Sisporto program. In this paper, we aim to explore the causal relationships between the important CTG features and fetal status evaluation. First, we utilized data visualization and Spearman correlation analysis to explore the relationship among CTG features and their importance on fetal status assessment. Second, we proposed a forward-stepwise-selection association rule analysis (ARA) to supplement the fetal status assessment rules based on sparse pathological cases. Third, we established structural equation models (SEMs) to investigate the latent causal factors and their causal coefficients to fetal status assessment. RESULTS Data visualization and the Spearman correlation analysis found that thirteen CTG features were relevant to the fetal state evaluation. The forward-stepwise-selection ARA further validated and complemented the CTG interpretation rules in the fetal monitoring guidelines. The measurement models validated the five latent variables, which were baseline category (BCat), variability category (VCat), acceleration category (ACat), deceleration category (DCat) and uterine contraction category (UCat) based on fetal monitoring knowledge and the above analyses. Furthermore, the interpretable models discovered the cause factors of fetal status assessment and their causal coefficients to fetal status assessment. For instance, VCat could predict BCat, and UCat could predict DCat as well. ACat, BCat and DCat directly affected fetal status assessment, where ACat was the important causal factor. CONCLUSIONS The analyses revealed the interpretation rules and discovered the causal factors and their causal coefficients for fetal status assessment. Moreover, the results are consistent with the computerized fetal monitoring and clinical knowledge. Our approaches are conducive to evidence-based medical research and realizing intelligent fetal monitoring.
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Affiliation(s)
- Liting Huang
- School of Computer, Guangdong University of Technology, Waihuan West Road, Guangzhou, China
- School of Medical Information Engineering, Guangzhou University of Chinese Medicine, Waihuandong Road, Guangzhou, China
| | - Zhiying Jiang
- School of Medical Information Engineering, Guangzhou University of Chinese Medicine, Waihuandong Road, Guangzhou, China
| | - Ruichu Cai
- School of Computer, Guangdong University of Technology, Waihuan West Road, Guangzhou, China
| | - Li Li
- The First Affiliated Hospital of Jinan University, Tianhe District People’s Hospital, Dongpu Road, Guangzhou, China
- Guangzhou Sanrui Medical Equipment Co, Gaoke Road, Guangzhou, China
| | - Qinqun Chen
- School of Medical Information Engineering, Guangzhou University of Chinese Medicine, Waihuandong Road, Guangzhou, China
| | - Jiaming Hong
- School of Medical Information Engineering, Guangzhou University of Chinese Medicine, Waihuandong Road, Guangzhou, China
| | - Zhifeng Hao
- Department of Mathematics, College of Science, Shantou University, Daxue Road, Shantou, 515063 China
| | - Hang Wei
- School of Medical Information Engineering, Guangzhou University of Chinese Medicine, Waihuandong Road, Guangzhou, China
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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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Al-yousif S, Jaenul A, Al-Dayyeni W, Alamoodi A, Najm IA, Md Tahir N, Alrawi AAA, Cömert Z, Al-shareefi NA, Saleh AH. A systematic review of automated pre-processing, feature extraction and classification of cardiotocography. PeerJ Comput Sci 2021; 7:e452. [PMID: 33987454 PMCID: PMC8093951 DOI: 10.7717/peerj-cs.452] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2020] [Accepted: 03/01/2021] [Indexed: 05/27/2023]
Abstract
CONTEXT The interpretations of cardiotocography (CTG) tracings are indeed vital to monitor fetal well-being both during pregnancy and childbirth. Currently, many studies are focusing on feature extraction and CTG classification using computer vision approach in determining the most accurate diagnosis as well as monitoring the fetal well-being during pregnancy. Additionally, a fetal monitoring system would be able to perform detection and precise quantification of fetal heart rate patterns. OBJECTIVE This study aimed to perform a systematic review to describe the achievements made by the researchers, summarizing findings that have been found by previous researchers in feature extraction and CTG classification, to determine criteria and evaluation methods to the taxonomies of the proposed literature in the CTG field and to distinguish aspects from relevant research in the field of CTG. METHODS Article search was done systematically using three databases: IEEE Xplore digital library, Science Direct, and Web of Science over a period of 5 years. The literature in the medical sciences and engineering was included in the search selection to provide a broader understanding for researchers. RESULTS After screening 372 articles, and based on our protocol of exclusion and inclusion criteria, for the final set of articles, 50 articles were obtained. The research literature taxonomy was divided into four stages. The first stage discussed the proposed method which presented steps and algorithms in the pre-processing stage, feature extraction and classification as well as their use in CTG (20/50 papers). The second stage included the development of a system specifically on automatic feature extraction and CTG classification (7/50 papers). The third stage consisted of reviews and survey articles on automatic feature extraction and CTG classification (3/50 papers). The last stage discussed evaluation and comparative studies to determine the best method for extracting and classifying features with comparisons based on a set of criteria (20/50 articles). DISCUSSION This study focused more on literature compared to techniques or methods. Also, this study conducts research and identification of various types of datasets used in surveys from publicly available, private, and commercial datasets. To analyze the results, researchers evaluated independent datasets using different techniques. CONCLUSIONS This systematic review contributes to understand and have insight into the relevant research in the field of CTG by surveying and classifying pertinent research efforts. This review will help to address the current research opportunities, problems and challenges, motivations, recommendations related to feature extraction and CTG classification, as well as the measurement of various performance and various data sets used by other researchers.
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Affiliation(s)
- Shahad Al-yousif
- Department of Medical Instrumentations Engineering Techniques, Dijlah University, Baghdad, Iraq
- Faculty of Information Science & Engineering, Management and Science University, Shah Alam, Selangoor, Malaysia
| | - Ariep Jaenul
- Department of Electrical Engineering, Faculty of Engineering and Computer Science, Jakarta Global University, Jakarta, Indonesia
| | - Wisam Al-Dayyeni
- Department of Medical Instrumentations Engineering Techniques, Dijlah University, Baghdad, Iraq
| | - Ah Alamoodi
- Department of Computing, Universiti Pendidikan Sultan Idris, Tanjong Malim, Perak, Malaysia
| | - IA Najm
- Faculty of Engineering, Tikrit University, Tikrit, Iraq
| | - Nooritawati Md Tahir
- Faculty of Electrical Engineering, Universiti Teknologi MARA (UiTM), Shah Alam, Selangor, Malaysia
| | - Ali Amer Ahmed Alrawi
- Training Directorate, Ministry of Science and Technology, Baghdad, Aljadireyah, Iraq
| | - Zafer Cömert
- Department of Software Engineering, Samsun University, Samsun, Turkey
| | - Nael A. Al-shareefi
- College of Biomedical Informatics, University of Information Technology and Communications (UOITC), Baghdad, Almansoor, Iraq
| | - Abbadullah H. Saleh
- Department Computer Engineering, Karabük University,, Karabük, Karabük, Turkey
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Davidson L, Boland MR. Towards deep phenotyping pregnancy: a systematic review on artificial intelligence and machine learning methods to improve pregnancy outcomes. Brief Bioinform 2021; 22:6065792. [PMID: 33406530 PMCID: PMC8424395 DOI: 10.1093/bib/bbaa369] [Citation(s) in RCA: 26] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2020] [Revised: 10/13/2020] [Accepted: 11/18/2020] [Indexed: 12/16/2022] Open
Abstract
Objective Development of novel informatics methods focused on improving pregnancy outcomes remains an active area of research. The purpose of this study is to systematically review the ways that artificial intelligence (AI) and machine learning (ML), including deep learning (DL), methodologies can inform patient care during pregnancy and improve outcomes. Materials and methods We searched English articles on EMBASE, PubMed and SCOPUS. Search terms included ML, AI, pregnancy and informatics. We included research articles and book chapters, excluding conference papers, editorials and notes. Results We identified 127 distinct studies from our queries that were relevant to our topic and included in the review. We found that supervised learning methods were more popular (n = 69) than unsupervised methods (n = 9). Popular methods included support vector machines (n = 30), artificial neural networks (n = 22), regression analysis (n = 17) and random forests (n = 16). Methods such as DL are beginning to gain traction (n = 13). Common areas within the pregnancy domain where AI and ML methods were used the most include prenatal care (e.g. fetal anomalies, placental functioning) (n = 73); perinatal care, birth and delivery (n = 20); and preterm birth (n = 13). Efforts to translate AI into clinical care include clinical decision support systems (n = 24) and mobile health applications (n = 9). Conclusions Overall, we found that ML and AI methods are being employed to optimize pregnancy outcomes, including modern DL methods (n = 13). Future research should focus on less-studied pregnancy domain areas, including postnatal and postpartum care (n = 2). Also, more work on clinical adoption of AI methods and the ethical implications of such adoption is needed.
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Affiliation(s)
- Lena Davidson
- MS degree at College of St. Scholastica, Duluth, MN, USA
| | - Mary Regina Boland
- Department of Biostatistics, Epidemiology, and Informatics at the University of Pennsylvania
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Zhang Z. Big data analysis with artificial intelligence technology based on machine learning algorithm. JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2020. [DOI: 10.3233/jifs-191265] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Artificial intelligence technology has been applied very well in big data analysis such as data classification. In this paper, the application of the support vector machine (SVM) method from machine learning in the problem of multi-classification was analyzed. In order to improve the classification performance, an improved one-to-one SVM multi-classification method was creatively designed by combining SVM with the K-nearest neighbor (KNN) method. Then the method was tested using UCI public data set, Statlog statistical data set and actual data. The results showed that the overall classification accuracy of the one-to-many SVM, one-to-one SVM and improved one-to-one SVM were 72.5%, 77.25% and 91.5% respectively in the classification of UCI publication data set and Statlog statistical data set, and the total classification accuracy of the neural network, decision tree, basic one-to-one SVM, directed acyclic graph improved one-to-one SVM and fuzzy decision method improved one-to-one SVM and improved one-to-one SVM proposed in this study was 83.98%, 84.55%, 74.07%, 81.5%, 82.68% and 92.9% respectively in the classification of fault data of transformer, which demonstrated the improved one-to-one SVM had good reliability. This study provides some theoretical bases for the application of methods such as machine learning in big data analysis.
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Affiliation(s)
- Zeliang Zhang
- Computer Science and Technology College, Beihua University, Jilin, China
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Hosni M, Carrillo de Gea JM, Idri A, El Bajta M, Fernández Alemán JL, García-Mateos G, Abnane I. A systematic mapping study for ensemble classification methods in cardiovascular disease. Artif Intell Rev 2020. [DOI: 10.1007/s10462-020-09914-6] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
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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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Cömert Z, Şengür A, Budak Ü, Kocamaz AF. Prediction of intrapartum fetal hypoxia considering feature selection algorithms and machine learning models. Health Inf Sci Syst 2019; 7:17. [PMID: 31435480 PMCID: PMC6702252 DOI: 10.1007/s13755-019-0079-z] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2019] [Accepted: 08/12/2019] [Indexed: 10/26/2022] Open
Abstract
INTRODUCTION Cardiotocography (CTG) consists of two biophysical signals that are fetal heart rate (FHR) and uterine contraction (UC). In this research area, the computerized systems are usually utilized to provide more objective and repeatable results. MATERIALS AND METHODS Feature selection algorithms are of great importance regarding the computerized systems to not only reduce the dimension of feature set but also to reveal the most relevant features without losing too much information. In this paper, three filters and two wrappers feature selection methods and machine learning models, which are artificial neural network (ANN), k-nearest neighbor (kNN), decision tree (DT), and support vector machine (SVM), are evaluated on a high dimensional feature set obtained from an open-access CTU-UHB intrapartum CTG database. The signals are divided into two classes as normal and hypoxic considering umbilical artery pH value (pH < 7.20) measured after delivery. A comprehensive diagnostic feature set forming the features obtained from morphological, linear, nonlinear, time-frequency and image-based time-frequency domains is generated first. Then, combinations of the feature selection algorithms and machine learning models are evaluated to achieve the most effective features as well as high classification performance. RESULTS The experimental results show that it is possible to achieve better classification performance using lower dimensional feature set that comprises of more related features, instead of the high-dimensional feature set. The most informative feature subset was generated by considering the frequency of selection of the features by feature selection algorithms. As a result, the most efficient results were produced by selected only 12 relevant features instead of a full feature set consisting of 30 diagnostic indices and SVM model. Sensitivity and specificity were achieved as 77.40% and 93.86%, respectively. CONCLUSION Consequently, the evaluation of multiple feature selection algorithms resulted in achieving the best results.
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Affiliation(s)
- Zafer Cömert
- Department of Software Engineering, Samsun University, Samsun, Turkey
| | - Abdulkadir Şengür
- Department of Electrical and Electronics Engineering, Technology Faculty, Firat University, Elazig, Turkey
| | - Ümit Budak
- Department of Electrical and Electronics Engineering, Bitlis Eren University, Bitlis, Turkey
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Preliminary Study on the Efficient Electrohysterogram Segments for Recognizing Uterine Contractions with Convolutional Neural Networks. BIOMED RESEARCH INTERNATIONAL 2019; 2019:3168541. [PMID: 31737659 PMCID: PMC6815646 DOI: 10.1155/2019/3168541] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/07/2019] [Revised: 07/28/2019] [Accepted: 09/11/2019] [Indexed: 11/17/2022]
Abstract
Background Uterine contraction (UC) is the tightening and shortening of the uterine muscles which can indicate the progress of pregnancy towards delivery. Electrohysterogram (EHG), which reflects uterine electrical activities, has recently been studied for UC monitoring. In this paper, we aimed to evaluate different EHG segments for recognizing UCs using the convolutional neural network (CNN). Materials and Methods In the open-access Icelandic 16-electrode EHG database (122 recordings from 45 pregnant women), 7136 UC and 7136 non-UC EHG segments with the duration of 60 s were manually extracted from 107 recordings of 40 pregnant women to develop a CNN model. A fivefold cross-validation was applied to evaluate the CNN based on sensitivity (SE), specificity (SP), and accuracy (ACC). Then, 1056 UC and 1056 non-UC EHG segments were extracted from the other 15 recordings of 5 pregnant women. Furthermore, the developed CNN model was applied to identify UCs using different EHG segments with the durations of 10 s, 20 s, and 30 s. Results The CNN achieved the average SE, SP, and ACC of 0.82, 0.93, and 0.88 for a 60 s EHG segment. The EHG segments of 10 s, 20 s, and 30 s around the TOCO peak achieved higher SE and ACC than the other segments with the same duration. The values of SE from 20 s EHG segments around the TOCO peak were higher than those from 10 s to 30 s EHG segments on the same side of the TOCO peak. Conclusion The proposed method could be used to determine the efficient EHG segments for recognizing UC with the CNN.
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Hao D, Peng J, Wang Y, Liu J, Zhou X, Zheng D. Evaluation of convolutional neural network for recognizing uterine contractions with electrohysterogram. Comput Biol Med 2019; 113:103394. [PMID: 31445226 PMCID: PMC6839746 DOI: 10.1016/j.compbiomed.2019.103394] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2019] [Revised: 08/16/2019] [Accepted: 08/17/2019] [Indexed: 10/29/2022]
Abstract
Uterine contraction (UC) activity is commonly used to monitor the approach of labour and delivery. Electrohysterograms (EHGs) have recently been used to monitor UC and distinguish between efficient and inefficient contractions. In this study, we aimed to identify UC in EHG signals using a convolutional neural network (CNN). An open-access database (Icelandic 16-electrode EHG database from 45 pregnant women with 122 recordings, DB1) was used to develop a CNN model, and 14000 segments with a length of 45 s (7000 from UCs and 7000 from non-UCs, which were determined with reference to the simultaneously recorded tocography signals) were manually extracted from the 122 EHG recordings. Five-fold cross-validation was applied to evaluate the ability of the CNN to identify UC based on its sensitivity (SE), specificity (SP), accuracy (ACC), and area under the receiver operating characteristic curve (AUC). The CNN model developed using DB1 was then applied to an independent clinical database (DB2) to further test its generalisation for recognizing UCs. The EHG signals in DB2 were recorded from 20 pregnant women using our multi-channel system, and 308 segments (154 from UCs and 154 from non-UCs) were extracted. The CNN model from five-fold cross-validation achieved average SE, SP, ACC, and AUC of 0.87, 0.98, 0.93, and 0.92 for DB1, and 0.88, 0.97, 0.93, and 0.87 for DB2, respectively. In summary, we demonstrated that CNN could effectively identify UCs using EHG signals and could be used as a tool for monitoring maternal and foetal health.
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Affiliation(s)
- Dongmei Hao
- College of Life Science and Bioengineering, Beijing University of Technology, Intelligent Physiological Measurement and Clinical Translation, Beijing International Platform for Scientific and Technological Cooperation, Beijing, 100024, China.
| | - Jin Peng
- College of Life Science and Bioengineering, Beijing University of Technology, Intelligent Physiological Measurement and Clinical Translation, Beijing International Platform for Scientific and Technological Cooperation, Beijing, 100024, China; Medical Device and Technology Research Group, Faculty of Health, Education, Medicine and Social Care, Anglia Ruskin University, Chelmsford, CM1 1SQ, UK
| | - Ying Wang
- College of Life Science and Bioengineering, Beijing University of Technology, Intelligent Physiological Measurement and Clinical Translation, Beijing International Platform for Scientific and Technological Cooperation, Beijing, 100024, China
| | - Juntao Liu
- Department of Obstetrics, Peking Union Medical College Hospital, Beijing, 100730, China
| | - Xiya Zhou
- Department of Obstetrics, Peking Union Medical College Hospital, Beijing, 100730, China
| | - Dingchang Zheng
- Medical Device and Technology Research Group, Faculty of Health, Education, Medicine and Social Care, Anglia Ruskin University, Chelmsford, CM1 1SQ, UK.
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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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Real-time survival prediction in emergency situations with unbalanced cardiac patient data. HEALTH AND TECHNOLOGY 2019. [DOI: 10.1007/s12553-019-00307-6] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/05/2023]
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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: 17] [Impact Index Per Article: 3.4] [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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20
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Akbulut A, Ertugrul E, Topcu V. Fetal health status prediction based on maternal clinical history using machine learning techniques. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2018; 163:87-100. [PMID: 30119860 DOI: 10.1016/j.cmpb.2018.06.010] [Citation(s) in RCA: 25] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/18/2018] [Revised: 05/19/2018] [Accepted: 06/08/2018] [Indexed: 05/14/2023]
Abstract
BACKGROUND AND OBJECTIVE Congenital anomalies are seen at 1-3% of the population, probabilities of which are tried to be found out primarily through double, triple and quad tests during pregnancy. Also, ultrasonographical evaluations of fetuses enhance detecting and defining these abnormalities. About 60-70% of the anomalies can be diagnosed via ultrasonography, while the remaining 30-40% can be diagnosed after childbirth. Medical diagnosis and prediction is a topic that is closely related with e-Health and machine learning. e-Health applications are critically important especially for the patients unable to see a doctor or any health professional. Our objective is to help clinicians and families to better predict fetal congenital anomalies besides the traditional pregnancy tests using machine learning techniques and e-Health applications. METHODS In this work, we developed a prediction system with assistive e-Health applications which both the pregnant women and practitioners can make use of. A performance comparison (considering Accuracy, F1-Score, AUC measures) was made between 9 binary classification models (Averaged Perceptron, Boosted Decision Tree, Bayes Point Machine, Decision Forest, Decision Jungle, Locally-Deep Support Vector Machine, Logistic Regression, Neural Network, Support Vector Machine) which were trained with the clinical dataset of 96 pregnant women and used to process data to predict fetal anomaly status based on the maternal and clinical data. The dataset was obtained through maternal questionnaire and detailed evaluations of 3 clinicians from RadyoEmar radiodiagnostics center in Istanbul, Turkey. Our e-Health applications are used to get pregnant women's health status and clinical history parameters as inputs, recommend them physical activities to perform during pregnancy, and inform the practitioners and finally the patients about possible risks of fetal anomalies as the output. RESULTS In this paper, the highest accuracy of prediction was displayed as 89.5% during the development tests with Decision Forest model. In real life testing with 16 users, the performance was 87.5%. This estimate is sufficient to give an idea of fetal health before the patient visits the physician. CONCLUSIONS The proposed work aims to provide assistive services to pregnant women and clinicians via an online system consisting of a mobile side for the patients, a web application side for their clinicians and a prediction system. In addition, we showed the impact of certain clinical data parameters of pregnant on the fetal health status, statistically correlated the parameters with the existence of fetal anomalies and showed guidelines for future researches.
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Affiliation(s)
- Akhan Akbulut
- Department of Computer Science, North Carolina State University, Raleigh, NC 27606, USA; Department of Computer Engineering, Istanbul Kultur University, Atakoy Campus Bakirkoy, Istanbul 34156, Turkey.
| | - Egemen Ertugrul
- Department of Computer Engineering, Istanbul Kultur University, Atakoy Campus Bakirkoy, Istanbul 34156, Turkey.
| | - Varol Topcu
- Department of Computer Engineering, Istanbul Kultur University, Atakoy Campus Bakirkoy, Istanbul 34156, Turkey.
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21
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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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22
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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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23
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Shukla AK, Singh P, Vardhan M. A hybrid gene selection method for microarray recognition. Biocybern Biomed Eng 2018. [DOI: 10.1016/j.bbe.2018.08.004] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
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24
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CÖMERT Z, KOCAMAZ A. A Study of Artificial Neural Network Training Algorithms for Classification of Cardiotocography Signals. ACTA ACUST UNITED AC 2017. [DOI: 10.17678/beuscitech.338085] [Citation(s) in RCA: 41] [Impact Index Per Article: 5.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
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25
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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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26
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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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27
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Hassan AR, Haque MA. An expert system for automated identification of obstructive sleep apnea from single-lead ECG using random under sampling boosting. Neurocomputing 2017. [DOI: 10.1016/j.neucom.2016.12.062] [Citation(s) in RCA: 87] [Impact Index Per Article: 12.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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28
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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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29
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Ebrahimi M, Abdolshah M, Abdolshah S. Developing a computer vision method based on AHP and feature ranking for ores type detection. Appl Soft Comput 2016. [DOI: 10.1016/j.asoc.2016.08.027] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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30
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Gorunescu F, Belciug S. Boosting backpropagation algorithm by stimulus-sampling: Application in computer-aided medical diagnosis. J Biomed Inform 2016; 63:74-81. [PMID: 27498068 DOI: 10.1016/j.jbi.2016.08.004] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2016] [Revised: 07/25/2016] [Accepted: 08/03/2016] [Indexed: 12/11/2022]
Abstract
Neural networks (NNs), in general, and multi-layer perceptron (MLP), in particular, represent one of the most efficient classifiers among the machine learning (ML) algorithms. Inspired by the stimulus-sampling paradigm, it is plausible to assume that the association of stimuli with the neurons in the output layer of a MLP can increase its performance. The stimulus-sampling process is assumed memoryless (Markovian), in the sense that the choice of a particular stimulus at a certain step, conditioned by the whole prior evolution of the learning process, depends only on the network's answer at the previous step. This paper proposes a novel learning technique, by enhancing the standard backpropagation algorithm performance with the aid of a stimulus-sampling procedure applied to the output neurons. The network uses the observable behavior that varies throughout the training process by stimulating the correct answers through corresponding rewards/penalties assigned to the output neurons. The proposed model has been applied in computer-aided medical diagnosis using five real-life breast cancer, colon cancer, diabetes, thyroid, and fetal heartbeat databases. The statistical comparison to well-established ML algorithms proved beyond doubt its efficiency and robustness.
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Affiliation(s)
- Florin Gorunescu
- Department of Biostatistics and Informatics, University of Medicine and Pharmacy of Craiova, Craiova 200349, Romania.
| | - Smaranda Belciug
- Department of Computer Science, University of Craiova, Craiova 200585, Romania.
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31
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Fujita H, Acharya UR, Sudarshan VK, Ghista DN, Sree SV, Eugene LWJ, Koh JE. Sudden cardiac death (SCD) prediction based on nonlinear heart rate variability features and SCD index. Appl Soft Comput 2016. [DOI: 10.1016/j.asoc.2016.02.049] [Citation(s) in RCA: 71] [Impact Index Per Article: 8.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
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32
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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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